DESC:FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
SYSTEM AND METHOD FOR DETERMINING A CURRENT LOCATION OF AN OBJECT

Applicant:
VARROC ENGINEERING LIMITED
An Indian Entity having address as:
L-4, MIDC Waluj, Aurangabad-431136, Maharashtra, India

The following specification particularly describes the invention and the manner in which it is to be performed.
CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
The present application claims priority from the Indian provisional patent application, having application number 202421005004, filed on 24th January 2024, incorporated herein by a reference.
FIELD OF INVENTION
The presently disclosed embodiments are related, in general, to navigation operation. More particularly, the presently disclosed embodiments are related to a system and a method to determine a current location of an object.
BACKGROUND OF THE INVENTION
This section is intended to introduce the reader to various aspects of art, which may be related to various aspects of the present disclosure that are described or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements in this background section are to be read in this light, and not as admissions of prior art. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
Global Positioning Systems (GPS) are being used in increasing numbers in automobiles to provide vehicle navigation functions. A GPS receiver or navigation device includes a digital road map of the area of travel. A user, such as driver of the vehicle, inputs a destination into GPS navigation device and the GPS navigation device calculates a route from the current position of the vehicle, through the network of roads as represented in the Geographic Information (GIS) database, to the destination. If the GPS signals are obstructed or interfered with, the current position of the vehicle may contain some errors and noise, for example, in urban areas with plurality of tall buildings, on roads with canopy of trees and in areas with high levels of background noise or interference. Under these conditions, GPS/GNSS (Global Navigation Satellite System) components which are very costly are not able to provide a complete solution.
To provide navigation updates in the absence of GPS signal reception, a variety of dead reckoning enhancements have been offered. Navigation using dead reckoning involves calculating a current position based upon the heading and distance from a previously known position. Sailors often use estimates of their speed and heading over a period of time to determine the relative change in position with regard to a previously known position and thus deduce their current location. In more modern applications such as automotive navigation systems, a dead reckoning system may get its necessary velocity measurements through a coupling to the vehicle’s odometer. However, such coupling involves considerable expense. Dead Reckoning systems have been developed that use accelerometers to provide velocity estimates from the integration of the acceleration. A problem remains, however, because the accelerometers must be precisely arranged with respect to the vehicle to provide an accurate velocity estimate, thereby requiring an expensive and time-consuming installation.
A dead reckoning system is a traditional method for navigation updates in the absence of GPS signal reception, and calculates a current position based on heading and distance from a previously known position. Further, the dead reckoning system may typically consist of several components working together to estimate the current position of a moving object based on its previous position, heading angle and speed. These components may include accelerometers, gyroscopes, magnetometers, and the like. Moreover, accelerometers may be used to provide an estimate of the velocity of the vehicle. Alternatively, using accelerometers to estimate velocity through acceleration integration presents significant challenge, as it demands precise accelerometer placement, leading to both high costs and time-consuming installations.
In light of the above stated discussion, there exists a need for an improved system and method for navigation operation in GPS-deprived environments to overcome at least one of the above stated disadvantages.
SUMMARY OF THE INVENTION
Before the present system and device and its components are summarized, it is to be understood that this disclosure is not limited to the system and its arrangement as described, as there can be multiple possible embodiments which are not expressly illustrated in the present disclosure. The present disclosure overcomes one or more shortcomings of the prior art and provides additional advantages discussed throughout the present disclosure. Additional features and advantages are realized through the techniques of the present disclosure. It is also to be understood that the terminology used in the description is for the purpose of describing the versions or embodiments only and is not intended to limit the scope of the present application. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in detecting or limiting the scope of the claimed subject matter.
According to example embodiments illustrated herein, a method for determining a current location of an object is disclosed. In one example, the method may be implemented on an electronic device. The method comprises receiving, via a transceiver, an initial location and data from a plurality of sensors. In an embodiment, the plurality of sensors being disposed on the object. Further, the method comprises calculating, via a current position computation unit, a current position of the object based on the initial location and the data received from the plurality of sensors. Further, the method comprises computing, via an update position computation unit, an updated position of the object by initiating a search to explore one or more estimated positions in a random space based on the current position by employing a Lévy flight technique. In an embodiment, for each of the one or more estimated positions, a proximity value is computed based on a step length. Furthermore, the method comprises providing, via the update position computation unit, in real-time the updated position of the object based on the proximity value. In an embodiment, the updated position corresponds to an optimized current location of the object.
According to another example embodiments illustrated herein, a system for determining the current location of the object is disclosed. In one example, the system may be implemented on a control unit. In an example embodiment, the control unit comprises a processor and a memory communicatively coupled to the processor. In an example embodiment, the memory stores processor instructions, which, on execution, causes the processor to receive, via the transceiver, the initial location and data from the plurality of sensors. In an example embodiment, the plurality of sensors being disposed on the object. Furthermore, the processor is configured to calculate, via the current position computation unit, the current position of the object based on the initial location and the data received from the plurality of sensors. Further, the processor is configured to compute, via the update position computation unit, the updated position of the object by initiating the search to explore the one or more estimated positions in the random space based on the current position by employing the Lévy flight technique. In an embodiment, for each of the one or more estimated positions the proximity value is computed based on the step length. Further, the processor is configured to provide, via the update position computation unit, in real-time the updated position of the object based on the proximity value. In an embodiment, the updated position corresponds to the optimized current location of the object.
According to yet another example embodiments illustrated herein, an electronic device disposed in a vehicle is disclosed, for determining the current location of the object. In an example embodiment, the electronic device comprising the processor and the memory communicatively coupled to the processor. In an example embodiment, the memory stores processor instructions, which, on execution, causes the processor to receive, via the transceiver, the initial location and data from the plurality of sensors. In an example embodiment, the plurality of sensors being disposed on the object. Furthermore, the processor is configured to calculate, via the current position computation unit, the current position of the object based on the initial location and the data received from the plurality of sensors. Further, the processor is configured to compute, via the update position computation unit, the updated position of the object by initiating the search to explore the one or more estimated positions in the random space based on the current position by employing the Lévy flight technique. In an embodiment, for each of the one or more estimated positions the proximity value is computed based on the step length. Furthermore, the processor is configured to provide, via the update position computation unit, in real-time the updated position of the object based on the proximity value. In an embodiment, the updated position corresponds to the optimized current location of the object.
According to yet another example embodiments illustrated herein, there may be provided a non-transitory computer-readable storage medium having stored thereon, a set of computer-executable instructions for causing the electronic device comprising one or more processors to perform steps comprising receiving, via the transceiver, the initial location and data from the plurality of sensors. In an example embodiment, the plurality of sensors being disposed on the object. Further, the non-transitory computer-readable storage medium comprises a step for calculating, via the current position computation unit, the current position of the object based on the initial location and the data received from the plurality of sensors. Further, the non-transitory computer-readable storage medium comprises a step for computing, via the update position computation unit, the updated position of the object by initiating the search to explore the one or more estimated positions in the random space based on the current position by employing the Lévy flight technique. In an example embodiment, for each of the one or more estimated positions the proximity value is computed based on the step length. Further, the non-transitory computer-readable storage medium comprises a step for providing, via the update position computation unit, in real-time the updated position of the object based on the proximity value. In an example embodiment, the updated position corresponds to the optimized current location of the object.
The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, examples, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.
BRIEF DESCRIPTION OF DRAWINGS
The detailed description is described with reference to the accompanying figures. In the figures, same numbers are used throughout the drawings to refer like features and components. Embodiments of the present invention will now be described, with reference to the following diagrams below wherein:
Figure 1 illustrates a block diagram describing a system (100) for determining a current location of an object, in accordance with at least one embodiment;
Figure 2 illustrates a block diagram describing an application server (103) communicatively coupled with an electronic device (101) comprising a control unit (208) configured to determine the current location of the object, in accordance with at least one embodiment;
Figure 3 illustrates a flowchart (300) describing an exemplary embodiment of various method steps implemented for determining the current location of the object, in accordance with at least one embodiment;
Figure 4 illustrates a flowchart describing the method (400) to determine the current location of the object, in accordance with at least one embodiment; and
Figures 5A-5C illustrates an exemplary embodiment (500) describing the system (100) and the method (400) for navigation operation using a Lévy Flight enhanced dead reckoning system for improving position estimation accuracy in GPS-deprived environments.
It should be noted that the accompanying figures are intended to present illustrations of exemplary embodiments of the present disclosure. These figures are not intended to limit the scope of the present disclosure. It should also be noted that accompanying figures are not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE INVENTION
Reference throughout the specification to “various embodiments,” “some embodiments,” “one embodiment,” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases “in various embodiments,” “in some embodiments,” “in one embodiment,” or “in an embodiment” in places throughout the specification are not necessarily all referring to the same embodiment. Furthermore, the features, structures or characteristics may be combined in any suitable manner in one or more embodiments.
The words "comprising," "having," "containing," and "including," and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items. It must also be noted that, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the exemplary methods are described. The disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various forms.
Referring to Figure 1 is a block diagram that illustrates a system (100) for determining a current location of an object, in accordance with at least one example embodiment. The system (100) may include an electronic device (101), a communication network (102), and an application server (103). The electronic device (101) and the application server (103) may be communicatively coupled with each other via the communication network (102). In an embodiment, the application server (103) may communicate with the electronic device (101) using one or more protocols such as, but not limited to, Open Database Connectivity (ODBC) protocol and Java Database Connectivity (JDBC) protocol.
In example implementation, the electronic device (101) may refer to a computing device used by a user. The electronic device (101) may include of one or more processors and one or more memories. The one or more memories may include computer readable code that may be executable by the one or more processors to perform predetermined operations. In an example embodiment, the electronic device (101) may present a user interface to transmit the data to the application server (103). Example web user interfaces presented on the electronic device (101) to determine the current location of the object have been explained in conjunction with Figure 2. In an example embodiment, the electronic device may include at least one of a navigation device, a telematics unit, and an instrument cluster. Examples of the electronic device (101) may include, but are not limited to, a personal computer, a laptop, a personal digital assistant (PDA), a mobile device, a tablet, or any other computing device.
In an example embodiment, the communication network (102) may correspond to a communication medium through which the application server (103), and the electronic device (101) may communicate with each other. Such a communication may be performed, in accordance with various wired and wireless communication protocols. Examples of such wired and wireless communication protocols include, but are not limited to, Transmission Control Protocol and Internet Protocol (TCP/IP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), ZigBee, EDGE, infrared IR), IEEE 802.11, 802.16, 2G, 3G, 4G, 5G, 6G cellular communication protocols, and/or Bluetooth (BT) communication protocols. The communication network (102) may include, but is not limited to, the Internet, a cloud network, a Wireless Fidelity (Wi-Fi) network, a Wireless Local Area Network (WLAN), a Local Area Network (LAN), a telephone line (POTS), and/or a Metropolitan Area Network (MAN).
In an embodiment, the application server (103) may refer to a computing device or a software framework hosting an application or a software service. In an example embodiment, the application server (103) may be implemented to execute procedures such as, but not limited to, programs, routines, or scripts stored in one or more memories for supporting the hosted application or the software service. In another example embodiment, the hosted application or the software service may be configured to perform one or more predetermined operations. The application server (103) may be realized through various types of application servers such as, but are not limited to, a Java application server, a .NET framework application server, a Base4 application server, a PHP framework application server, or any other application server framework.
In an example embodiment, the application server (103) includes a transceiver (204) which may be configured to receive an initial location and data from a plurality of sensors. In an example embodiment, the initial location received by the transceiver (204) may correspond to last known GPS coordinates of the object In another embodiment, the plurality of sensors being disposed on the object.
The application server (103) includes a current position computation unit (205). Further, the current position computation unit (205) may be configured to calculate a current position of the object based on the received data and the initial location. Further, the current position computation unit (205) may be further configured to calculate the current position of the object based on an initial location, a current heading angle, and a speed of the object. Further, the current position computation unit (205) may be configured to calculate a displacement of the object based on the speed of the object, estimated velocity, and the current heading angle. Further, the displacement of the object may be calculated by multiplying the speed of the object with one of a Sin component of the current heading angle or Cos component of the current heading angle. In one example embodiment, the Cos component is used for determining the displacement if the object’s current heading angle is 0^0, and in another example embodiment, the Sin component is used for determining the displacement if the object’s current heading angle is 90^0. Further, the current position computation unit (205) may be configured to integrate the initial location of the object with the displacement of the object to obtain the current position of the object. In an example embodiment, the current position of the object is provided as input to the Lévy flight technique. Further, the current position computation unit (205) may be configured to compute the proximity value of each of the one or more estimated positions based on equation 1. Furthermore, the current position computation unit (205) may be configured to calculate a step length based on equation 2. (Described in detail in Figure 3).
The application server (103) includes an update position computation unit (206). Further, the update position computation unit (206) may be configured to compute an updated position of the object by initiating a search to explore one or more estimated positions in a random space based on the current position by employing a Lévy flight technique. In an example embodiment, for each of the one or more estimated positions a proximity value is computed based on the step length. Further, the update position computation unit (206) may be configured to update the proximity value of each of the one or more estimated positions by updating a position vector for an individual best position and updating the position vector for a global best position. Furthermore, the update position computation unit (206) may be configured to update a fitness value of the position vector for the individual best position and the global best position. Further, the update position computation unit (206) may be configured to determine a probable updated position, based on equation 3. (Described in detail in Figure 3). Further, the update position computation unit (206) may be configured to provide in real-time the updated position of the object based on the proximity value. In an example embodiment, the updated position corresponds to an optimized current location of the object.
Now referring to Figure 2 is a block diagram describing the application server (103) communicatively coupled with the electronic device (101) includes a control unit (208) configured to determine the current location of the object, in accordance with at least one example embodiment. In one example implementation, the control unit (208) may include the transceiver (204), the current position computation unit (205), and the update position computation unit (206). Further, figure 2 is explained in conjunction with elements from figure 1. In an example embodiment, the application server (103) includes a processor (201), an input/output interface (I/O interface) (202), a memory (203), the transceiver (204), the current position computation unit (205), the updated position computation unit (206), and a data (207). The processor (201) may be communicatively coupled to the memory (203), the transceiver (204), the current position computation unit (205), the updated position computation unit (206), and the data (207). The transceiver (204) may be communicatively coupled to the communication network (102).
The processor (201) may include suitable logic, circuitry, interfaces, and/or code that may be configured to execute a set of instructions stored in the memory (203). The processor (201) may be implemented based on a number of processor technologies known in the art. The processor (201) may work in coordination with the transceiver (204), the current position computation unit (205), the updated position computation unit (206), and the data (207) to determine the current location of the object. Examples of the processor (201) include, but not limited to, an X86-based processor, a Reduced Instruction Set Computing (RISC) processor, an Application-Specific Integrated Circuit (ASIC) processor, a Complex Instruction Set Computing (CISC) processor, and/or any suitable processor.
The input/output interface (202) may include suitable logic, circuitry, interfaces, and/or code that may be configured to receive an input or transmit an output to the electronic device (101). The I/O interface (202) may include various input and output devices that are configured to communicate with the processor (201). Examples of the Input devices include, but are not limited to, a keyboard, a mouse, a joystick, a touch screen, a microphone, a camera, and/or a docking station. Examples of the output devices include, but are not limited to, a display screen and/or a speaker.
The memory (203) may include suitable logic, circuitry, interfaces, and/or code that may be configured to store the set of instructions, which are executed by the processor (201). In an example embodiment, the memory (203) may be configured to store one or more programs, routines, or scripts that may be executed in coordination with the processor (201). The memory (203) may be implemented based on a Random Access Memory (RAM), a Read-Only Memory (ROM), a Hard Disk Drive (HDD), a storage server, and/or a Secure Digital (SD) card.
The transceiver (204) may include suitable logic, circuitry, interfaces, and/or code that may be configured to receive the data (207) from the electronic device (101). The transceiver (204) may further be configured to transmit information pertaining to received data (207) to the electronic device (101), via the communication network (102). The transceiver (204) may implement one or more known technologies to support wired or wireless communication with the communication network (102). In an example embodiment, the transceiver (204) may include, but is not limited to, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a Universal Serial Bus (USB) device, a coder-decoder (CODEC) chipset, a subscriber identity module (SIM) card, and/or a local buffer. The transceiver (204) may communicate via wireless communication with networks, such as the Internet, an Intranet and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN) and/or a metropolitan area network (MAN). The wireless communication may use any of a plurality of communication standards, protocols and technologies, such as: Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), wideband code division multiple access (W-CDMA), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Wireless Fidelity (Wi-Fi) (e,g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g and/or IEEE 802.11n), voice over Internet Protocol (VoIP), Wi-MAX, a protocol for email, instant messaging, and/or Short Message Service (SMS).
The current position computation unit (205) may include suitable logic, circuitry, interfaces, and/or code that may be configured to calculate the current position of the object based on the received data and the initial location. The current position computation unit (205) may be configured to calculate the current position of the object based on an initial location, a current heading angle, and a speed of the object.
The updated position computation unit (206) may include suitable logic, circuitry, interfaces, and/or code that may be configured to compute the updated position of the object by initiating the search to explore one or more estimated positions in the random space based on the current position by employing the Lévy flight technique. In an example embodiment, for each of the one or more estimated positions the proximity value is computed based on the step length.
The data (207) may include a sensor data and a position data. In an example embodiment, the sensor data may include the data (207) received from the plurality of sensors. Further, the plurality of sensors may include at least one of an accelerometer, a gyroscope, a magnetometer, or combination thereof. In an example embodiment, the accelerometer is configured to measure a linear acceleration of the object across a plurality of axis. Furthermore, in an example embodiment, the gyroscope data may include an angular velocity of the object, and the magnetometer data may include a strength of Earth’s magnetic field and a directional change of the Earth’s magnetic field. Furthermore, the position data may include the data (207) of current position of the object based on an initial location, a current heading angle, and a speed of the object, obtained with help of inputs from plurality of sensors.
In an example embodiment, the accelerometer is at least one of a dual-axis accelerometer, a triple-axis accelerometer or combination of the same. Further, in an example embodiment, the gyroscope is oriented substantially perpendicular with respect to a longitudinal axis of the object. Furthermore, the accelerometer may be configured to measure a linear acceleration of the object across a plurality of axis.
Referring to Figure 3 illustrates a flowchart (300) describing an exemplary embodiment of various method steps implemented for determining the current location of the object. In an example implementation, the method starts by initializing the previous known position, previous position, and heading of the object. An initial position within a random space is assigned, and the initial heading angle in degrees and speed are set. An initial simulation loop with a dimension of 100 (dim=100) is established. Input from the plurality of sensors on the object is received to provide real-time data. The current heading angle is calculated based on this sensor data, followed by the calculation of displacement using the formula Disp = speed*[cosd (current heading), sind (current heading)]. The current position is then updated by adding this displacement to the previous position. Additional sensor input is received to continuously refine the object's positional data. A random space is initialized, and the simulation iteration is set to 0, with the number of steps initialized to 1 and the number of dimensions set to 2. Input for the Lévy flight technique is provided, followed by the calculation of step length. The fitness value of the current position is calculated to assess its optimality. If the maximum number of iterations is reached, the algorithm terminates; otherwise, it continues to iterate. The fitness of updated search agents is evaluated, and if a better solution is found, the current position is updated accordingly. The method concludes by providing the optimized current location of the object based on the updated fitness values and positional calculations.
In an example implementation, the method (400) for determining the current location of the object is disclosed herein. The transceiver (204) may be configured to receive, the initial location and the data (207) from the plurality of sensors. In another example embodiment, the plurality of sensors being disposed on the object. For example, the initial location is the last known GPS coordinates of a delivery drone, and the sensor data includes accelerometer readings that measure the linear acceleration, gyroscope readings that provide the angular velocity, and magnetometer readings that capture the Earth's magnetic field strength. In an example embodiment, the object is in at least one of a stationary state or moving state. For example, the delivery drone could be hovering in place to make a delivery (stationary state) or flying towards its next destination (moving state).
In an exemplary embodiment, the object may be autonomous vehicles, delivery drones, surveillance drones, marine vessels, wearable devices including smart watches, fitness trackers, and more.
Furthermore, the current position computation unit (205) may be configured to calculate the current position of the object based on the received data and the initial location. Furthermore, the current position computation unit (205) may be configured to calculate the current position of the object based on the initial location, the current heading angle, and the speed of the object.
In an example embodiment, calculating the current position of the object may include determining an estimated velocity of the object based on accelerometer data. Furthermore, calculating the current position of the object may include determining the current heading angle of the object using gyroscope data and magnetometer data. In an example embodiment, the gyroscope data may include an angular velocity of the object, and the magnetometer data may include a strength of Earth’s magnetic field and a directional change of the Earth’s magnetic field.
For example, if the object is a car navigating through a city, the current position computation unit (205) initially receives the car's last known GPS coordinates (initial location) and data from the plurality of sensors such as the accelerometer, gyroscope, and magnetometer. Based on this initial location, the current position computation unit (205) calculates the car's current position by integrating the sensor data, which includes the car's speed and heading angle. The speed is derived from the accelerometer data, and the heading angle is determined using the gyroscope and magnetometer data. As the car moves, these continuous updates enable the update position computation unit (206) to provide an accurate real-time position, ensuring the car's navigation system can give precise turn-by-turn directions even in areas with poor GPS signal reception, such as tunnels or urban canyons.
Further, calculating the current position of the object may include calculating the displacement of the object based on the speed of the object, estimated velocity, and the current heading angle. Thus, combining this data (207) from the plurality of sensors, the car calculates its displacement i.e. how far it has travelled in a given direction.
Furthermore, calculating the current position of the object may include integrating the initial location of the object with the displacement of the object to obtain the current position of the object. In an example embodiment, the current position of the object is provided as input to the Lévy flight technique.
For example, if the car is moving at a speed of 20 km/h with an estimated velocity of 19 km/h and a heading angle of 45 degrees, the displacement can be calculated by considering the directional components of its movement. This calculated displacement is then integrated with the car's last known position, allowing the autonomous system to accurately update the car's current position, even in the absence of GPS signals. This precise calculation ensures the car can navigate safely and effectively, providing accurate real-time location updates essential for tasks like turn-by-turn navigation and obstacle avoidance.
In an example embodiment, calculating, via the current position computation unit (205), the displacement of the object by multiplying the speed of the object with one of the Sin component of the current heading angle or Cos component of the current heading angle. In an example embodiment, the Cos component is used for determining the displacement if the object’s current heading angle is 0^0, and in an example embodiment, the Sin component is used for determining the displacement if the object’s current heading angle is 90^0.
In an example embodiment, the update position computation unit (206) may be configured to compute the updated position of the object by initiating the search to explore the one or more estimated positions in the random space based on the current position by employing the Lévy flight technique. In an example embodiment, for each of the one or more estimated positions, the proximity value is computed based on the step length.
In an example embodiment, the updated position of the object being provided in absence of Global Navigation Satellite System (GNSS). In one example, the object corresponds to a vehicle. In another example embodiment, the updated position provided in real-time is used to provide turn by turn navigation instructions for navigating the vehicle.
For example, a fleet of autonomous delivery vehicles operates in a dense urban environment with tall buildings obstructing GNSS signals. Each vehicle, equipped with sensors and computational units, continuously calculates its position using a Lévy flight technique when GNSS signals are unavailable. This technique enables the vehicles to explore multiple estimated positions in a random space based on their current positions. For each estimated position, the vehicle computes a proximity value using the step length, determining how close it is to the actual target, thus guiding the vehicles to select the most optimal route in real-time. These updated positions are crucial for providing turn-by-turn navigation instructions, ensuring the smooth navigation of the vehicles through city streets without relying on traditional satellite-based systems.
In an example embodiment, the Lévy flight technique is a Stochastic Metaheuristic technique, and the Lévy flight technique includes steps of initiating an initial iteration for exploring and exploiting the one or more estimated positions in the random space using a plurality of agents based on the current position and a recorded number of inputs from the plurality of sensors.
In an example embodiment, the current position computation unit (205) may be configured to compute the proximity value of each of the one or more estimated positions based on equation 1. In an example embodiment, each of the one or more estimated positions being represented as vectors.
L(s)~|s|^(-1-ß) ……. Equation 1
where s represents the step length, and ß is an index which lies in a range [0, 2].
In an example embodiment, the current position computation unit (205) may be configured to calculate, the step length based on equation 2:
s=u/|v|^(1/ß) ……. Equation 2
where u and v are normally distributed parameters and are specified as:
u~N(0,s_u^2 )
v~N(0,s_v^2 )
where,
s_u={(?(1+ß) sin?(pß/2))/(ß?[(1+ß)/2]*2^(((ß-1))/2) )}^(1/ß)
s_v=1
where ?() represents Gamma function and is defined as follows:
?(1+ß)=?_0^8¦?t^ß ?exp?^(-t) dt? of ß is an integer, then the Gamma function is defined as follows:
?(1+ß)=ß!
Further, the update position computation unit (206) may be configured to update the proximity value of each of the one or more estimated positions by updating a position vector for an individual best position and updating the position vector for a global best position. Moreover, the update position computation unit (206) may be configured to update a fitness value of the position vector for the individual best position and the global best position.
In an example embodiment, the individual best position is determined by evaluating the fitness of the current position, as input from the plurality of sensors, and comparing the same to the fitness of the previously recorded positions. For example, in a first iteration, the best position is compared with the initial position. This refers to the best solution found by a specific agent from the plurality of agents up to a current iteration. Further, by comparing the individual best positions of the plurality of agents and selecting the best among them, the update position computation unit (206) identifies the best solution found by an entire swarm of agents up to the current iteration. For example, if the delivery drone encounters an obstacle while navigating a city, the update position computation unit would adjust the position vectors, accordingly, ensuring the delivery drone finds the best path to its destination while considering both its own position and that of other drones in the vicinity. Additionally, the delivery drone refines the fitness values associated with these positions based on factors like distance travelled and obstacles avoided.
In an example embodiment, the Lévy flight technique is a Stochastic Metaheuristic technique, and the Lévy flight technique includes steps of identifying a number of short steps with occasional large jumps to be taken by the plurality of agents for exploring and exploiting the random space.
In an example embodiment, the Lévy flight technique is a Stochastic Metaheuristic technique, and the Lévy flight technique includes steps of incrementing a count of the initial iteration by one to balance exploration and exploitation.
Further, in an example embodiment, the plurality of agents may be configured to take random long jumps for exploring and exploiting the random space. In another example embodiment, the random long jumps allow to explore a larger portion of the random space. In yet another example embodiment, the short steps refine the search around one or more promising regions within the random space.
Furthermore, the update position computation unit (206) may be configured to determine the probable updated position, based on equation 3:
probable updated position = s.* estimated position … Equation 3
In an example embodiment, if the probable updated position is precise than the estimated position based on the proximity value, then the probable updated position is identified as the updated position of the object. In an example embodiment, if the probable updated position is imprecise than the estimated position based on the proximity value, then re-initiating an iteration for exploring the one or more estimated positions. Furthermore, the update position computation unit (206) may be configured to provide in real-time the updated position of the object based on the proximity value. In an example embodiment, the updated position corresponds to an optimized current location of the object.
In one example scenario, the delivery drone navigating an urban environment where GNSS signals are frequently obstructed by tall buildings, leading to intermittent GPS signal loss. The drone, equipped with a transceiver, dual-axis accelerometer, gyroscope, and magnetometer, initially receives its location from the last known GPS coordinates. As the drone flies, the drone continuously collects sensor data to calculate its current position, including velocity and heading angle, which is updated based on the initial location. To enhance position accuracy when GNSS is unavailable, the drone employs the Lévy flight technique, exploring and exploiting estimated positions in the random space and calculating proximity values for each step length. The drone's position vectors for both the individual best position (the best position it has found) and the global best position (the best position found within a swarm of drones) are updated, along with their fitness values, ensuring optimal navigation. This allows the drone to provide real-time turn-by-turn navigation instructions, maintaining accurate trajectory and avoiding obstacles. The method enables the drone to continuously adjust the current position using the Lévy flight technique, ensuring it adopts the most precise position and re-initiates the search if necessary, thereby ensuring reliable and efficient deliveries even without consistent GNSS signals.
Now referring to Figure 4, illustrates a flowchart describing a method (400) to determine the current location of the object, in accordance with at least one example embodiment. Further, the method (400) starts at step 401 and proceeds sequentially until step 404.
At step 401, the transceiver (204), may be configured to receive an initial location and data from the plurality of sensors. In an example embodiment, the plurality of sensors being disposed on the object.
At step 402, the current position computation unit (205) may be configured to calculate, the current position of the object based on the received data and the initial location.
At step 403, the update position computation unit (206) may be configured to compute the updated position of the object by initiating the search to explore the one or more estimated positions in the random space based on the current position by employing a Lévy flight technique. In an example embodiment, for each of the one or more estimated positions a proximity value is computed based on a step length.
At step 404, the update position computation unit (206) may be configured to provide in real-time the updated position of the object based on the proximity value. In an example embodiment, the updated position corresponds to an optimized current location of the object.
In an exemplary embodiment, the disclosed system (100) and the method (400) may be utilized for providing updated location information even in the absence of GPS signals. Furthermore, the system (100) and the method (400) may provide real-time updates about the current location of the device.
Further, while example embodiments of the present invention may generally refer to the technical solution environment and the schematics described herein, it is understood that the techniques described may be extended to other implementation contexts.
In another example scenario, the disclosed method may be utilized in emergency SOS call systems to accurately determine the caller's location, even in GPS-denied environments. By integrating the enhanced dead reckoning system with the Lévy flight technique into emergency response systems, first responders may quickly locate individuals in need of assistance, reducing response times and potentially saving lives.
In yet another example scenario, the disclosed method may be utilized in daily commute applications, such as ridesharing or delivery services, the disclosed method may ensure accurate navigation even in urban environments with tall buildings that may obstruct GPS signals. By utilizing the enhanced dead reckoning system, commuters may rely on continuous and reliable navigation updates, ensuring timely and efficient travel routes.
In yet another example scenario, the disclosed method may be utilized in tracking automotives. The enhanced dead reckoning system may be integrated into autonomous vehicles used for reconnaissance missions in GPS-denied environments. By employing the Lévy flight technique, these autonomous vehicles may navigate through complex terrains, such as urban areas or forests, with improved accuracy and reliability, providing crucial intelligence to necessary personnel.
In yet another example scenario, the disclosed method may be employed in antitheft systems for vehicles and valuable assets. By integrating the enhanced dead reckoning system with antitheft devices, the system may accurately track the location of stolen vehicles or assets, even in areas with poor GPS reception. This ensures quick recovery and minimizes losses for vehicle owners and businesses.
In yet another example scenario, the disclosed method may be employed in one or more wearable devices providing fitness applications, such as running or cycling, the disclosed method may provide accurate tracking of the user's route and performance metrics, even in GPS-deprived environments such as indoor tracks or dense forests. By utilizing the enhanced dead reckoning system, fitness enthusiasts may track their activities with precision, ensuring accurate data for analysis and improvement.
In an exemplary embodiment, the disclosed method collects data on the user's movement and orientation, initializes the position, and estimates speed using accelerometer and gyroscope data to determine direction. Further, the method updates the position based on speed and direction, generates the step length using equation 2, and applies the random step. In no-reception areas, the method uses dead reckoning for accurate tracking. Further, for fitness tracking, the method uses motion data to determine activity type and calculate steps and distance travelled based on the estimated position updates. For example, if the user enters a tunnel or dense forest, the robustness approach of LF-DR handles unpredictable user movements and varying environmental conditions, such as heavy rainfall or snowfall, thereby ensuring consistent tracking.
In an exemplary working scenario: Now referring to exemplary embodiment (500) including Figures 5A-5C, the disclosed system (100) and the method (400) for navigation operation using the Lévy Flight enhanced dead reckoning system for improving position estimation accuracy in GPS-deprived environments, follows the following steps:
Constant Pre-conditions applicable in every case:
The initial position of the object may be always [0o, 0o] which denotes that the object is stationary.
Initial Headings may be initiated by 0o and after the object starts moving the updated headings may be stored in an array for further signal generation and processing.
Speed may be recorded by accelerometer and keep updated at every instance and stored inside an array.
The updated position may be recorded after every 0.5 second of time frame [time frame can be updated w.r.t specific requirement].
Speed, Magnetic field variation data and angular velocity variation data may be recorded with help of the plurality of sensors as explained under dead reckoning system and may be stored inside the respective arrays.
Initialize the objective dimension:
Declare a dimension space of 1000 [random space for initiating the exploration, this space may be modified w.r.t specific requirements and to enhance the accuracy of the optimizer.]
Start an iteration loop from 1 to total recorded changes in heading angle of the object during travelling.
Initialise the Lévy Flight parameters and start the exploration and exploitation process.
Position calculation and graph plot: In reference to Figure 5A
X-axis will be plotted by the data of Time Step of position updating.
Y-axis will be plotted by the data of Updated position.
In this example,
Heading angles taken into consideration are as follows:
headings = [-30, -28, -26, -24, -22, -20, -18, -16, -14, -12, -10, -8, -6, -4, -2,0,2,4,6,8,10,12,14,16,18,20];
Red line depicts the path taken by the object:
The object comes into motion at 1.4th time frame of the unit time with the recorded position of 0.16o as optimised solution w.r.t the known position of 0o as initialised in the above headings array.
Speed and time frame are same as described in pre-conditions section.
Position Processing: In reference to Figure 5B
Above figure shows the calculated position as a result of exploration and exploitation of the objective space from every iteration.
The calculated position depicts the latitude and longitude of the object during the motion.
Negative values are calculated w.r.t negative heading angles recorded by the plurality of sensors; it can be due to acceleration in opposite direction w.r.t orientation of the sensor.
For example: If an accelerometer is aligned such that positive X points right, positive Y points forward, and positive Z points up, an acceleration to the left will produce a negative X value, an acceleration backward will produce a negative Y value, and an acceleration downward will produce a negative Z value.
Sigma_u and Step Length Calculation:
Position = -1.5858 0.3475
Step length = -0.14068 -3.1338
Sigma u = 0.75968
Position = -0.31575 8.9828
Calculation of Sigma u:
s_u={(?(1+ß) sin?(pß/2))/(ß?[(1+ß)/2]*2^(((ß-1))/2) )}^(1/ß)
During iteration the sigma_u is divided into two parts i.e. numerator and denominator counter parts.
Then it may be integrated in the above-mentioned formula for getting the value:
Sigma u = 0.75968
Numerator = 1.0049
Denominator = 1.4766
Input for Sigma_u may be the value of Beta which is 1.4 for this example.
Calculation of Step Length i.e. s=u/|v|^(1/ß)
During the iteration the step length is divided into two parts i.e. u and v counter parts, u for the numerator and v for denominator part.
Then it may be integrated in the above-mentioned formula for getting the value.
u = 0.14044 0.15612
v = -1.885 0.22762
Position = -0.11988 1.6747
Input for Step length may be the value of u and v i.e. normal distribution within the range 0, Sigma u.
Based on these above calculations Position may be calculated and printed on the Command Window in MATLAB, as disclosed in Figure 5C.
Therefore, overall working and calculation of estimated location of the object is as follows:
Location may be recorded by the plurality of sensors and inputted as heading angles and stored in an array for further processing and estimation calculations, For the above stated example we have taken into consideration the following mentioned heading angles:
headings = [-30, -28, -26, -24, -22, -20, -18, -16, -14, -12, -10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20];
Based on these recorded angles optimiser may start estimating the position.
The initial location taken into consideration may be 0^o
In the next step:
The optimiser may calculate the update in the position-
First updation- ?0.1679?^oat time step as 1.457-unit time.
Second updation- ?0.4276?^o at time step as 1.689-unit time.
Third updation- ?0.7499?^o at time step as 2-unit time.
To be noted: Data points taken from the graph as referred to Figure 5A.
The disclosed system (100) and the method (400) for navigation operation uses a combination of Lévy flight enhanced dead reckoning system to optimize the current location of the object, thereby enhancing the navigation operation in the following advantages:
Improved Navigation Accuracy: By computing the updated position of the object by initiating a search to explore one or more estimated positions in a random space based on the current position, the system can explore a larger portion of the search space, leading to more accurate position estimates even in challenging environments where GPS signals are unavailable.
Reduced Cost and Complexity: Unlike traditional dead reckoning systems that require expensive coupling with vehicle odometers or precise accelerometer placement, the disclosed system offers a cost-effective and simplified solution, making it more accessible for various applications.
Ease of Installation: The system eliminates the need for precise sensor placement, simplifying the installation process and reducing associated costs.
Enhanced Reliability: The large steps used to explore the one or more estimated positions in the random space prevent the system from repeatedly searching the same locations, reducing the likelihood of errors and improving overall reliability.
Real-time Updates: The system provides real-time navigation updates, ensuring that users have access to accurate location information, even in challenging environments.
Versatile Application: The system's enhanced dead reckoning capabilities make it suitable for a wide range of applications beyond automotive navigation, including defence, telecommunications, and autonomous robotics, where accurate position estimation is crucial.
In light of the above-mentioned advantages and the technical advancements provided by the disclosed method and system, the claimed steps as discussed above are not routine, conventional, or well understood in the art, as the claimed steps enable the following solutions to the existing problems in conventional technologies. Further, the claimed steps clearly bring an improvement in the functioning of the device itself as the claimed steps provide a technical solution to a technical problem.
Various embodiments of the disclosure provide a non-transitory computer readable medium and/or storage medium, and/or a non-transitory machine-readable medium and/or storage medium having stored thereon, a machine code and/or a computer program having at least one code section executable by a machine and/or a computer for determining the current location of the object. The at least one code section in an application server (103) causes the machine and/or computer including one or more processors to perform the steps, which includes receiving, by an application server (103), the data (207) associated with the plurality of sensors and position of the object. The one or more processors may be configured to selecting, by the application server, an activation function based on a desired output, wherein the desired output is based on an industry type and an application area of the electronic device (101). The one or more processors may be configured to perform one or more predetermined operations for determining the current location of the object.
Embodiments of the present invention have been described in relation to particular embodiments which are intended in all respects to be illustrative rather than restrictive. Alternative embodiments will become apparent to those of ordinary skill in the art to which the present invention pertains without departing from its scope.
From the foregoing, it will be seen that this invention is one well adapted to attain all the ends and objects hereinabove set forth together with other advantages which are obvious, and which are inherent to the structure.
It will be understood that certain features and sub-combinations are of utility and may be employed without reference to other features or sub-combinations. This is contemplated by and is within the scope of the claims.
,CLAIMS:WE CLAIM:
A method (400) for determining a current location of an object, the method (400) comprising:
receiving (401), via a transceiver (204), an initial location and data from a plurality of sensors, wherein the plurality of sensors being disposed on the object;
calculating (402), via a current position computation unit (205), a current position of the object based on the initial location and the data received from the plurality of sensors;
computing (403), via an update position computation unit (206), an updated position of the object by initiating a search to explore one or more estimated positions in a random space based on the current position by employing a Lévy flight technique, wherein for each of the one or more estimated positions a proximity value is computed based on a step length; and
providing (404), via the update position computation unit (206), the updated position of the object in real-time based on the proximity value, wherein the updated position corresponds to an optimized current location of the object.
The method (400) as claimed in claim 1, wherein the updated position of the object being provided in absence of Global Navigation Satellite System (GNSS), wherein the object corresponds to a vehicle, and wherein the updated position provided in real-time is used to provide turn by turn navigation instructions for navigating the vehicle.
The method (400) as claimed in claim 1, wherein the plurality of sensors comprises an accelerometer, a gyroscope, a magnetometer, or combination thereof, wherein the accelerometer is at least one of a dual-axis accelerometer, a triple-axis accelerometer, or combination thereof, and the gyroscope is oriented substantially perpendicular with respect to a longitudinal axis of the object.
The method (400) as claimed in claim 3, wherein the accelerometer is configured to measure a linear acceleration of the object across a plurality of axis.
The method (400) as claimed in claim 1, wherein the object is in at least one of a stationary state and moving state.
The method (400) as claimed in claim 3, wherein the current position of the object is calculated, via the current position computation unit (205), based on the initial location, a current heading angle, and a speed of the object.
The method (400) as claimed in claim 6, wherein the current position of the object is calculated by:
determining an estimated velocity of the object based on accelerometer data;
determining the current heading angle of the object using gyroscope data and magnetometer data, wherein the gyroscope data comprises an angular velocity of the object, and the magnetometer data comprises a strength of Earth’s magnetic field and a directional change of the Earth’s magnetic field;
calculating a displacement of the object based on the speed of the object, the estimated velocity, and the current heading angle; and
integrating the initial location of the object with the displacement of the object to obtain the current position of the object, wherein the current position of the object is provided as input to the Lévy flight technique.
The method (400) as claimed in claim 7, wherein the displacement of the object is calculated, via the current position computation unit (205), by multiplying the speed of the object with one of Sin component of the current heading angle or Cos component of the current heading angle, wherein the Cos component is used to determine the displacement if the object’s current heading angle is 0^0, and wherein the Sin component is used to determine the displacement if the object’s current heading angle is 90^0.
The method (400) as claimed in claim 1, wherein the Lévy flight technique is a Stochastic Metaheuristic technique, and the Lévy flight technique comprises steps of:
initiating an initial iteration for exploring and exploiting the one or more estimated positions in the random space using a plurality of agents based on the current position and a recorded number of inputs from the plurality of sensors;
identifying a number of short steps with occasional large jumps to be taken by the plurality of agents for exploring and exploiting the random space; and
incrementing a count of the initial iteration by one, to balance exploration and exploitation.
The method (400) as claimed in claim 1, wherein the proximity value of each of the one or more estimated positions is computed, via the current position computation unit (205), based on equation 1, wherein each of the one or more estimated positions being represented as vectors.
L(s)~|s|^(-1-ß) ……. Equation 1
where s represents the step length, and ß is an index which lies in a range [0, 2].
The method (400) as claimed in claim 1, wherein the step length is calculated, via the current position computation unit (205), based on equation 2:
s=u/|v|^(1/ß) ……. Equation 2
where u and v are normally distributed parameters and are specified as:
u~N(0,s_u^2 )
v~N(0,s_v^2 )
where,
s_u={(?(1+ß) sin?(pß/2))/(ß?[(1+ß)/2]*2^(((ß-1))/2) )}^(1/ß)
s_v=1
where ?() represents Gamma function and is defined as follows:
?(1+ß)=?_0^8¦?t^ß ?exp?^(-t) dt? ß is an integer, then the Gamma function is defined as follows:
?(1+ß)=ß!
The method (400) as claimed in claim 1, wherein the proximity value of each of the one or more estimated positions is updated, via the update position computation unit (206), by:
updating a position vector for an individual best position;
updating the position vector for a global best position; and
updating a fitness value of the position vector for the individual best position and the global best position.
The method (400) as claimed in claim 1, comprises determining a probable updated position, via the update position computation unit (206), based on equation 3:
probable updated position = s.* estimated position … Equation 3
wherein if the probable updated position is precise than the estimated position based on the proximity value, then the probable updated position is identified as the updated position of the object; and
wherein if the probable updated position is imprecise than the estimated position based on the proximity value, then re-initiating an iteration for exploring the one or more estimated positions.
A system (100) to determine a current location of an object, the system comprising:
a control unit (208) comprises a processor (201); and
a memory (203) communicatively coupled to the processor (201), wherein the memory (203) stores processor instructions, which, on execution, causes the processor (201) to:
receive, via a transceiver (204), an initial location and data from a plurality of sensors, wherein the plurality of sensors being disposed on the object;
calculate, via a current position computation unit (205), a current position of the object based on the initial location and the data received from the plurality of sensors;
compute, via an update position computation unit (206), an updated position of the object by initiating a search to explore one or more estimated positions in a random space based on the current position by employing a Lévy flight technique, wherein for each of the one or more estimated positions a proximity value is computed based on a step length; and
provide, via the update position computation unit (206), the updated position of the object in real-time based on the proximity value, wherein the updated position corresponds to an optimized current location of the object.
The system (100) as claimed in claim 14, wherein the updated position of the object being provided in absence of Global Navigation Satellite System (GNSS), wherein the object corresponds to a vehicle, and wherein the updated position provided in real-time is used to provide turn by turn navigation instructions for navigating the vehicle.
The system (100) as claimed in claim 14, wherein the plurality of sensors comprises an accelerometer, a gyroscope, a magnetometer, or combination thereof, wherein the accelerometer is at least one of a dual-axis accelerometer, a triple-axis accelerometer, or combination thereof, and the gyroscope is oriented substantially perpendicular with respect to a longitudinal axis of the object.
The system (100) as claimed in claim 16, wherein the accelerometer is configured to measure a linear acceleration of the object across a plurality of axis.
The system (100) as claimed in claim 14, wherein the object is in at least one of a stationary state and moving state.
The system (100) as claimed in claim 16, wherein the current position of the object is calculated, via the current position computation unit (205), based on the initial location, a current heading angle, and a speed of the object.
The system (100) as claimed in claim 19, wherein the current position of the object is calculated by:
determining an estimated velocity of the object based on accelerometer data;
determining the current heading angle of the object using gyroscope data and magnetometer data, wherein the gyroscope data comprises an angular velocity of the object, and the magnetometer data comprises a strength of Earth’s magnetic field and a directional change of the Earth’s magnetic field;
calculating a displacement of the object based on the speed of the object, the estimated velocity, and the current heading angle; and
integrating the initial location of the object with the displacement of the object to obtain the current position of the object, wherein the current position of the object is provided as input to the Lévy flight technique.
The system (100) as claimed in claim 20, wherein the displacement of the object is calculated, via the current position computation unit (205), by multiplying the speed of the object with one of a Sin component of the current heading angle or Cos component of the current heading angle, wherein the Cos component is used to determine the displacement if the object’s current heading angle is 0^0, and wherein the Sin component is used to determine the displacement if the object’s current heading angle is 90^0.
The system (100) as claimed in claim 14, wherein the Lévy flight technique is a Stochastic Metaheuristic technique, and the Lévy flight technique comprises steps of:
initiating an initial iteration for exploring and exploiting the one or more estimated positions in the random space using a plurality of agents based on the current position and a recorded number of inputs from the plurality of sensors;
identifying a number of short steps with occasional large jumps to be taken by the plurality of agents for exploring and exploiting the random space; and
incrementing a count of the initial iteration by one to balance exploration and exploitation.
The system (100) as claimed in claim 14, wherein the proximity value of each of the one or more estimated positions is computed, via the current position computation unit (205), based on equation 1, wherein each of the one or more estimated positions being represented as vectors.
L(s)~|s|^(-1-ß) ……. Equation 1
where s represents the step length, and ß is an index which lies in a range [0, 2].
The system (100) as claimed in claim 14, wherein the step length is calculated, via the current position computation unit (205), based on equation 2:
s=u/|v|^(1/ß) ……. Equation 2
where u and v are normally distributed parameters and are specified as:
u~N(0,s_u^2 )
v~N(0,s_v^2 )
where,
s_u={(?(1+ß) sin?(pß/2))/(ß?[(1+ß)/2]*2^(((ß-1))/2) )}^(1/ß)
s_v=1
where ?() represents Gamma function and is defined as follows:
?(1+ß)=?_0^8¦?t^ß ?exp?^(-t) dt? ß is an integer, then the Gamma function is defined as follows:
?(1+ß)=ß!
The system (100) as claimed in claim 14, wherein the proximity value of each of the one or more estimated positions is updated, via the update position computation unit (206), by:
updating a position vector for an individual best position;
updating the position vector for a global best position; and
updating a fitness value of the position vector for the individual best position and the global best position.
The system (100) as claimed in claim 14, wherein the update position computation unit (206) is configured to determine a probable updated position based on equation 3:
probable updated position = s.* estimated position … Equation 3
wherein if the probable updated position is precise than the estimated position based on the proximity value, then the probable updated position is identified as the updated position of the object; and
wherein if the probable updated position is imprecise than the estimated position based on the proximity value, then re-initiating an iteration for exploring the one or more estimated positions.
An electronic device disposed in a vehicle to determine a current location of an object, the electronic device comprising:
a processor (201); and
a memory (203) communicatively coupled to the processor, wherein the memory stores processor instructions, which, on execution, causes the processor to:
receive, via a transceiver (204), an initial location and data from a plurality of sensors, wherein the plurality of sensors being disposed on the object;
calculate, via a current position computation unit (205), a current position of the object based on the initial location and the data received from the plurality of sensors;
compute, via an update position computation unit (206), an updated position of the object by initiating a search to explore one or more estimated positions in a random space based on the current position by employing a Lévy flight technique, wherein for each of the one or more estimated positions a proximity value is computed based on a step length; and
provide, via the update position computation unit (206), in real-time the updated position of the object based on the proximity value, wherein the updated position corresponds to an optimized current location of the object.
The electronic device as claimed in claim 27, wherein the electronic device comprises at least one of a navigation device, a telematics unit, and an instrument cluster, wherein the initial location received by the transceiver (204) corresponds to last known GPS coordinates of the object.
A non-transitory computer-readable storage medium having stored thereon, a set of computer-executable instructions for causing an electronic device comprising one or more processors to perform steps comprising:
receiving an initial location and data from a plurality of sensors, wherein the plurality of sensors being disposed on an object;
calculating (402) a current position of the object based on the initial location and the data received from the plurality of sensors;
computing an updated position of the object by initiating a search to explore one or more estimated positions in a random space based on the current position by employing a Lévy flight technique, wherein for each of the one or more estimated positions a proximity value is computed based on a step length; and
providing in real-time the updated position of the object based on the proximity value, wherein the updated position corresponds to an optimized current location of the object.
Dated this January 24, 2024

Priyank Gupta
Agent for the Applicant
IN/PA-1454