AI robot intelligent target identification and tracking platform based on deep learning

Abstract

The invention provides an AI robot intelligent target identification and tracking platform based on deep learning, and aims to realize efficient, accurate and real-time target identification and tracking by integrating an advanced deep learning algorithm, a multi-sensor fusion technology, edge computing and cloud collaboration. The platform comprises a hardware layer, a sensing layer, a data processing layer, a decision-making layer and an execution layer, is equipped with various sensors such as a high-definition camera, a laser radar, an infrared sensor and an ultrasonic sensor, carries out target identification by using a YOLOv7 model, realizes multi-target continuous tracking by combining a Deep SORT algorithm, and realizes multi-target tracking. And multi-sensor data fusion is carried out through Kalman filtering and particle filtering algorithms. The adaptive learning module improves the adaptability and generalization ability of the system through a self-supervised learning and federated learning mechanism. The edge calculation module realizes real-time data processing and decision making, the cloud collaboration module is responsible for model updating and data analysis, and the energy management system optimizes energy use and ensures long-time stable operation of the robot. The method has high precision, real-time performance and robustness, is widely applied to the fields of security and protection, logistics, medical treatment, unmanned driving and the like, and has remarkable application value and market prospect.

Description

Technical Field

[0001] The present invention relates to the technical fields of artificial intelligence and robotics, and particularly relates to an AI robot intelligent target recognition and tracking platform based on deep learning. Background Art

[0002] With the rapid development of artificial intelligence (AI) and robotics, intelligent target recognition and tracking technology has been increasingly widely applied in many fields such as security, logistics, healthcare, and autonomous driving. In the prior art, although certain target recognition and tracking capabilities have been achieved, the recognition accuracy, real-time performance, and multi-target tracking capabilities in complex environments still need to be improved. In addition, the existing systems have deficiencies in multi-sensor data fusion, energy management, and adaptive learning, which limit their performance and application scope in practical applications.

[0003] In recent years, artificial intelligence and robotics have achieved rapid development, especially in the fields of deep learning, computer vision, sensor technology, and edge computing. Through a multi-layer neural network structure, deep learning can automatically extract high-level features from data, greatly improving the performance of machine learning models in complex tasks. The development of computer vision technology enables robots to perceive the surrounding environment through visual sensors such as cameras, understand image and video content, and achieve target recognition and tracking. Multi-sensor fusion technology enhances the robot's environmental perception ability and robustness by integrating data from different types of sensors. The rise of edge computing technology enables robots to perform efficient data processing and decision-making locally, reducing the dependence on cloud computing resources and improving the real-time performance and response speed of the system.

[0004] Intelligent target recognition and tracking technology has wide applications in fields such as security monitoring, logistics management, medical assistance, and autonomous driving. Traditional target recognition and tracking systems mainly rely on rule-based algorithms and shallow learning models, which have disadvantages such as high computational complexity, low recognition accuracy, and poor real-time performance. In recent years, deep learning-based target recognition and tracking methods have gradually become a research hotspot. Among them, the YOLO (You Only Look Once) series of models have received extensive attention for their efficient real-time object detection capabilities; while the Deep SORT (Simple Online and Realtime Tracking with a Deep Association Metric) algorithm performs well in multi-target tracking tasks and can effectively solve the problems of target occlusion and identity switching.

[0005] Multi-sensor fusion technology enhances a robot's environmental perception ability and robustness by integrating data from different types of sensors. Common sensors include high-definition cameras, lidar, infrared sensors, and ultrasonic sensors, etc. Visual sensors can provide rich image information, lidar can accurately measure the three-dimensional spatial structure of the environment, infrared sensors can detect heat source information, and ultrasonic sensors are suitable for detecting obstacles at close range. However, issues such as the inconsistency of data from different sensors in time and space, as well as noise and data loss, pose challenges to multi-sensor data fusion. In existing technologies, common fusion methods include Kalman filtering, particle filtering, and deep learning fusion, etc., but it is still difficult to achieve high-precision, real-time, and robust fusion effects in complex dynamic environments.

[0006] Edge computing can achieve real-time data processing and decision-making by deploying computing resources locally on the robot, reducing dependence on the cloud and enhancing the system's response speed and stability. Especially in target recognition and tracking tasks, the computational complexity of deep learning models is relatively high, and traditional cloud computing-based methods are easily affected by network latency and bandwidth limitations, making it difficult to meet real-time requirements. Edge computing devices such as the NVIDIA Jetson series and Google Coral, etc., provide high-performance GPU or TPU accelerators, enabling complex deep learning models to run efficiently locally. However, edge computing resources are limited, and how to optimize the use of computing resources while ensuring model performance is a hot research issue currently.

[0007] Although existing technologies have made significant progress in aspects such as target recognition and tracking, multi-sensor fusion, and edge computing, there are still deficiencies in the following aspects:

[0008] Insufficient recognition accuracy and real-time performance: The recognition accuracy and real-time performance of existing deep learning-based target recognition models in complex environments still need to be improved, especially in multi-target and dynamic environments.

[0009] Complex multi-sensor data fusion: Existing multi-sensor fusion methods still face challenges in dealing with the consistency, robustness, and real-time performance of multi-source data, and it is difficult to achieve efficient fusion in complex dynamic environments.

[0010] Limited adaptive learning ability: Existing systems lack the ability to dynamically adjust and adaptively learn when dealing with different environmental and task requirements, resulting in insufficient generalization performance of the system in new environments.

[0011] Inefficient energy management: During long-term operation of the robot, energy management and optimization are insufficient, restricting the system's endurance and stability.

[0012] Lack of system integration and collaboration: Existing systems have deficiencies in multi-module integration and cloud collaboration, making it difficult to optimize and improve overall performance. Summary of the Invention

[0013] The present invention aims to provide an AI robot intelligent target recognition and tracking platform based on deep learning. By integrating key technologies such as advanced deep learning algorithms, multi-sensor fusion technology, edge computing, and cloud collaboration, it realizes efficient, accurate, and real-time target recognition and tracking, significantly enhancing the robot's autonomous decision-making and environmental perception capabilities in complex environments.

[0014] To achieve the above object, the present invention provides an AI robot intelligent target recognition and tracking platform based on deep learning, including:

[0015] Hardware layer, which is composed of a robot body, a computing unit, a sensor module, a power management module, and a communication module. The robot body includes a mobile chassis and a mechanical structure, and the computing unit is equipped with a high-performance GPU to support real-time inference of deep learning models;

[0016] Perception layer, configured with a high-definition camera, a lidar, an infrared sensor, and an ultrasonic sensor for collecting multi-source data. The high-definition camera is used to capture visual information in the environment, the lidar is used to measure the three-dimensional spatial information of the environment, the infrared sensor is used to detect heat sources, and the ultrasonic sensor is used for close-range obstacle detection;

[0017] Data preprocessing module, used to perform spatio-temporal synchronization, noise filtering, and data format conversion on the collected multi-source sensor data to ensure the spatio-temporal consistency and processing efficiency of each sensor data;

[0018] Deep learning target recognition module, based on the YOLOv7 model, can achieve high-precision and low-latency real-time target detection and identify various target objects in the environment;

[0019] Target tracking module, adopting the Deep SORT algorithm, combining the appearance features and motion information of the target to achieve continuous tracking of multiple targets and ensure stable monitoring of the target in a dynamic environment;

[0020] Multi-sensor data fusion module, using Kalman filtering and particle filtering algorithms to fuse multi-source data from vision, lidar, infrared, etc., enhancing the accuracy and robustness of environmental perception;

[0021] Adaptive learning module, through a self-supervised learning mechanism, uses the data autonomously collected by the robot for online learning, dynamically adjusts the parameters of the recognition and tracking models, and improves the adaptability and generalization ability of the system;

[0022] The edge computing module deploys a lightweight deep learning model to achieve real-time data processing and decision-making, reduce data transmission latency, and improve the system response speed;

[0023] The cloud collaboration module communicates with the cloud platform via wireless communication methods such as Wi-Fi or 5G for model updates, data backup, and large-scale data analysis, enhancing the overall performance and intelligence level of the system;

[0024] The execution control module controls the movement and behavior of the robot according to the decision results, including a motion control unit for navigation and path planning, and a behavior execution unit for performing specific operations such as obstacle avoidance and following a target;

[0025] The energy management system includes a high-capacity lithium battery, an energy scheduling unit, and a charging management unit, which are used to intelligently allocate energy requirements, optimize energy usage efficiency, and support multiple charging methods to ensure that the robot can operate continuously and stably in different environments;

[0026] The user interaction interface includes a visualization interface and a remote control unit, allowing users to monitor the robot status in real time, configure system parameters, and perform remote operations, enhancing the operability of the system and the user experience;

[0027] The data security module includes a data encryption unit and an access control unit to ensure the security and privacy of the transmitted and stored data, preventing unauthorized access and data leakage;

[0028] The environmental perception optimization unit optimizes the collaborative work of multiple sensors by dynamically adjusting the working mode and parameter settings of the sensors, enhancing the perception ability and recognition accuracy of the system in different environments;

[0029] The fault detection and self-repair module is used to monitor the operating status of each module of the system in real time, detect and handle faults in a timely manner, and ensure the high reliability and stability of the system.

[0030] Preferably, the adaptive learning module further includes:

[0031] The data acquisition unit is used to collect sensor data of the robot in different environments in real time, including visual data, lidar data, infrared data, and ultrasonic data;

[0032] The online training unit conducts online training and fine-tuning of the deep learning model based on the collected data, using self-supervised learning algorithms that do not rely on manually labeled data, enhancing the recognition and tracking ability of the model in new environments;

[0033] The model evaluation unit is used to evaluate the performance of the model after online training in real time, and determine whether the model meets the expected effect through set evaluation metrics;

[0034] The dynamic parameter adjustment unit dynamically adjusts the parameters of the target recognition and tracking model according to the model evaluation results, including the learning rate, weight decay, etc., to optimize the model performance;

[0035] The knowledge transfer unit supports transferring the knowledge learned by one robot to other robots, promotes multi-robot collaborative learning, and improves the intelligence level of the entire system;

[0036] The data storage unit is used to store the collected raw data and the trained model parameters, and supports subsequent data analysis and model optimization requirements.

[0037] Through the above structural design, the adaptive learning module can enable the robot to continuously optimize its own recognition and tracking capabilities during long-term operation, improve the intelligence level and application effect of the system, and enhance the adaptability and generalization ability of the system in different environments.

[0038] Preferably, the multi-sensor data fusion module further includes:

[0039] The data synchronization unit is used to ensure the consistency of data from different sensors in time and space, and eliminates the spatio-temporal deviation of multi-sensor data through high-precision clock synchronization and spatial calibration;

[0040] The feature extraction unit extracts representative feature information from the data of each sensor, including visual features, spatial features, thermal features, and distance features, for subsequent data fusion processing;

[0041] The fusion decision-making unit performs data fusion based on the extracted feature information, using the Kalman filter and particle filter algorithms to generate a comprehensive environmental perception result, and dynamically adjusts the fusion weight according to the data quality of different sensors;

[0042] The anomaly detection unit is used to detect and process the abnormal data from each sensor to ensure the accuracy and robustness of data fusion;

[0043] The real-time update unit supports real-time updating of the weights and fusion strategies of sensor data in a dynamic environment to adapt to environmental changes and fluctuations in sensor performance;

[0044] The data optimization unit further processes the fused data through an optimization algorithm to improve the accuracy and reliability of the data, and ensures the efficient operation of the subsequent target recognition and tracking module.

[0045] Preferably, the edge computing module further includes:

[0046] The lightweight model deployment unit is used to deploy the compressed and optimized deep learning model to adapt to the computing power and storage limitations of edge devices, and ensure the efficient operation of the model on edge devices;

[0047] A real-time processing unit responsible for instantaneously processing and analyzing sensor data, including data decoding, feature extraction, and target detection, and generating real-time decision results;

[0048] A resource management unit that dynamically allocates computing resources, optimizes the performance and energy consumption of edge devices, and ensures the stable operation of the system under high load;

[0049] A task scheduling unit that reasonably allocates computing resources according to the current system load and task priorities to improve the overall processing efficiency of the system;

[0050] A local storage unit for temporarily storing data and intermediate results during processing, reducing data transmission latency, and improving the system response speed;

[0051] A fault detection unit that monitors the operating status of the edge computing module in real time, promptly discovers and handles potential faults, and ensures the high reliability of the system;

[0052] An interface management unit that provides an efficient communication interface with other modules to ensure seamless connection between the edge computing module and the perception layer, decision-making layer, and execution layer.

[0053] Through the above design, the edge computing module can achieve efficient real-time data processing and decision-making at the robot end, reduce dependence on the cloud, improve the system response speed and overall performance, and meet the requirements of application scenarios with high real-time requirements.

[0054] Preferably, the cloud collaboration module further includes:

[0055] A model update unit for receiving and integrating model parameters after online learning uploaded from multiple robots, and performing unified update and optimization of the model through methods such as federated learning or centralized training;

[0056] A data backup unit responsible for backing up multi-source sensor data and operation logs collected by the robot to cloud storage to ensure data security and recoverability;

[0057] A large-scale data analysis unit that deeply analyzes and mines the backed-up data to extract valuable information for further optimizing the deep learning model and system performance;

[0058] A distributed computing unit that utilizes the powerful computing power of the cloud to perform complex computing tasks, such as large-scale model training, simulation testing, and data processing, to improve the intelligence level of the system;

[0059] A remote management unit that provides remote management and monitoring functions based on the cloud platform, allowing users to monitor the robot status, configure system parameters, and perform remote control in real time through the Internet;

[0060] A security management unit, including data encryption, access control, and anomaly detection functions, ensures the security of data transmission and storage between the cloud and the robot, preventing data leakage and unauthorized access;

[0061] An interface adaptation unit supports the conversion and adaptation of multiple communication protocols and data formats, ensuring that robots of different types and brands can work in coordination with the cloud, achieving cross-platform data intercommunication and function integration.

[0062] Preferably, the execution control module further includes:

[0063] A motion control unit is used to control the movement of the robot according to the decision result, including speed adjustment, direction steering, and path planning. It optimizes the motion trajectory through a real-time feedback mechanism to ensure that the robot can efficiently navigate to the target position;

[0064] A behavior execution unit is used to execute specific operation tasks, such as obstacle avoidance, following a target, grasping an object, etc. It is equipped with a multi-functional actuator and an end effector, capable of completing a variety of complex operations;

[0065] A feedback monitoring unit monitors the status information during the execution process in real time, including position, speed, action execution status, etc., and transmits the feedback information to the decision-making layer to adjust the control strategy;

[0066] A path optimization unit calculates the optimal path in real time based on the environmental perception result and the target position, avoids dynamic obstacles and adapts to environmental changes, improving the accuracy and flexibility of motion control;

[0067] A task scheduling unit manages and schedules the multi-task execution of the robot, ensuring that each task is carried out in an orderly manner according to the priority and time requirements, improving the overall working efficiency of the system;

[0068] An energy consumption management unit monitors the energy consumption of each module during the execution process, dynamically adjusts the execution strategy to optimize energy use, and extends the battery life of the robot;

[0069] A safety protection unit includes an emergency stop mechanism and a collision detection function, ensuring that the robot can respond to emergencies in a timely manner during the execution process, protecting the safety of the system and the surrounding environment;

[0070] A communication interface unit provides a communication interface with other modules and external devices, ensuring that the execution control module can receive instructions from the decision-making layer and feedback the execution status to other modules;

[0071] A fault recovery unit can quickly switch to a backup plan or take self-recovery measures when a fault occurs during the execution process, ensuring the continuous operation of the system and the successful completion of the task;

[0072] The self-learning optimization unit continuously optimizes the control strategy and execution algorithm by analyzing the data and feedback information during the execution process, improving the intelligence level and adaptability of the execution control module.

[0073] Preferably, the power management module further includes:

[0074] The energy storage unit, which uses high-capacity lithium batteries or fuel cells to provide a continuous and stable power supply, supporting the long-term autonomous operation of the robot;

[0075] The energy scheduling unit is responsible for intelligently allocating the energy requirements of each module, dynamically adjusting the energy distribution according to the real-time operating state and task priority, and optimizing the overall energy usage efficiency;

[0076] The charging management unit supports multiple charging methods, including wireless charging, wired charging, and fast charging, ensuring that the robot can quickly replenish energy in different environments;

[0077] The energy recovery unit improves the energy utilization efficiency and extends the battery life of the robot by recovering braking energy or other forms of energy recovery mechanisms;

[0078] The power monitoring unit monitors the power consumption of each module and the battery status in real time, provides accurate power information, and prevents system failures caused by insufficient power;

[0079] The backup power supply unit is configured with a backup power supply, such as a supercapacitor, to ensure that the system can continue to operate for a period of time in case of a main power failure, avoiding the impact of sudden power outages;

[0080] The energy optimization algorithm unit uses intelligent algorithms to optimize the energy management strategy, predict energy requirements, and perform energy scheduling and allocation in advance, improving the intelligence level of energy usage;

[0081] The temperature control unit monitors the temperature of the battery and power supply module, and takes heat dissipation or heating measures to ensure that the power supply system operates within a safe temperature range and extends the battery life;

[0082] The communication interface unit communicates and coordinates with other modules (such as the computing unit, execution control module) regarding the energy status, ensuring the collaborative efficiency of the overall system energy management;

[0083] The fault detection and protection unit detects abnormal situations of the power supply system in real time and takes protection measures such as power-off and alarm to prevent safety hazards such as overcharging, over-discharging, and short-circuiting of the battery.

[0084] Compared with the prior art, the AI robot intelligent target recognition and tracking platform based on deep learning of the present invention has the following remarkable advantages:

[0085] High precision and real-time performance: By adopting the YOLOv7 and Deep SORT algorithms, high-precision and low-latency object recognition and tracking are achieved, meeting the requirements of application scenarios with high real-time performance.

[0086] Multi-sensor fusion: Through a multi-sensor data fusion method that combines Kalman filtering and particle filtering, the accuracy and robustness of environmental perception are improved, ensuring the stable operation of the system in complex dynamic environments.

[0087] Adaptive learning ability: By introducing self-supervised learning and federated learning mechanisms, online model updates and multi-robot collaborative training are realized, enhancing the adaptability and generalization ability of the system.

[0088] Edge-cloud collaboration: By combining edge computing and cloud collaboration, the respective advantages are fully utilized to improve the overall performance and intelligent level of the system, ensuring the balance between real-time performance and scalability.

[0089] Intelligent energy management: Through efficient energy storage, intelligent energy scheduling, and support for multiple charging methods, efficient utilization and optimized allocation of energy are achieved, enhancing the battery life of the robot and the stability of the system.

[0090] Modular design: The system adopts a modular design, supports flexible configuration and expansion, can be customized and optimized according to the needs of different application scenarios, and has high scalability and adaptability.

[0091] High reliability and security: By integrating fault detection and protection mechanisms, the high reliability and security of the system are ensured, preventing system failures and data leaks, and guaranteeing the safe operation of the robot in complex environments.

[0092] Through the above beneficial effects, the AI robot intelligent object recognition and tracking platform of the present invention is significantly superior to the prior art in terms of performance, adaptability, and reliability, and has broad application prospects and market value. Detailed implementation manners

[0093] The detailed implementation manners of the present invention are not limited to the following specific embodiments. Any person skilled in the art can make various modifications and transformations to the present invention without departing from the spirit and principle of the present invention.

[0094] I. System architecture design

[0095] The AI robot intelligent object recognition and tracking platform of the present invention adopts a hierarchical architecture design, mainly including five parts: a hardware layer, a perception layer, a data processing layer, a decision-making layer, and an execution layer. Data transmission and interaction between layers are achieved through efficient communication interfaces.

[0096] 1. Hardware layer

[0097] The hardware layer is the foundation of the system, including the robot body, computing unit, sensor module, power management module, and communication module.

[0098] Robot body: Select a mobile robot platform with high stability and scalability, such as Husky from Clearpath, which has strong motion capabilities and load-carrying capacity, and is suitable for carrying a variety of sensors and computing devices.

[0099] Computing unit: Adopt a high-performance edge computing device, such as NVIDIA Jetson AGX Xavier, equipped with a high-performance GPU, supporting real-time inference of deep learning models and complex computing tasks.

[0100] Sensor module: Integrate high-definition cameras (such as RGB-D cameras), lidar (such as Velodyne VLP-16), infrared sensors (such as FLIR infrared cameras), and ultrasonic sensors to provide multi-source and multi-dimensional environmental data.

[0101] Power management module: Adopt a high-capacity lithium battery and an intelligent energy scheduling unit to ensure that the robot can operate stably for a long time and support multiple charging methods.

[0102] Communication module: Integrate wireless communication interfaces such as Wi-Fi, 5G, and Bluetooth to achieve efficient data transmission and remote control between the robot and the cloud platform.

[0103] 2. Perception layer

[0104] The perception layer is responsible for the acquisition and preliminary processing of environmental data, mainly including the following components:

[0105] High-definition camera: Used to capture visual information in the environment, providing high-resolution image and video data, and supporting the precise identification and positioning of targets.

[0106] Lidar: Used to obtain the three-dimensional spatial information of the environment, generating high-precision point cloud data, and assisting in the spatial positioning of targets and obstacle detection.

[0107] Infrared sensor: Used to detect heat source information, suitable for target detection and identification under night or low-light conditions.

[0108] Ultrasonic sensor: Used for close-range obstacle detection, providing real-time distance measurement information, and assisting the robot in obstacle avoidance and path planning.

[0109] 3. Data processing layer

[0110] The data processing layer includes a data preprocessing module, a multi-sensor data fusion module, and a data storage and management module.

[0111] Data preprocessing module: Responsible for spatio-temporal synchronization, noise filtering, and format conversion of the multi-source sensor data collected, ensuring the consistency and processing efficiency of the data from each sensor.

[0112] Multi-sensor data fusion module: Adopts Kalman filtering and particle filtering algorithms to fuse the data from visual, laser, infrared, and ultrasonic sensors, generating comprehensive environmental perception results, and improving the accuracy and robustness of recognition and tracking.

[0113] Data storage and management module: Constructs an efficient data storage system, supports fast retrieval and processing of large-scale data, and ensures data security and privacy at the same time.

[0114] 4. Decision-making layer

[0115] The decision-making layer is the core of the system, mainly including a deep learning object recognition module, an object tracking module, an adaptive learning module, and an edge computing module.

[0116] Deep learning object recognition module: Based on the YOLOv7 model, it realizes high-precision and low-latency real-time object detection, recognizes various target objects in the environment, and generates the position information and class labels of the targets.

[0117] Object tracking module: Adopts the Deep SORT algorithm, combines the appearance features and motion information of the objects, realizes continuous tracking of multiple objects, and solves the problems of object occlusion and identity switching.

[0118] Adaptive learning module: Through a self-supervised learning mechanism, it uses the data autonomously collected by the robot for online learning and model fine-tuning, dynamically adjusts the parameters of the recognition and tracking models, and improves the adaptability and generalization ability of the system.

[0119] Edge computing module: Deploys a lightweight deep learning model, responsible for real-time data processing and decision-making, reduces data transmission latency, improves the system response speed, and supports local fault detection and self-repair functions at the same time.

[0120] 5. Execution layer

[0121] The execution layer is responsible for controlling the movement and behavior of the robot according to the decision results, mainly including an execution control module and a feedback monitoring module.

[0122] Execution control module: According to the decision results, controls the movement and behavior of the robot, including operations such as navigation, path planning, obstacle avoidance, and target following.

[0123] Feedback monitoring module: Real-time monitors the status information during the execution process, including position, speed, and action execution, and transmits the feedback information to the decision-making layer to optimize the control strategy.

[0124] II. Key Technology Implementation

[0125] The key technologies of the present invention include deep learning object recognition and tracking algorithms, multi-sensor data fusion technology, adaptive learning mechanisms, edge computing and cloud collaboration, and energy management and optimization.

[0126] 1. Deep learning object recognition and tracking algorithms

[0127] 1.1 Object recognition algorithm

[0128] The present invention adopts the YOLOv7 (You Only Look Once version 7) model as the core algorithm for object recognition. YOLOv7 is a single-stage object detection algorithm with high efficiency in real-time performance and excellent detection accuracy. By training on a large amount of labeled data, YOLOv7 can identify multiple objects in an image and accurately locate their positions and categories. The advantage of YOLOv7 lies in its fast inference speed and end-to-end training method, which is suitable for deployment on resource-constrained edge computing devices.

[0129] 1.2 Object tracking algorithm

[0130] The object tracking adopts the Deep SORT (Simple Online and Realtime Tracking with a Deep Association Metric) algorithm. Deep SORT combines a Kalman filter and a deep learning feature extractor, enabling continuous tracking of multiple objects in an environment with high real-time requirements. Specifically, Deep SORT uses a deep learning model to extract the appearance features of the object, combines them with the Kalman filter to predict the position and motion trajectory of the object, and performs data association through the Hungarian algorithm, thus achieving stable tracking of multiple objects. This algorithm can effectively solve the problems of object occlusion and identity switching, ensuring the continuity and accuracy of tracking.

[0131] 1.3 Integration of object recognition and tracking

[0132] Integrate YOLOv7 and the Deep SORT algorithm to achieve real-time object recognition and tracking. The system first performs object detection on the input image through the YOLOv7 model, identifies various target objects in the image, and generates their bounding boxes and class labels. Subsequently, the Deep SORT algorithm tracks these objects, assigns a unique identity through appearance features and motion information, and ensures continuous monitoring of the objects in a dynamic environment. The entire process runs in real-time on the edge computing module, ensuring the efficiency and real-time performance of the system.

[0133] 2. Multi-sensor data fusion technology

[0134] Multi-sensor data fusion is the key to improving the accuracy of environmental perception and the robustness of the system. The present invention adopts a method combining Kalman filtering and particle filtering to fuse data from high-definition cameras, lidars, infrared sensors, and ultrasonic sensors.

[0135] 2.1 Data Synchronization and Calibration

[0136] There are inconsistencies in time and space among the data of different sensors, so spatio-temporal synchronization and calibration are required. Through high-precision clock synchronization technology, the timestamps of the data of each sensor are ensured to be consistent. At the same time, spatial calibration is carried out to unify the coordinate systems of each sensor and eliminate the spatial deviation of multi-sensor data.

[0137] 2.2 Feature Extraction and Preprocessing

[0138] Representative feature information is extracted from the data of each sensor. The image data provided by the high-definition camera extracts visual features through a deep learning model, the point cloud data provided by the lidar extracts spatial features through point cloud processing algorithms, the thermal imaging data provided by the infrared sensor extracts thermal features, and the distance measurement data provided by the ultrasonic sensor extracts distance features. Preprocessing includes denoising, data compression, and feature standardization to ensure the quality and consistency of the data of each sensor.

[0139] 2.3 Data Fusion Algorithm

[0140] A fusion algorithm combining Kalman filtering and particle filtering is adopted. The Kalman filter is used to process sensor data with linear and Gaussian distributions to provide fast state estimation; the particle filter is used to process sensor data with non-linear and non-Gaussian distributions to provide more flexible and accurate state estimation. Through weight adjustment and dynamic fusion strategies, the advantages of each sensor are integrated to generate a comprehensive environmental perception result, improving the accuracy and robustness of recognition and tracking.

[0141] 2.4 Anomaly Detection and Handling

[0142] During the multi-sensor data fusion process, sensor data anomalies or faults may occur. The system monitors the data quality of each sensor in real time through an anomaly detection module to identify and handle abnormal data. For example, when the data of a certain sensor is abnormal, the system can temporarily reduce the data weight of that sensor or even eliminate the abnormal data to ensure the stability and reliability of data fusion.

[0143] 3. Adaptive Learning Mechanism

[0144] To improve the adaptability of the system in different environments and tasks, the present invention introduces an adaptive learning mechanism to achieve online model update and multi-robot collaborative training through self-supervised learning and federated learning.

[0145] 3.1 Self-supervised learning

[0146] Self-supervised learning conducts online training and fine-tuning of the deep learning model by leveraging a large amount of unlabeled data autonomously collected by the robot. Specifically, during operation, the robot continuously collects environmental data and target information, and performs self-supervised training of the model by automatically generating pseudo-labels or using consistency constraints. In this way, the model can continuously optimize and adjust parameters according to the actual application scenario, improving the accuracy and generalization ability of recognition and tracking.

[0147] 3.2 Federated learning

[0148] In the scenario of multi-robot collaborative applications, federated learning can achieve cross-device model sharing and training, enhancing the overall intelligence level of the system. Each robot trains the model locally and uploads the model parameters or gradients to the cloud server for centralized aggregation and optimization. The optimized model parameters are then distributed to each robot to ensure that the update and improvement of the model are adapted to the actual application environment of all robots at the same time. In addition, federated learning can effectively protect data privacy and avoid uploading the original data to the cloud.

[0149] 3.3 Dynamic parameter adjustment

[0150] The adaptive learning module also includes a dynamic parameter adjustment unit that dynamically adjusts hyperparameters such as the learning rate and weight decay of the model according to real-time model evaluation results and environmental changes to optimize the model performance. Through real-time monitoring and feedback mechanisms, it ensures that the model always maintains the best performance under different environments and tasks.

[0151] 3.4 Knowledge transfer

[0152] The knowledge transfer unit supports transferring the knowledge learned by one robot to other robots, achieving knowledge sharing and rapid adaptation to new environments. Specifically, the experience and knowledge accumulated by the robot in a specific environment can help other robots quickly adapt to similar environments through the sharing and transfer of model parameters, improving the collaborative working ability and intelligence level of the entire system.

[0153] 4. Edge computing and cloud collaboration

[0154] The combination of edge computing and cloud collaboration is the key to achieving efficient and real-time target recognition and tracking in the present invention.

[0155] 4.1 Edge computing module

[0156] The edge computing module is deployed locally on the robot and is responsible for real-time data processing and decision-making. The specific functions include:

[0157] Real-time data processing: By deploying lightweight deep learning models, immediate processing of sensor data and target recognition are achieved.

[0158] Decision generation: Based on the processing results, decision instructions for the robot's movement and behavior are generated, such as navigation path planning, target following, and obstacle avoidance operations.

[0159] Fault detection and self-repair: The operating status of each module of the system is monitored in real time. When abnormal situations are detected, a self-repair mechanism is initiated, such as restarting the module, switching to the backup system, etc., to ensure the high reliability and stability of the system.

[0160] 4.2 Cloud collaboration module

[0161] The cloud collaboration module conducts data transmission and collaborative work with the cloud platform through a wireless communication interface. Specific functions include:

[0162] Model update and optimization: The cloud platform is responsible for summarizing and optimizing the online learning models from each robot. Through federated learning or centralized training, optimized model parameters are generated and distributed to each robot to ensure the continuous optimization and improvement of the model.

[0163] Data backup and management: The multi-source sensor data and operation logs collected by the robot are regularly uploaded to the cloud for backup and storage to ensure the security and recoverability of the data.

[0164] Large-scale data analysis: The cloud platform utilizes powerful computing resources to deeply analyze and mine the backed-up data, extract valuable information, and use it to further optimize the deep learning model and system performance.

[0165] Remote management and monitoring: The cloud provides a web-based management interface that allows users to monitor the robot status, configure system parameters, view operation logs, and perform remote control operations in real time via the Internet, improving the operability of the system and the user experience.

[0166] 4.3 Collaboration between the edge and the cloud

[0167] The collaboration between edge computing and the cloud can give full play to their respective advantages and improve the overall performance and intelligent level of the system. The edge computing module is responsible for real-time and low-latency data processing and decision-making to ensure the real-time performance and response speed of the system; while the cloud collaboration module is responsible for large-scale data analysis, model optimization, and remote management to enhance the intelligence and scalability of the system. Through an efficient data transmission and collaboration mechanism, the edge and the cloud complement each other and jointly support the efficient operation and intelligent decision-making of the robot in complex environments.

[0168] 5. Energy management and optimization

[0169] During the long-term operation of the robot, energy management and optimization are crucial. Through the intelligent energy management system, this invention realizes the efficient utilization and optimized allocation of energy, enhancing the robot's endurance and the stability of the system.

[0170] 5.1 High-Efficiency Energy Storage

[0171] Adopt high-capacity lithium batteries or fuel cells to provide continuous and stable power supply. Lithium batteries have the advantages of high energy density and long lifespan, being suitable for the long-term autonomous operation of robots. Fuel cells generate electric energy through chemical reactions, with advantages such as high energy density and fast charging speed, and are applicable to application scenarios with high requirements for endurance time.

[0172] 5.2 Intelligent Energy Scheduling

[0173] The energy scheduling unit, through intelligent algorithms, monitors the energy requirements and consumption of each module in real time, dynamically allocates energy, and optimizes the overall energy usage efficiency. According to the operating state and task requirements of the system, rationally allocate the energy of the computing unit, sensor module, and execution control module to ensure that the system can operate efficiently under both high-load and low-load conditions.

[0174] 5.3 Support for Multiple Charging Methods

[0175] The charging management unit supports multiple charging methods, including wireless charging, wired charging, and fast charging, ensuring that the robot can quickly replenish energy in different environments. For example, deploy wireless charging devices at fixed workstations, and the robot can automatically return to the charging station for wireless charging during task breaks; at the same time, support wired charging and fast charging modes to meet the charging requirements in different application scenarios.

[0176] 5.4 Energy Recovery Mechanism

[0177] The energy recovery unit improves the energy utilization efficiency and extends the robot's endurance time through the recovery of braking energy during the robot's movement or other forms of energy recovery mechanisms. For example, when the robot brakes or decelerates, the kinetic energy is converted into electric energy through the regenerative braking system and stored back in the battery to reduce energy waste.

[0178] 5.5 Power Monitoring and Protection

[0179] The power monitoring unit monitors the power consumption of each module and the battery status in real time, provides accurate power information, and prevents system failures caused by insufficient power. The system also includes a backup power supply unit, such as a supercapacitor, which provides short-term power support when the main power supply fails, ensuring that the system can be safely shut down or switched to the backup system to avoid the impact of sudden power outages.

[0180] 5.6 Energy Optimization Algorithm

[0181] The energy optimization algorithm unit uses intelligent algorithms such as machine learning and optimization theory to predict energy demand, perform energy scheduling and allocation in advance, and optimize energy usage strategies. For example, by analyzing historical energy consumption data and task requirements, it predicts the energy demand in the future period, adjusts the energy allocation in advance, and ensures that the system can operate efficiently under different tasks.

[0182] 5.7 Temperature Control and Management

[0183] The temperature control unit monitors the temperatures of the battery and power supply module, takes heat dissipation or heating measures to ensure that the power supply system operates within a safe temperature range and extends the battery life. For example, in a high-temperature environment, it activates the active heat dissipation system to lower the battery temperature; in a low-temperature environment, it activates the heating system to keep the battery at the optimal working temperature.

[0184] 5.8 Fault Detection and Protection

[0185] The fault detection and protection unit detects abnormal conditions of the power supply system in real time, such as overcharging, over-discharging, short circuit, etc. By taking protection measures (such as power-off, alarm, etc.), it prevents battery damage and system failures and ensures the safe operation of the robot.

[0186] 5.9 Energy Consumption Optimization

[0187] The system reduces energy consumption by optimizing the use of computing resources and the working modes of sensors. For example, in a low-load situation, it reduces the working frequency of the computing unit, reduces unnecessary sensor data acquisition, and extends the battery life; in a high-load situation, it dynamically adjusts the data acquisition frequency and resolution of the sensors to balance system performance and energy consumption.

[0188] 5.10 User Configuration and Control

[0189] The energy management system provides a user interaction interface that allows users to configure energy management strategies according to actual needs, such as setting energy allocation priorities, adjusting charging strategies, and monitoring energy consumption. Through an intuitive visualization interface, users can view the energy usage situation in real time, make manual adjustments and optimizations, and improve the controllability and flexibility of the system.

[0190] III. System Implementation Steps

[0191] The implementation of the AI robot intelligent target recognition and tracking platform of the present invention includes steps such as hardware assembly, sensor configuration, software development and integration, deep learning model training and optimization, multi-sensor data fusion setting, adaptive learning mechanism configuration, edge and cloud collaborative architecture construction, and system testing and optimization.

[0192] 1. Hardware Assembly and Configuration

[0193] First, select a mobile robot platform with high stability and scalability (such as Husky from Clearpath) for the assembly and configuration of the robot body. Install a high-performance edge computing device (such as NVIDIA Jetson AGX Xavier) on the robot body to ensure its electrical connection and mechanical fixation with other robot modules. Subsequently, install and connect high-definition cameras, lidars, infrared sensors, and ultrasonic sensors to ensure that the viewing angles and coverage ranges of the sensors can meet the requirements of target recognition and tracking.

[0194] 2. Sensor Calibration and Synchronization

[0195] Perform spatio-temporal calibration on all sensors to ensure the consistency of data from different sensors in time and space. Adopt high-precision clock synchronization technology to unify the data timestamps of each sensor; use spatial calibration tools to adjust the installation positions and angles of each sensor to ensure the spatial alignment of the data. After calibration, conduct tests on sensor data to verify the accuracy and consistency of the data.

[0196] 3. Software Development and System Integration

[0197] Develop an operating system and middleware based on ROS (Robot Operating System) to achieve communication and collaborative work among system modules. The deep learning model is developed and trained using the PyTorch or TensorFlow framework. Write the code for the target recognition module and the target tracking module to implement the integration and optimization of the YOLOv7 and Deep SORT algorithms. Develop the algorithm for the multi-sensor data fusion module to achieve the fusion processing of Kalman filtering and particle filtering. After completing the software development of all modules, conduct system integration tests to ensure the efficient collaboration and stable operation among modules.

[0198] 4. Deep Learning Model Training and Optimization

[0199] Collect a large amount of labeled data covering different scenarios and targets to build a diverse dataset. Adopt data augmentation techniques, such as image rotation, scaling, cropping, and noise injection, to improve the robustness and generalization ability of the model. Use the YOLOv7 model for target recognition training, optimize the model structure and parameters, and improve the detection accuracy and speed. After training, export the optimized model and deploy it to the edge computing device for real-time inference testing.

[0200] 5. Multi-sensor Data Fusion Settings

[0201] Configure a multi-sensor data fusion module, set the parameters of Kalman filtering and particle filtering to ensure the accuracy and robustness of data fusion. Write data synchronization and calibration algorithms to achieve spatio-temporal consistency of different sensor data. Through experimental tests, adjust the weights and parameters of the fusion algorithm to optimize the data fusion effect and ensure high precision and stability of environmental perception.

[0202] 6. Configuration of Adaptive Learning Mechanism

[0203] Implement an adaptive learning module, including a data acquisition unit, an online training unit, a model evaluation unit, and a dynamic parameter adjustment unit. Write self-supervised learning algorithms to achieve online training and model fine-tuning based on data autonomously collected by the robot. Configure a federated learning framework to achieve collaborative training and model sharing among multiple robots. Test the performance of the adaptive learning mechanism in different environments to ensure continuous optimization and improved adaptability of the model.

[0204] 7. Construction of Edge-Cloud Collaborative Architecture

[0205] Build a cloud collaborative platform, configure a model update unit, a data backup unit, and a large-scale data analysis unit. Implement the communication interface between the edge computing module and the cloud collaborative module to ensure efficient data transmission and timely model updates. Deploy cloud collaborative algorithms to achieve centralized optimization and distribution of models. Test the stability and efficiency of the edge-cloud collaborative architecture to ensure the improvement of the overall system performance and the optimization of the intelligent level.

[0206] 8. System Testing and Optimization

[0207] Conduct comprehensive system testing, including unit testing, integration testing, and scenario testing. Through target recognition and tracking tests in different environments (such as indoor, outdoor, multi-obstacle environments, etc.), evaluate the accuracy, real-time performance, and robustness of the system. Conduct multi-target tracking tests to verify the system's recognition and tracking capabilities when multiple targets appear simultaneously. Test the adaptability of the system in a dynamic environment and evaluate its performance under different lighting, weather, and other conditions. According to the test results, optimize the algorithms and parameters of each module of the system to improve the overall performance and stability. IV. Specific Embodiments

[0209] Embodiment 1: Security Patrol Robot

[0210] Deploy the AI robot intelligent target recognition and tracking platform of the present invention in a large commercial complex, and the robot is mainly used for intelligent patrol and abnormal behavior detection.

[0211] System Configuration

[0212] Robot platform: Clearpath Husky mobile robot.

[0213] Computing Unit: NVIDIA Jetson AGX Xavier.

[0214] Sensors:

[0215] High-definition RGB-D camera for collecting environmental visual information.

[0216] Velodyne VLP-16 lidar for obtaining three-dimensional space information.

[0217] FLIR infrared camera for heat source detection, suitable for night monitoring.

[0218] Ultrasonic sensor for close-range obstacle detection and avoidance.

[0219] Power Management: High-capacity lithium battery to support long-term patrol tasks.

[0220] Communication Module: Integrated Wi-Fi and 5G interfaces to achieve efficient data transmission and remote control with the cloud platform.

[0221] System Workflow

[0222] Environmental Data Collection: After the robot starts, the camera, lidar, infrared sensor, and ultrasonic sensor work simultaneously to collect environmental data in real time.

[0223] Data Preprocessing: The data preprocessing module performs spatio-temporal synchronization, noise filtering, and format conversion on the collected multi-source data to ensure data consistency.

[0224] Target Recognition: The YOLOv7 model is used to perform real-time target detection on the image data collected by the camera to identify people, vehicles, and other suspicious objects.

[0225] Target Tracking: The Deep SORT algorithm tracks the detected targets, assigns unique identity identifiers, and ensures continuous tracking of multiple targets.

[0226] Data Fusion: Kalman filter and particle filter algorithms fuse data from lidar and infrared sensors to generate comprehensive environmental perception results.

[0227] Decision Making and Control: The edge computing module generates motion and behavior decision instructions based on the fused perception results and target tracking information, such as navigation path planning, target following, and obstacle avoidance operations.

[0228] Execution of Operations: The execution control module controls the movement of the robot according to the decision instructions to complete patrol and target following tasks.

[0229] Exception handling: Once abnormal behavior is detected, such as the intrusion of a suspicious person, the system immediately sends an alarm message to the security center through the communication module and starts autonomous tracking and locking of the target.

[0230] Energy management: The energy management system monitors the battery status in real time, intelligently schedules energy usage, and ensures the long-term stable operation of the robot. The robot automatically returns to the charging station for wireless charging during the gap of the patrol task.

[0231] System advantages

[0232] High-precision monitoring: Through the YOLOv7 and Deep SORT algorithms, high-precision and continuous tracking of multiple targets is achieved, ensuring the timely detection and response to abnormal behavior.

[0233] Strong robustness: Multi-sensor data fusion improves the system's perception ability and robustness in complex environments, ensuring the stable operation of the system under different lighting and weather conditions.

[0234] High real-time performance: The edge computing module enables real-time data processing and decision-making, ensuring that the system can respond promptly to the dynamically changing environment and target behavior.

[0235] Energy efficiency: The intelligent energy management system optimizes energy usage, extends the robot's battery life, and ensures long-term stable operation.

[0236] Embodiment 2: Automated warehouse management robot

[0237] Deploy the AI robot intelligent target recognition and tracking platform of the present invention in a large logistics warehouse. The robot is mainly used for automatic identification, handling, and sorting of items.

[0238] System configuration

[0239] Robot platform: A custom mobile robot developed independently, with high load capacity and flexible motion control.

[0240] Computing unit: NVIDIA Jetson Xavier NX, with both high performance and low power consumption.

[0241] Sensors:

[0242] High-resolution RGB-D camera, used for item identification and positioning.

[0243] Velodyne VLP-16 lidar, used for 3D modeling of the spatial structure in the warehouse.

[0244] Infrared sensor, used for item detection in nighttime or low-light environments.

[0245] Ultrasonic sensor, used for close-range obstacle detection and avoidance.

[0246] Power Management: High-capacity lithium battery, supporting all-weather automated operation.

[0247] Communication Module: 5G interface, enabling real-time data synchronization and remote control with the cloud warehouse management system.

[0248] System Workflow

[0249] Item Identification: The robot automatically patrols the warehouse, the camera captures item images, and various items and their location information are identified through the YOLOv7 model.

[0250] Item Positioning: The lidar scans the warehouse environment to generate 3D point cloud data, which is fused with the camera data to accurately locate the items in the warehouse.

[0251] Target Tracking: The Deep SORT algorithm tracks the moving items to ensure continuous monitoring and positioning during the handling process.

[0252] Path Planning and Obstacle Avoidance: Based on the comprehensive environmental perception results, the edge computing module conducts path planning to plan the optimal handling path, and at the same time uses ultrasonic sensors and lidar for real-time obstacle avoidance.

[0253] Item Handling and Sorting: The execution control module controls the robot to move to the item location, uses the robotic arm to grab the item and transports it to the designated area according to the preset sorting rules.

[0254] Data Synchronization and Management: Through the cloud collaboration module, the robot real-time uploads item handling data and warehouse environment information, and the cloud warehouse management system performs data backup and real-time inventory management.

[0255] Energy Management: The energy management system monitors the battery status in real-time, intelligently schedules energy usage to ensure the long-term stable operation of the robot. After completing the handling task, the robot automatically returns to the charging station for wireless charging.

[0256] System Advantages

[0257] High Efficiency and Automation: Through deep learning object recognition and tracking, automatic identification, handling, and sorting of items are achieved, greatly improving the efficiency and accuracy of warehouse management.

[0258] Flexible Adaptability: The multi-sensor fusion technology enables the robot to adapt to different warehouse layouts and environmental changes, ensuring the flexibility and robustness of the system.

[0259] Real-time Data Management: The cloud collaboration module realizes real-time data synchronization and remote management, optimizing the inventory management and logistics processes of the warehouse.

[0260] Energy Optimization: The intelligent energy management system optimizes energy usage, extends the robot's battery life, and ensures stable long-term operation.

[0261] Example 3: Medical Assistant and Monitoring Robot

[0262] Deploy the AI robot intelligent target recognition and tracking platform of the present invention within a certain hospital. The robot is mainly used for the automatic handling of medical supplies and the monitoring of patients.

[0263] System Configuration

[0264] Robot Platform: A custom mobile robot developed independently, with high-precision positioning and flexible motion control.

[0265] Computing Unit: NVIDIA Jetson TX2, providing sufficient computing power and low-power consumption characteristics.

[0266] Sensors:

[0267] High-resolution RGB-D camera, used for monitoring patient activities and identifying supplies.

[0268] Velodyne VLP-16 lidar, used for 3D modeling of the hospital environment and obstacle detection.

[0269] FLIR infrared camera, used for monitoring the health status of patients and night monitoring.

[0270] Ultrasonic sensor, used for close-range obstacle detection and obstacle avoidance.

[0271] Power Management: High-capacity lithium battery, supporting round-the-clock operation.

[0272] Communication Module: Wi-Fi interface, enabling real-time data synchronization and remote control with the hospital management system.

[0273] System Workflow

[0274] Patient Monitoring: The robot patrols within the hospital, monitors the activities and health status of patients through the RGB-D camera and infrared sensors. Identifies patients and their actions through the YOLOv7 model, real-time detects abnormal behaviors (such as falls, apnea, etc.), and immediately alarms.

[0275] Medical Supply Handling: The robot automatically identifies the location of medical supplies, precisely locates the supplies through a deep learning model, and tracks the location of the supplies during handling through the Deep SORT algorithm. The robot automatically transports the supplies to the designated location as needed, ensuring the timely supply of medical supplies.

[0276] Path Planning and Obstacle Avoidance: Based on data from lidar and ultrasonic sensors, the edge computing module performs real-time path planning and obstacle avoidance to ensure that the robot can move safely and effectively in the hospital environment.

[0277] Data Synchronization and Management: Through the cloud collaboration module, the robot uploads patient monitoring data and material handling data in real time, and the hospital management system conducts data analysis and decision support to optimize the medical service process.

[0278] Energy Management: The energy management system monitors the battery status in real time, intelligently schedules energy usage, and ensures the long-term stable operation of the robot. After completing the task, the robot automatically returns to the charging station for wireless charging.

[0279] System Advantages

[0280] Improve Medical Efficiency: By automatically transporting medical supplies and monitoring patients, the workload of medical staff is reduced, and the efficiency and quality of medical services are improved.

[0281] Real-time Health Monitoring: Through high-definition cameras and infrared sensors, the health status of patients is monitored in real time, abnormal situations are detected and responded to in a timely manner, and the safety of patients is ensured.

[0282] High Reliability: Multi-sensor fusion and edge computing achieve high reliability and robustness of the system, ensuring the stable operation of the robot in a complex hospital environment.

[0283] Intelligent Energy Management: Optimize energy usage, extend the battery life of the robot, and ensure that the robot can perform tasks stably for a long time.

[0284] Example 4: Environmental Perception of Unmanned Vehicles

[0285] In a certain intelligent transportation system, an unmanned vehicle integrated with the AI robot intelligent target recognition and tracking platform of the present invention realizes high-precision perception of the road environment through high-definition cameras, lidar, and infrared sensors.

[0286] System Configuration

[0287] Vehicle Platform: An independently developed unmanned vehicle equipped with a high-performance computing unit and multiple sensors.

[0288] Computing Unit: NVIDIA Drive AGX Xavier, designed specifically for autonomous driving, provides powerful computing capabilities.

[0289] Sensors:

[0290] High-definition RGB camera, used for traffic sign and pedestrian recognition.

[0291] Velodyne VLP-32 lidar for high-precision three-dimensional environment perception.

[0292] FLIR infrared camera for target detection in night and low-light conditions.

[0293] Ultrasonic sensors for close-range obstacle detection and vehicle distance control.

[0294] Power management: High-capacity battery pack to support long-duration driverless operation.

[0295] Communication module: 5G interface to enable real-time data exchange with traffic management centers and other vehicles.

[0296] System workflow

[0297] Environmental perception: After the driverless vehicle starts, it collects road environment data in real time through high-definition cameras, lidar, infrared sensors, and ultrasonic sensors. It uses the YOLOv7 model to identify pedestrians, vehicles, and traffic signs on the road and the Deep SORT algorithm to track dynamic targets.

[0298] Path planning and decision-making: The edge computing module performs path planning and driving decisions based on the environmental perception results, such as lane changes, acceleration, deceleration, and obstacle avoidance operations. It uses Kalman filtering and particle filtering algorithms to fuse data from different sensors to generate a comprehensive environmental perception result, improving the accuracy and robustness of the decision-making.

[0299] Execution control: The execution control module controls the steering, acceleration, and braking of the vehicle according to the decision instructions to achieve precise driving control.

[0300] Real-time data synchronization: Through the cloud collaboration module, the vehicle uploads real-time perception data and driving status to the cloud, and the traffic management center conducts data analysis and traffic flow optimization. At the same time, the cloud platform can send down the latest traffic information and model updates to improve the intelligence level of the system.

[0301] Energy management: The energy management system monitors the vehicle battery status in real time, intelligently schedules energy usage to ensure long-term stable operation of the vehicle. The vehicle automatically returns to the charging station for fast charging after completing a long journey.

[0302] System advantages

[0303] High-precision environmental perception: Through multi-sensor fusion and deep learning algorithms, it realizes high-precision perception of the road environment, improving the safety and reliability of driverless vehicles.

[0304] Real-time decision-making and response: The edge computing module enables real-time data processing and decision-making to ensure that driverless vehicles can quickly respond to dynamically changing road conditions.

[0305] Strong robustness: Multi-sensor data fusion enhances the robustness of the system in complex traffic environments, ensuring the stable operation of driverless vehicles under different lighting and weather conditions.

[0306] Intelligent energy management: Optimize energy use, extend the vehicle's battery life, and ensure that driverless vehicles can perform tasks stably for a long time.

[0307] The present invention provides an AI robot intelligent target recognition and tracking platform based on deep learning. By integrating key technologies such as advanced deep learning algorithms, multi-sensor fusion technology, edge computing, and cloud collaboration, it solves the deficiencies of existing technologies in aspects such as target recognition and tracking, multi-sensor data fusion, adaptive learning, and energy management. This platform has high precision, real-time performance, and robustness, and is applicable to multiple application fields such as security, logistics, healthcare, and driverless driving, with broad market prospects and application value. In the future, with the continuous progress and optimization of technology, the platform will further improve its intelligence level and promote the development and innovation of related fields.

Claims

1. An AI robot intelligent target recognition and tracking platform based on deep learning, characterized in that: include: The hardware layer consists of a robot body, a computing unit, a sensor module, a power management module, and a communication module, wherein the robot body includes a mobile chassis and a mechanical structure, and the computing unit is equipped with a high-performance GPU to support real-time reasoning of deep learning models; The perception layer is equipped with high-definition cameras, laser radars, infrared sensors and ultrasonic sensors for collecting multi-source data. The high-definition cameras are used to capture visual information in the environment, the laser radars are used to measure the three-dimensional spatial information of the environment, the infrared sensors are used to detect heat sources, and the ultrasonic sensors are used to detect obstacles at close range. The data preprocessing module is used to perform spatiotemporal synchronization, noise filtering, and data format conversion on the collected multi-source sensor data to ensure the spatiotemporal consistency and processing efficiency of each sensor data; The deep learning target recognition module, based on the YOLOv7 model, can achieve high-precision, low-latency real-time target detection and identify various target objects in the environment; The target tracking module uses the Deep SORT algorithm and combines the target's appearance features and motion information to achieve continuous tracking of multiple targets and ensure stable monitoring of targets in dynamic environments; The multi-sensor data fusion module uses Kalman filtering and particle filtering algorithms to fuse data from multiple sources such as vision, laser, and infrared to enhance the accuracy and robustness of environmental perception; The adaptive learning module uses the self-supervised learning mechanism to conduct online learning using the data collected autonomously by the robot, dynamically adjust the parameters of the recognition and tracking models, and improve the adaptability and generalization ability of the system; Edge computing module deploys lightweight deep learning models to achieve real-time data processing and decision-making, reduce data transmission delays, and improve system response speed; The cloud collaboration module uses wireless communications such as Wi-Fi or 5G to communicate with the cloud platform for model updates, data backup, and large-scale data analysis, improving the overall performance and intelligence level of the system; The execution control module controls the robot's motion and behavior based on the decision results, including a motion control unit for navigation and path planning, and a behavior execution unit for performing specific operations, such as obstacle avoidance and target following; Energy management system, including high-capacity lithium batteries, energy dispatch unit and charging management unit, is used to intelligently distribute energy demand, optimize energy efficiency and support multiple charging methods to ensure that the robot can continue to operate stably in different environments; User interaction interface, including visual interface and remote control unit, allows users to monitor robot status in real time, configure system parameters and perform remote operations, improving system operability and user experience; Data security module, including data encryption unit and access control unit, ensures the security and privacy of transmitted and stored data and prevents unauthorized access and data leakage; Environmental perception optimization unit, which optimizes the collaborative work of multiple sensors by dynamically adjusting the working mode and parameter settings of sensors, and improves the system's perception ability and recognition accuracy in different environments; The fault detection and self-repair module is used to monitor the operating status of each module of the system in real time, detect and handle faults in a timely manner, and ensure the high reliability and stability of the system.

2. The AI ​​robot intelligent target recognition and tracking platform based on deep learning according to claim 1 is characterized in that: The adaptive learning module further comprises: Data acquisition unit, used to collect sensor data of the robot in different environments in real time, including visual data, lidar data, infrared data and ultrasonic data; Online training unit, which trains and fine-tunes deep learning models online based on collected data, using self-supervised learning algorithms that do not rely on manually labeled data to improve the model's recognition and tracking capabilities in new environments; The model evaluation unit is used to evaluate the performance of the model after online training in real time and determine whether the model has achieved the expected effect through the set evaluation indicators; Dynamic parameter adjustment unit, which dynamically adjusts the parameters of the target recognition and tracking model, including learning rate, weight decay, etc., according to the model evaluation results to optimize the model performance; The knowledge transfer unit supports the transfer of knowledge learned by one robot to other robots, promotes multi-robot collaborative learning, and improves the intelligence level of the entire system; The data storage unit is used to store the collected raw data and trained model parameters to support subsequent data analysis and model optimization needs. Through the above structural design, the adaptive learning module can enable the robot to continuously optimize its recognition and tracking capabilities during long-term operation, improve the intelligence level and application effect of the system, and enhance the adaptability and generalization ability of the system in different environments.

3. The AI ​​robot intelligent target recognition and tracking platform based on deep learning according to claim 1 is characterized in that: The multi-sensor data fusion module further comprises: Data synchronization unit, used to ensure the consistency of data from different sensors in time and space, and eliminate the temporal and spatial deviation of multi-sensor data through high-precision clock synchronization and spatial calibration; The feature extraction unit extracts representative feature information from each sensor data, including visual features, spatial features, thermal features, and distance features, to facilitate subsequent data fusion processing; The fusion decision unit uses Kalman filtering and particle filtering algorithms to perform data fusion based on the extracted feature information, generates comprehensive environmental perception results, and dynamically adjusts the fusion weight according to the data quality of different sensors; Anomaly detection unit, used to detect and process abnormal data from each sensor to ensure the accuracy and robustness of data fusion; Real-time update unit, which supports real-time update of sensor data weights and fusion strategies in dynamic environments to adapt to environmental changes and fluctuations in sensor performance; The data optimization unit further processes the fused data through optimization algorithms to improve the accuracy and reliability of the data and ensure the efficient operation of subsequent target recognition and tracking modules.

4. The AI ​​robot intelligent target recognition and tracking platform based on deep learning according to claim 1 is characterized in that: The edge computing module further includes: A lightweight model deployment unit for deploying compressed and optimized deep learning models to fit the computing power and storage limitations of edge devices, ensuring that the models run efficiently on edge devices; The real-time processing unit is responsible for real-time processing and analysis of sensor data, including data decoding, feature extraction and target detection, and generates real-time decision results; Resource management unit, dynamically allocates computing resources, optimizes the performance and energy consumption of edge devices, and ensures stable operation of the system under high load; The task scheduling unit allocates computing resources reasonably according to the current system load and task priority to improve the overall processing efficiency of the system; Local storage unit, used to temporarily store data and intermediate results in processing, reduce data transmission delays, and improve system response speed; Fault detection unit, which monitors the operating status of the edge computing module in real time, detects and handles potential faults in a timely manner, and ensures high reliability of the system; The interface management unit provides an efficient communication interface with other modules to ensure seamless connection between the edge computing module and the perception layer, decision layer, and execution layer. Through the above design, the edge computing module can realize efficient real-time data processing and decision-making on the robot side, reduce dependence on the cloud, improve the response speed and overall performance of the system, and meet the needs of application scenarios with high real-time requirements.

5. The AI ​​robot intelligent target recognition and tracking platform based on deep learning according to claim 1 is characterized in that: The cloud collaboration module further includes: The model update unit is used to receive and integrate the model parameters uploaded by multiple robots after online learning, and to uniformly update and optimize the model through federated learning or centralized training; The data backup unit is responsible for backing up the multi-source sensor data and operation logs collected by the robot to cloud storage to ensure data security and recoverability; Large-scale data analysis unit, which conducts in-depth analysis and mining of the backed-up data, extracting valuable information for further optimizing deep learning models and system performance; Distributed computing units use the powerful computing power of the cloud to perform complex computing tasks, such as large-scale model training, simulation testing, and data processing, to improve the intelligence level of the system; Remote management unit, which provides remote management and monitoring functions based on the cloud platform, allowing users to monitor the robot status in real time, configure system parameters and perform remote control via the Internet; Security management unit, including data encryption, access control and anomaly detection functions, to ensure the security of data transmission and storage between the cloud and the robot, and prevent data leakage and unauthorized access; The interface adapter unit supports the conversion and adaptation of multiple communication protocols and data formats, ensuring that robots of different types and brands can work together with the cloud and achieve cross-platform data interoperability and functional integration.

6. The AI ​​robot intelligent target recognition and tracking platform based on deep learning according to claim 1 is characterized in that: The execution control module further comprises: The motion control unit is used to control the movement of the robot according to the decision results, including speed regulation, direction steering and path planning. It optimizes the motion trajectory through a real-time feedback mechanism to ensure that the robot can navigate to the target position efficiently. The behavior execution unit is used to perform specific operation tasks, such as obstacle avoidance, target following, object grabbing, etc. It is equipped with multifunctional actuators and end-effectors and can complete a variety of complex operations; Feedback monitoring unit monitors the status information of the execution process in real time, including position, speed, action execution status, etc., and transmits feedback information to the decision-making layer to adjust the control strategy; The path optimization unit calculates the optimal path in real time based on the environmental perception results and target position, avoids dynamic obstacles and adapts to environmental changes, and improves the accuracy and flexibility of motion control; Task scheduling unit, which manages and schedules the robot's multi-task execution, ensuring that each task is carried out in order according to priority and time requirements, thus improving the overall work efficiency of the system; Energy management unit, which monitors the energy consumption of each module during execution and dynamically adjusts the execution strategy to optimize energy use and extend the robot's endurance; Safety protection unit, including emergency stop mechanism and collision detection function, ensures that the robot can respond to emergencies in time during execution and ensures the safety of the system and the surrounding environment; The communication interface unit provides communication interfaces with other modules and external devices, ensuring that the execution control module can receive instructions from the decision-making layer and feedback the execution status to other modules; Fault recovery unit, which can quickly switch to backup solutions or take self-recovery measures when a fault occurs during execution, to ensure the continuous operation of the system and the smooth completion of the task; The self-learning optimization unit continuously optimizes the control strategy and execution algorithm by analyzing the data and feedback information during the execution process, thereby improving the intelligence level and adaptability of the execution control module.

7. The AI ​​robot intelligent target recognition and tracking platform based on deep learning according to claim 1 is characterized in that: The power management module further comprises: Energy storage unit, using high-capacity lithium batteries or fuel cells, provides a continuous and stable power supply to support the robot's long-term autonomous operation; The energy scheduling unit is responsible for intelligently allocating the energy requirements of each module, dynamically adjusting energy allocation according to real-time operating status and task priority, and optimizing overall energy efficiency; The charging management unit supports multiple charging methods, including wireless charging, wired charging and fast charging, ensuring that the robot can quickly replenish energy in different environments; Energy recovery unit, which improves energy efficiency and extends the robot's endurance by recovering braking energy or other forms of energy recovery mechanisms; The power monitoring unit monitors the power consumption and battery status of each module in real time, provides accurate power information, and prevents system failures due to insufficient power; Backup power supply unit, equipped with backup power supply, such as super capacitor, to ensure that the system can continue to operate for a period of time when the main power fails, avoiding the impact of sudden power outage; Energy optimization algorithm unit, which uses intelligent algorithms to optimize energy management strategies, predict energy demand, schedule and allocate energy in advance, and improve the level of intelligence in energy use; Temperature control unit, which monitors the temperature of the battery and power module and takes cooling or heating measures to ensure that the power system operates within a safe temperature range and prolong battery life; The communication interface unit communicates and coordinates the energy status with other modules (such as the computing unit and the execution control module) to ensure the coordinated and efficient energy management of the system as a whole; The fault detection and protection unit detects abnormal conditions of the power supply system in real time and takes protective measures such as power off and alarm to prevent safety hazards such as battery overcharging, over-discharging and short circuit. Through the above design, the power management module can effectively manage and optimize the robot's energy usage, extend the robot's battery life, ensure long-term stable operation of the system, improve overall energy efficiency, and meet the robot's continuous working needs in complex environments.