Description:FIELD OF THE INVENTION
This invention relates to AI Lens for complete product specifications.
BACKGROUND OF THE INVENTION
Laboratories in many scientific fields struggle with the organization and management of equipment, tools, and materials used in their respective laboratories. The methods currently used for the organization and management of this require much workforce, which is inefficiently prone to waste and, hence, might provoke errors in inventory management and safety risks of the delicate or hazardous items if mishandled. The need for a technological solution that will autonomously and strictly carry out lab logistics is very urgent: the items have to be stored in an appropriate place regularly and be readily available when needed by researchers or technicians.
These include the automated systems that still are not advanced and developed enough to operate effectively in the flexible laboratory scenario, where complex spatial constraints, diversity of objects and object types, and different storage configurations bring complex logistical hurdles. Furthermore, traditional robotics platforms operate with difficulty under the circumstances of real-time object detection, and their exact navigation limits the possibility of changing conditions that can be adopted and harnessed for the most entire efficiency of workflows.
These challenges surmount a requirement for high-end, fully complete robotic systems equipped with advanced sensors, intelligent image processing, and adaptive navigation algorithms. It incorporates very recent models in computer vision, like YOLO 5, for fast and accurate detection of various objects and is equipped with robust sensor fusion techniques using lidar, ultrasonic, and infrared sensors for precise localization and performing functions in the mapped lab space. Machine learning frameworks like TensorFlow will be deployed such that the system can learn and keep improving how organizations are done in dynamic and diverse laboratory environments.
This new robotic solution aims to help streamline laboratory operations, enhance laboratory safety protocols, and enable researchers to spend more time on scientific inquiry and experimentation. This will supposedly bring about an overall change in laboratory management practices and set new standards of efficiency and productivity levels in work related to research and development.
US8930370B2 The method comprises processing plural product information records from the product information sources into one or more groups based on which product information records are likely to correspond to the same product, correlating a unique product ID corresponding to the product associated with each of said groups to identify the product, comparing each identified product to categories of a taxonomy to determine a category for the identified products in the taxonomy, and determining attributes for each categorized product based on the product information records corresponding to each group, creating product specifications based on the determined attributes and storing the product specification in the corresponding determined categories of the taxonomy.
RESEARCH GAP: This invention synthetically makes a 3D map of the environment and provides the level of detail required to navigate then and move with the utmost precision around obstacles. Increased Scientific Focus: This allows for increased focus of laboratory technicians and researchers on their core scientific activities and experimentation, thereby fostering innovation and discovery.
US20190325498A1 This disclosure is directed to systems, methods, and machine-readable media for facilitating online purchasing. In general, techniques are disclosed to facilitate online and mobile purchasing. The disclosure includes technology that allows an agnostic shopping experience, i.e., the shopping may occur on a vendor neutral platform. According to one or more embodiments, facilitating online purchasing may allow a person to purchase correctly sized products or products suited to the need, more conveniently, more consistently, more cheaply and more easily. The more accurate or correct shopping of products saves the wasting of resources required in return shipments and replacement shipments and packaging.
RESEARCH GAP: Adapts to changes of laboratory layouts and configurations through machine learning frameworks to enhance operational flexibility.It has comprehensive vision capabilities through the adoption of high-resolution cameras and sophisticated computer vision algorithms, like YOLO 5, for rapid and accurate object detection and localization.
US11915288B2 This disclosure is directed to systems, methods, and machine readable media for facilitating online purchasing. In general, techniques are disclosed to facilitate online and mobile purchasing. The disclosure includes technology that allows an agnostic shopping experience, i.e., the shopping may occur on a vendor neutral platform. According to one or more embodiments, facilitating online purchasing may allow a person to purchase correctly sized products or products suited to the need, more conveniently, more consistently, more cheaply and more easily. The more accurate or correct shopping of products saves the wasting of resources required in return shipments and replacement shipments and packaging.
RESEARCH GAP: Precision and Accuracy: Uses articulated arms with precision motors and advanced sensors for soft manipulation and object positioning accurately.
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 Lens for complete product specifications.
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 invention is a system—more specifically, a general robotic system that maintains order and efficiency in many laboratory environments. Imagine now a robot that has such sensors and actuators; it can identify objects and then manipulate them in the way it's supposed to. This type of robot would also help further to store the equipment, tools, and materials that are supposed to be permanently placed in specific locations to reduce human errors.
On the less technical level, the robotic assistant will automate most of the ordinary, mundane labor involved in shelving items, rearranging equipment, and even running inventory checks. This would free laboratory technicians and researchers from a lot of the logistics associated with their work. At the same time, they would then be able to stick to their core jobs, knowing quite well that the robot would do it to perfection in any case. Not only does it save much time, but it also boosts safety by capturing accidental occurrences from mishandled and misplaced stuff.
It is somewhat technical in that the robot's architecture is robust and very sophisticated. It is endowed with several articulated arms having precision motors and servo mechanisms matching grasp and manipulation for delicate grip over objects. Its in-built cameras using computer vision algorithms based on technologies such as YOLO 5 enable the robot to look around and most accurately assess which objects are positioned where. Other sensors, in the likes of lidar, ultrasonic, and infrared, equip it with being perceptive to the environment and further enable it to navigate across very complex laboratory settings.
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 Structure of the system
Figure 2: Detailed Structure 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 invention is a system—more specifically, a general robotic system that maintains order and efficiency in many laboratory environments. Imagine now a robot that has such sensors and actuators; it can identify objects and then manipulate them in the way it's supposed to. This type of robot would also help further to store the equipment, tools, and materials that are supposed to be permanently placed in specific locations to reduce human errors.
On the less technical level, the robotic assistant will automate most of the ordinary, mundane labor involved in shelving items, rearranging equipment, and even running inventory checks. This would free laboratory technicians and researchers from a lot of the logistics associated with their work. At the same time, they would then be able to stick to their core jobs, knowing quite well that the robot would do it to perfection in any case. Not only does it save much time, but it also boosts safety by capturing accidental occurrences from mishandled and misplaced stuff.
It is somewhat technical in that the robot's architecture is robust and very sophisticated. It is endowed with several articulated arms having precision motors and servo mechanisms matching grasp and manipulation for delicate grip over objects. Its in-built cameras using computer vision algorithms based on technologies such as YOLO 5 enable the robot to look around and most accurately assess which objects are positioned where. Other sensors, in the likes of lidar, ultrasonic, and infrared, equip it with being perceptive to the environment and further enable it to navigate across very complex laboratory settings.
Figure 1: General Structure of the system this new robotic solution aims to help streamline laboratory operations, enhance laboratory safety protocols, and enable researchers to spend more time on scientific inquiry and experimentation. This will supposedly bring about an overall change in laboratory management practices and set new standards of efficiency and productivity levels in work related to research and development.
Figure 2: Detailed Structure of the system the core of intelligence is based on a Raspberry Pi or similar embedded computing platform, including very powerful processors along with GPUs. Such hardware will allow real-time decision and coordination of working with the help of machine-learning frameworks, such as TensorFlow. By learning and adapting continuously, the organizational skills are fine-tuned, adapting to the emerging environment in a changed laboratory layout with the addition of new objects or configurations.
The busy array of high-resolution cameras strategically positioned initiates the processing process of the robotic system for the capture of the comprehensive scenes of the laboratory workspace. Yet before that, each image goes through several careful preprocessing steps aimed at clarity and consistency: resizing to standard dimensions, correction for lighting variations to normalize levels of brightness, and filtration to reduce noise, enhancing image quality for further analysis.
Around that, these images are fed to advanced computer vision algorithms empowered by YOLO 5, a high-speed and highly accurate state-of-the-art object detection model. YOLO 5 works by taking an image and dividing it into a grid. It then simulates the regression of bounding boxes and acts on the class probabilities for each grid cell that might fall within an object. Among laboratory setups, this will allow the robot to quickly and efficiently identify large amounts of items, mainly comprising equipment, tools, chemicals, and containers.
It uses advanced localization techniques to support the detection of objects and establish their exact spatial coordinates within the laboratory. Armed with information from the robot's set of sensors, namely lidar, ultrasonic, infrared, and those derived from cameras, it can generate a very detailed 3D map of the environment or surroundings. This mapping allows it to move accurately throughout a laboratory or to know the relative positioning among objects, shelves, workbenches, and structures.
Figure 3: Algorithmic structure of the system, this enables the robot to perform its way around the lab, constantly realigning its path with the updating internal map in a dynamic manner that is sensor feedback-based in real-time. Adaptive navigation ensures that the robot moves safely and effectively around constraints or environmental modifications; it guarantees operational fluidity while not sacrificing the accuracy of movements.
In addition, it also contains possibly machine learning models with a Tensorflow backend fitted into it, which enhances the decision-making ability of the system. Models aid the robot to learn over time and, therefore, adapt—advancing the propositioned abilities to handle complicated scenarios such as the identification and handling of fragile equipment or hazardous materials that need proper care of.
In summary, the processing pipeline for the robotic system smoothly fuses the front-line techniques in image processing, robust sensor fusion, and elevated AI algorithms. State-of-the-art AI and sensor technologies merged in this robotic system honestly mark laboratory automation at a new level of sophistication. It effectively manages the logistics in the lab, working hand in hand with efficiency, safety, and organization; from there, more research heroes and technicians are left with more time for scientific inquiry and experimentation. With further advancements in technology, such innovations promise to restructure the very operational functions of laboratories in the pursuit of scientific discovery and innovation.
A Lens for complete product specifications comprise a plurality of Bots (1.1, 1.2, 1.N), 3D Mapping Area (2), Cloud Server (3), Relocate Misplaced Items (4), Raspberry Pi 3v+ (4), Lidar Sensor (5), Camera (1280x720p) (6), 16 Channel Servo Module (7), 1 to 3 Servo Motors (8), Keyboard (9), Infrared (9), Mouse (10), Motor Driver (10), DC encoded Motor (10), LCD Screen (10), 12v 3amp Lithium Polymer (Battery), (11), Charger (12), AC Outlet (13) and Changing Current (14) wherein Sensors such as lidar, ultrasonic, and infrared enable the system comprehend the environment and navigate through exceptionally complicated laboratory environments.
In another embodiment actuator is used for identify objects and then manipulate them in the way it's supposed to.
In another embodiment the core of intelligence is based on a microprocessor or similar embedded computing platform, including very powerful processors along with GPUs. Such hardware will allow real-time decision and coordination of working with the help of machine learning frameworks.
ADVANTAGES OF THE INVENTION
Labor Efficiency: The automation of shelving, equipment rearrangement, and inventory checks speeds up just about any kind of business, those mundane tasks allowing human resources to be channeled into more complex and creative tasks.
Safety Enhancement: Avoids accidents and mishaps by assuredly treating fragile and dangerous stuff carefully and accurately.
Time savings: It executes tasks effectively and consistently, which yields significant time savings over manual operations.
Environmental awareness: Equipped with lidar, ultrasonic, and infrared sensors for safe and optimum movement through a complex environment such as the laboratory.
, Claims:1. A Lens for complete product specifications comprise a plurality of Bots (1.1, 1.2, 1.N), 3D Mapping Area (2), Cloud Server (3), Relocate Misplaced Items (4), Raspberry Pi 3v+ (4), Lidar Sensor (5), Camera (1280x720p) (6), 16 Channel Servo Module (7), 1 to 3 Servo Motors (8), Keyboard (9), Infrared (9), Mouse (10), Motor Driver (10), DC encoded Motor (10), LCD Screen (10), 12v 3amp Lithium Polymer (Battery), (11), Charger (12), AC Outlet (13) and Changing Current (14) wherein Sensors such as lidar, ultrasonic, and infrared enable the system comprehend the environment and navigate through exceptionally complicated laboratory environments.
2. The system as claim in claim 1, wherein actuator is used for identify objects and then manipulate them in the way it's supposed to.
3. The system as claim in claim 1, wherein the core of intelligence is based on a microprocessor or similar embedded computing platform, including very powerful processors along with GPUs.
4. The system as claim in claim 1, wherein hardware allows real-time decision and coordination of working with the help of machine-learning frameworks.
5. The system as claim in claim 1, wherein the computing platform is configured to process images captured by the cameras and perform real-time decision-making.
6. The system as claim in claim 1, wherein the computer vision algorithm is configured to detect and identify objects within the laboratory environment.
7. The system as claim in claim 1, wherein the localization system uses sensor data to generate a 3D map of the laboratory environment.
8. The system as claim in claim 1, wherein the machine learning model is configured to enable the robot to learn and adapt to changes in the laboratory environment.
9. The system as claim in claim 1, wherein the sensors include lidar, ultrasonic, and infrared sensors.
10. The system as claim in claim 1, wherein the machine learning model is based on TensorFlow.