Description:Field of the Invention
This invention relates to a system of Pattern detection and replication with computer vision and AI.
Background of the Invention
There is considerable demand for autonomous mobile robots that can patrol, manage crowds, and monitor the environment in real-time with slight human interference in various industrial and public environments. Most of those present in the market today are costly software-based solutions; they require much custom coding. The result is very little flexibility; thus, advanced automation has been beyond the reach of smaller business and industry applications. These systems also essentially require significant technical expertise to set up and operate, hence their limited usability among non-expert operators.
That is what makes taking a cost-effective, user-friendly, highly functional, and flexible mobile robot system capable of incorporating advanced AI and robust sensor technology to allow autonomous navigation and natural interaction with the environment for effective, safe, and reliable operation an imperative. The problems are not restricted to the level of technical performance, though; ergonomics and usability necessities need to be provided for reaching successfully an extensive user base. Examples of this include establishing how quickly and easily the robot can be set up, configured, and scaled to diverse environmental and operational demands without the need for advanced technical knowledge or resources.
In addition, the robot has to fuse multiple sensory inputs: high-megapixel cameras, IMUs, LiDAR, infrared, and ultrasonic sensors, into an experience of the environment.
It is expected to process the data in real-time by using these advanced AI models through the features of TensorFlow and YOLOv5, enabling it to recognize precise images and detect vital patterns for viable and responsive navigation. Besides that, there should be effective human-robot interaction enabled through audio components, making this received voice command and feedback given verbally helpful and functional in all kinds of ways while being completely versatile. Finally, it's this invention, designed to proffer a solution that is advanced yet affordable, with democratized technology towards automation to be feasible across much wider applications and industry space. The proposed mobile robot system, by facing all these multidimensional challenges, endeavors to really innovate the way that autonomous robots are deployed and used across the various sectors, increasing the value in terms of their efficiency, safety, and overall operational performance.
US9573277B2 The disclosed visual RRC-humanoid robot is a computer-based system that has been programmed to reach human-like levels of visualization Artificial Intelligence (AI). Behavioral-programming techniques are used to reach human-like levels of identification AI, recognition AI, visualization AI, and comprehension AI. The system is programmed to identify, recognize, visualize and comprehend the full array of sizes, distances, shapes, and colors of objects recorded in the FOV of the system. The following innovative features have been incorporated into the system: (i) incorporation of the RRC, (ii) incorporation of the Relational Correlation Sequencer (RCS): A proprietary RRC-module, (iii) a paradigm shift in the analytical-programming methodology employed in computer vision systems, (iv) incorporation of a central hub of intelligence, (v) design of a “self knowledge” capability and Internalization of all data, and (vi) design of an interface circuit compatible with human-like levels of visualization-AI.
RESEARCH GAP: Scalable to new environment: The robot can be used in various environments, from an industrial setting to public space a factor that is an added advantage for its modular design.
US11662228B2 A surface shape determination system includes a surface shape sensor in the form of a flexible and stretchable elastomeric substrate with strain/displacement sensing elements embedded in it. The sensor may be a single-core optical fiber with a series of fiber Bragg Gratings (FBGs) located at predetermined positions along its length. A light source provides an incident light spectrum at one end of the fiber. Each grating of the fiber has index modulation which causes particular wavelengths of the light spectrum that do not satisfy the Bragg condition to be reflected back in the fiber. The refractive index of each grating changes with strain on the substrate due to deflection of it. An interrogator captures the reflected wavelengths and retrieves signal information there from. A processor receives the output of the interrogator and performs non-linear regression analysis on the information using a neural network to reconstruct the surface morphology in real-time.
RESEARCH GAP: Scalable Software Updates: An open-source platform like TensorFlow allows scalable and continuous updating of software on this system, hence keeping it fully abreast of innovations in the field of AI and robotics.
US10366531B2 Described herein are systems for generating 3D models. A first point cloud of data for an object may be generated based on boundary information obtained by the object boundary detector(s). Dimensions for the object may be determined based on the first point cloud of data. A second point cloud of data may be generated based on the dimensions for the object and a configuration of light projectors where the second point cloud corresponds to potential coordinates for a location where the robotic member and end effector can be positioned along a path around the object to capture the image data of the object. A path may be generated to avoid collision between the object and the robotic member or end effector while optimizing the number of capture location points within the second point cloud of data.
RESEARCH GAP: Improved security The robot's advanced sensing and processing capabilities could be used in security applications for monitoring restricted areas, as well as sensing for unauthorized access.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. This invention relates to Pattern detection and replication with computer vision and AI.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The mobile robot system has several advanced technologies that enable the system to operate independently and flexibly execute tasks. The included technologies consist of a high-resolution camera for capturing fine details of visual data, an Inertial Measurement Unit composed of an accelerometer, magnetometer, and gyroscope for high-resolution, accurate orientation tracking of the movements, and a microcontroller for sensor data processing to control the robot. The central computing unit is based on a Raspberry Pi that lodges TensorFlow and YOLOv5, which are responsible for real-time image recognition and pattern detection. The DC-encoded motors provide accurate movement control, while this robot has additional sensors that quickly improve environmental awareness, such as LiDAR, infrared, and ultrasonic sensors. It has a set of audio components that are inclusive of speakers and microphones for human-robot scenarios and can support handling uttered commands or giving back output in the form of speech.
The other side of our system is the nontechnical issues associated with robot practical deployment to be usable within various setups. These include the usability of robots through design and ergonomics to make it easier when setting up and operating the robot. The system is designed to be user-friendly, with minimal need for technical competence to be implemented during the setup and configuration for deployment. This makes it user-friendly to a broad group of users, from industrial workers to non-expert operators. Its modular design allows the robot to customize itself easily and scale up per the designed parameters that may fit the requirements and demands of any job or environment.
In addition, such a system cuts costs because it does not involve costly software requirements or call for extensive, custom-coded solutions, thus opening the potential of advanced automation technologies to smaller businesses and industries.

Typically, the data processing workflow starts by capturing images related to the environment via a camera for a mobile robot system. The images captured by the camera are then used as input for processing within the Raspberry Pi. At the same time, uses TensorFlow and YOLOv5 models to identify and recognize the patterns and objects within the visual data. AI models process images online, extract features, and convey that information in data from which specific actions can be taken.
At the same time, the information about the orientation and motion of the robot is perceived. The accelerometer perceives the acceleration; the gyroscope provides information about the angular velocity, and the magnetometer measures position relative to the Earth's magnetic field. The system is realized in the microcontroller retains coherent tracking of the position and movement of the robot.
Such processed visual and orientation data is integrated so that the system can create the overall feeling of the robot's surroundings. This information is implemented in generating accurate navigation commands, sent to the DC-encoded motors controlling the robot's wheels, enabling it to navigate along a moving path smoothly and with high precision.
As the robot moves, its sensors feed it with a constant data stream. The LiDAR executes our distance measurements, although the infrared sensor can detect heat signatures, and the ultrasonic decides the proximity to an obstacle in space. All this makes it possible for the robot to interact with environmental changes by detecting obstacles and finding out how change is realized in its environment.
In this course, the robot shall communicate with the human operator by functioning its auditory devices. The microphone captures spoken commands, which, in turn, the system processes to carry out the actions. The speaker delivers linguistic data, either to inform the operator of the robot's state or as a warning in the case of some error.
This uninterrupted flow of information from image capturing to the execution of actions makes the robot work effectively and autonomously. The integration of advanced AI models—with robust sensor technology—allows the robot to handle complex tasks with minimal human intervention, which makes this a versatile solution for a variety of industrial applications. Its modular design and the user-friendly interface further boost applicability, as it allows easy deployment and customization in quite a variety of environments.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
Figure 1: General Architecture of the System
Figure 2: Detailed Architecture of the system
Figure 3: Algorithmic Structure of the system
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.

DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The mobile robot system has several advanced technologies that enable the system to operate independently and flexibly execute tasks. The included technologies consist of a high-resolution camera for capturing fine details of visual data, an Inertial Measurement Unit composed of an accelerometer, magnetometer, and gyroscope for high-resolution, accurate orientation tracking of the movements, and a microcontroller for sensor data processing to control the robot. The central computing unit is based on a Raspberry Pi that lodges TensorFlow and YOLOv5, which are responsible for real-time image recognition and pattern detection. The DC-encoded motors provide accurate movement control, while this robot has additional sensors that quickly improve environmental awareness, such as LiDAR, infrared, and ultrasonic sensors. It has a set of audio components that are inclusive of speakers and microphones for human-robot scenarios and can support handling uttered commands or giving back output in the form of speech.
The other side of our system is the nontechnical issues associated with robot practical deployment to be usable within various setups. These include the usability of robots through design and ergonomics to make it easier when setting up and operating the robot. The system is designed to be user-friendly, with minimal need for technical competence to be implemented during the setup and configuration for deployment. This makes it user-friendly to a broad group of users, from industrial workers to non-expert operators. Its modular design allows the robot to customize itself easily and scale up per the designed parameters that may fit the requirements and demands of any job or environment.
In addition, such a system cuts costs because it does not involve costly software requirements or call for extensive, custom-coded solutions, thus opening the potential of advanced automation technologies to smaller businesses and industries.
Typically, the data processing workflow starts by capturing images related to the environment via a camera for a mobile robot system. The images captured by the camera are then used as input for processing within the Raspberry Pi. At the same time, uses TensorFlow and YOLOv5 models to identify and recognize the patterns and objects within the visual data. AI models process images online, extract features, and convey that information in data from which specific actions can be taken.
At the same time, the information about the orientation and motion of the robot is perceived. The accelerometer perceives the acceleration; the gyroscope provides information about the angular velocity, and the magnetometer measures position relative to the Earth's magnetic field. The system is realized in the microcontroller retains coherent tracking of the position and movement of the robot.
Such processed visual and orientation data is integrated so that the system can create the overall feeling of the robot's surroundings. This information is implemented in generating accurate navigation commands, sent to the DC-encoded motors controlling the robot's wheels, enabling it to navigate along a moving path smoothly and with high precision.
As the robot moves, its sensors feed it with a constant data stream. The LiDAR executes our distance measurements, although the infrared sensor can detect heat signatures, and the ultrasonic decides the proximity to an obstacle in space. All this makes it possible for the robot to interact with environmental changes by detecting obstacles and finding out how change is realized in its environment.
In this course, the robot shall communicate with the human operator by functioning its auditory devices. The microphone captures spoken commands, which, in turn, the system processes to carry out the actions. The speaker delivers linguistic data, either to inform the operator of the robot's state or as a warning in the case of some error.
This uninterrupted flow of information from image capturing to the execution of actions makes the robot work effectively and autonomously. The integration of advanced AI models—with robust sensor technology—allows the robot to handle complex tasks with minimal human intervention, which makes this a versatile solution for a variety of industrial applications. Its modular design and the user-friendly interface further boost applicability, as it allows easy deployment and customization in quite a variety of environments.
A system of Pattern detection and replication with Computer vision and AI comprises a plurality of Robots (1.1, 1.2. 1.N) Path/Coordinates (2), Cloud Server (3), Perform action using actuators (4), FHD camera (5), Lidar (TF-LUNA) (6), IMU (7), Gyroscope (LYPR540AH) (8) Neural stick (9), Raspberry Pi 3v+ (10), Microcontroller (11), Motor Driver (12), Infrared (13), GPS (14), Accelometer (15), Magnetometer (16), DC encoded motor (17), Motor (18), 12v 3amp Lithium Polymer (Battery), (19), Charger (20), AC Outlet (21), Changing Current (22) wherein the camera is used for capturing fine details of visual data.
In another embodiment an Inertial measurement unit composed of an accelerometer, magnetometer, and gyroscope for high-resolution, accurate orientation tracking of the movements, and a microcontroller for sensor data processing to control the robot.
In another embodiment the sensors detect obstacles, measure or gauge distances, and change itself in response to environmental signals.
In another embodiment the DC-encoded motors provide accurate movement control, while this robot has additional sensors that quickly improve environmental awareness.
In another embodiment a set of audio components that are inclusive of speakers and microphones for human-robot scenarios and can support handling uttered commands or giving back output in the form of speech.
In another embodiment the central computing unit is based on a Raspberry Pi.
In another embodiment the processing of the captured images includes using TensorFlow and YOLOv5 models.
In another embodiment the controlling of the movement of the mobile robot includes using DC-encoded motors.

ADVANTAGES OF THE INVENTION
1. Real-Time Image Recognition and Processing; This lets the image recognition and pattern detection be accomplished in real-time, using TensorFlow and YOLOv5 on the Raspberry Pi to allow the robot to react appropriately in its usual environment.
2. Improved Environmental Awareness: Other sensors include LiDAR, infrared, and ultrasonic, which afford the robot a substantially extended perception of its environment. It can detect obstacles, measure or gauge distances, and change itself in response to environmental signals.
3. Versatile Human-Robot Interaction: Audio components such as speakers and microphones allow the robot to respond to spoken commands and give voice outputs, thereby making the robot friendly for the user.
4. User-Friendly Design and Ergonomics: The system is user-friendly with an ergonomic design, making it easy for technical and non-technical users to set up and operate the robot without much effort.
5. Modular and Scalable Alongside this, modularity makes it easy to customize and scale, so robots can fundamentally change their task and work environment with minimal need for reconfiguration.
6. Cost-Effective Implementation: The system has been cost-effective, as it does not rely heavily on expensive software solutions or extensive custom coding, thus reducing the cost to industry and bringing advanced automation technology within reach of small businesses.
, Claims:1. A system of Pattern detection and replication with Computer vision and AI comprises a plurality of Robots (1.1, 1.2. 1.N) Path/Coordinates (2), Cloud Server (3), Perform action using actuators (4), FHD camera (5), Lidar (TF-LUNA) (6), IMU (7), Gyroscope (LYPR540AH) (8) Neural stick (9), Raspberry Pi 3v+ (10), Microcontroller (11), Motor Driver (12), Infrared (13), GPS (14), Accelometer (15), Magnetometer (16), DC encoded motor (17), Motor (18), 12v 3amp Lithium Polymer (Battery), (19), Charger (20), AC Outlet (21), Changing Current (22) wherein the camera is used for capturing fine details of visual data.
2. The system as claimed in claim 1, wherein an Inertial measurement unit composed of an accelerometer, magnetometer, and gyroscope for high-resolution, accurate orientation tracking of the movements, and a microcontroller for sensor data processing to control the robot.
3. The system as claimed in claim 1, wherein the sensors detect obstacles, measure or gauge distances, and change itself in response to environmental signals.
4. The system as claimed in claim 1, wherein the DC-encoded motors provide accurate movement control, while this robot has additional sensors that quickly improve environmental awareness.
5. The system as claimed in claim 1, wherein a set of audio components that are inclusive of speakers and microphones for human-robot scenarios and can support handling uttered commands or giving back output in the form of speech.
6. A method of operating a mobile robot, comprising:
capturing images of an environment;
processing the captured images using a central computing unit to identify objects and patterns;
controlling the movement of the mobile robot based on the identified objects and patterns.
7. The system as claimed in claim 1, wherein the central computing unit is based on a Raspberry Pi.
8. The system as claimed in claim 1, wherein the processing of the captured images includes using TensorFlow and YOLOv5 models.
9. The system as claimed in claim 1, wherein the controlling of the movement of the mobile robot includes using DC-encoded motors.