ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING-BASED SUPPLY CHAIN MANAGEMENT ROBOTIC SYSTEM AND METHOD THEREOF

Abstract

The invention provides a comprehensive AI and ML-based supply chain management system that integrates a smart conveyor belt system and warehouse navigation robots to enhance operational efficiency. The smart conveyor belt is equipped with load cells for weight measurement and high-resolution image processing cameras for visual inspection. An AI module analyzes data from these sensors in real time, controlling actuators to adjust the speed and direction of the conveyor belt for optimal product routing. Warehouse navigation robots, equipped with LIDAR and ultrasonic sensors, use machine learning algorithms for precise path planning and obstacle detection, ensuring safe navigation within the warehouse. A central AI control module coordinates the entire system, employing convolutional neural networks (CNNs) for image analysis and reinforcement learning for optimizing robot movement and conveyor operations. This integration reduces human intervention, enhances product quality control, and optimizes resource utilization, making the supply chain more efficient and scalable.

Specification

Description:[013] The following sections of this article will provide various embodiments of the current invention with references to the accompanying drawings, whereby the reference numbers utilised in the picture correspond to like elements throughout the description. However, this invention is not limited to the embodiment described here and may be embodied in several other ways. Instead, the embodiment is included to ensure that this disclosure is extensive and complete and that individuals of ordinary skill in the art are properly informed of the extent of the invention. Numerical values and ranges are given for many parts of the implementations discussed in the following thorough discussion. These numbers and ranges are merely to be used as examples and are not meant to restrict the claims' applicability. A variety of materials are also recognised as fitting for certain aspects of the implementations. These materials should only be used as examples and are not meant to restrict the application of the innovation.
[014] Referring now to the drawings, these are illustrated in FIG. 1, the invention provides a comprehensive AI and ML-based supply chain management system integrating conveyor belt systems and warehouse navigation robots. The key components of the invention are described below in detail:
[015] In accordance with another embodiment of the present invention, smart Conveyor Belt System: The conveyor belt system is equipped with load cells distributed along the length of the belt. These load cells continuously measure the weight of products with high precision. High-resolution image processing cameras are positioned strategically along the conveyor belt to capture detailed visual data of the products being transported. The cameras enable the system to perform visual inspection, detecting defects or irregularities in the products.
[016] In accordance with another embodiment of the present invention, the load cells and image processing cameras are connected to an AI module, which uses machine learning algorithms to analyze the data in real time. The AI module determines the appropriate actions, such as product classification, sorting, and movement, to ensure operational efficiency. Actuators are installed to dynamically adjust the conveyor belt's speed and direction based on the AI module's commands, thereby optimizing the product routing process. This configuration allows for adaptive decision-making, resulting in improved throughput and minimal manual intervention.
[017] In accordance with another embodiment of the present invention, The warehouse navigation robots are designed for precise transportation of goods within warehouse environments. Each robot is equipped with LIDAR for generating a 3D map of the warehouse, ultrasonic sensors for obstacle detection, and ML processors for real-time path planning and navigation. LIDAR sensors emit laser pulses and measure the time taken for reflections to return, allowing the robots to accurately map their surroundings. Ultrasonic sensors add an extra layer of safety by detecting obstacles that might be transparent or not easily identified by LIDAR as shown in figure 2.
[018] In accordance with another embodiment of the present invention, the ML processors onboard the robots run advanced algorithms to determine the most efficient route for transporting goods, taking into account factors such as warehouse layout and dynamic obstacles. The robots communicate with the central AI module to receive task instructions, such as transporting goods from the conveyor belt to designated storage areas. The robots use motorized wheels controlled by precise motor drivers to execute movement commands. This automation ensures seamless integration with the conveyor system, reducing human labor and errors while increasing efficiency.
[019] AI Control Module: The AI control module is the central processing unit of the entire supply chain management system. It interfaces with the conveyor belt and navigation robots, collecting data from load cells, cameras, LIDAR, and ultrasonic sensors. The AI control module is housed in a high-performance server capable of processing large volumes of data in real time.
The AI control module uses convolutional neural networks (CNNs) for image analysis to classify products and detect defects.
[020] Reinforcement learning algorithms are used for optimizing the movements of robots and the operation of the conveyor belt. For instance, when the AI module detects a defective product, it instructs the conveyor system to redirect the product to a rejection bin. Similarly, the AI module calculates optimal paths for the navigation robots to avoid obstacles and minimize travel time. The control module continuously monitors sensor feedback to dynamically adjust operations, ensuring the system is always operating at maximum efficiency and effectively handling any unexpected situations.
[021] The integration of AI and ML with the conveyor belt and warehouse navigation robots significantly increases the overall efficiency of the supply chain. Automated sorting, classification, and movement of products reduce the time required for manual handling, thereby speeding up the entire process. The AI control module processes data from various sensors in real time, enabling the system to make quick decisions. This real-time capability allows for immediate responses to changing conditions, such as product defects or unexpected obstacles, resulting in minimal delays and enhanced productivity.
[022] By automating tasks such as product sorting, movement, and navigation within the warehouse, the system reduces the need for human labor. This not only minimizes human errors but also frees up personnel to focus on more strategic tasks, thereby improving workforce efficiency.
[023] The benefits and advantages that the present invention may offer have been discussed above with reference to particular embodiments. These benefits and advantages are not to be interpreted as critical, necessary, or essential features of any or all of the embodiments, nor are they to be read as any elements or constraints that might contribute to their occurring or becoming more evident. The use of high-resolution image processing cameras for visual inspection allows the system to detect defects and irregularities in products with high accuracy. This results in improved quality control, ensuring that only products meeting the required standards proceed through the supply chain.
The AI-driven navigation robots and conveyor belt system are designed to optimize the use of resources such as energy and space. The robots calculate the most efficient routes, and the conveyor belt's speed is adjusted dynamically to match the workload, thereby reducing energy consumption and maximizing operational efficiency.
The integration of LIDAR and ultrasonic sensors in the navigation robots enhances safety by accurately detecting and avoiding obstacles. This reduces the risk of collisions and accidents, creating a safer working environment within the warehouse.
[024] Although specific embodiments have been used to describe the current invention, it should be recognized that these embodiments are merely illustrative and that the invention is not limited to them. The aforementioned embodiments are open to numerous alterations, additions, and improvements. These adaptations, changes, additions, and enhancements are considered to be within the purview of the invention. , Claims:1. A supply chain management system, comprising:
a smart conveyor belt system equipped with load cells and high-resolution image processing cameras, wherein the load cells measure the weight of products and the cameras capture visual data of products on the conveyor belt;
an AI module connected to the conveyor belt system, configured to analyze data from the load cells and cameras using machine learning algorithms, and control actuators to adjust the speed and direction of the conveyor belt based on the analysis;
warehouse navigation robots equipped with LIDAR sensors, ultrasonic sensors, ML processors, and motorized wheels, wherein the LIDAR sensors generate a 3D map of the warehouse and the ultrasonic sensors detect obstacles; and
an AI control module interfacing with the conveyor belt system and warehouse navigation robots, wherein the AI control module uses machine learning algorithms to classify products, detect defects, and optimize robot movements and conveyor belt operations;
wherein the conveyor belt system comprises actuators that dynamically adjust the speed and direction of the conveyor belt to optimize the routing of products based on commands from the AI module;
wherein the AI module is configured to perform product classification, sorting, and defect detection by analyzing the visual data captured by the high-resolution cameras.
wherein the warehouse navigation robots are configured to communicate with the AI control module to receive task instructions, including transporting goods from the conveyor belt system to designated storage areas.
2. The system as claimed in claim 1, wherein the warehouse navigation robots use LIDAR sensors to generate a 3D map of the warehouse environment and ultrasonic sensors to detect obstacles, thereby ensuring safe navigation within the warehouse.
3. The system as claimed in claim 1, wherein the ML processors onboard the warehouse navigation robots determine the most efficient route for transporting goods, taking into account the warehouse layout and dynamic obstacles.
4. The system as claimed in claim 1, wherein the AI control module is housed in a high-performance server configured to process data collected from load cells, cameras, LIDAR sensors, and ultrasonic sensors in real time.
5. The system as claimed in claim 1, wherein the AI control module uses convolutional neural networks (CNNs) for image analysis to classify products and detect defects.
6. The system as claimed in claim 1, wherein the AI control module is configured to generate instructions for redirecting defective products to a rejection bin and calculating optimal paths for warehouse navigation robots to minimize travel time.
7. The system as claimed in claim 1, wherein the AI control module continuously monitors sensor feedback from the conveyor belt system and warehouse navigation robots to dynamically adjust operations in response to unexpected situations.
8. The system as claimed in claim 1, wherein the warehouse navigation robots are equipped with motorized wheels controlled by motor drivers to execute movement commands received from the AI control module.
9. The system as claimed in claim 1, wherein the integration of LIDAR and ultrasonic sensors in the warehouse navigation robots is configured to enhance safety by detecting and avoiding obstacles, thereby reducing the risk of collisions and accidents within the warehouse.
10. The system as claimed in claim 1, wherein the conveyor belt system and warehouse navigation robots are designed to optimize resource utilization, including energy and space, by dynamically adjusting operations based on the workload and calculated efficient routes.

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