Complex environment-oriented disinfection robot dynamic path planning embedded system

Abstract

The invention discloses a disinfection robot dynamic path planning embedded system oriented to a complex environment, and relates to the technical field of path planning, a data acquisition module is used for acquiring environmental data in real time, a dynamic map establishment module is used for establishing a dynamic map by using a real-time positioning and mapping technology, and the dynamic path planning module is used for planning the dynamic path of a disinfection robot. The intelligent planning module executes optimal global path planning according to the constructed dynamic map and calculates an optimal global path from a starting point to a target point, and the obstacle avoidance module avoids sudden dynamic obstacles and dynamically adjusts the advancing route of the robot through a local obstacle avoidance algorithm on the basis of keeping an original path. And the intelligent adjustment module adjusts the moving speed and direction of the robot in real time according to the optimal global path and the dynamic adjustment result. Through combination of global path planning and intelligent regulation and control, the robot not only can plan an initial path, but also can adjust the path in real time according to a dynamic environment in an execution process, and the adaptive capacity of the robot in the dynamic environment is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and in particular to a dynamic path planning embedded system for a disinfection robot in complex environments. Background Art

[0002] With the development of intelligent technology, the application of robots in medical, industrial, public places and other fields has gradually increased. Especially in epidemic prevention and control, environmental cleaning and other aspects, disinfection robots, as a highly efficient automated equipment, have gradually gained favor. Disinfection robots need to work in complex environments, including areas with dense traffic, narrow corridors, furniture of different shapes, etc., which require robots to have strong dynamic path planning capabilities to ensure the smooth completion of the task.

[0003] The existing technology has the following defects: Traditional path planning algorithms usually rely on static maps. When encountering dynamic obstacles (such as pedestrians, other equipment or sudden obstacles), path planning is difficult to adjust in real time. This makes the disinfection robot have certain limitations when operating in complex and dynamic environments. For example, when the robot is operating in an environment full of obstacles, its path planning may quickly become invalid, causing the robot to stagnate or deviate from the target. In addition, traditional path planning methods often ignore real-time response to environmental changes, which makes the robot lack flexibility and adaptability when encountering unknown or unpredictable environmental changes.

[0004] Based on this, the present invention proposes a dynamic path planning embedded system for disinfection robots in complex environments. By combining global path planning with intelligent control, the robot can not only plan a preliminary path, but also adjust the path in real time according to the dynamic environment during execution, effectively improving the robot's adaptability in dynamic environments. Summary of the Invention

[0005] The purpose of the present invention is to provide a dynamic path planning embedded system for a disinfection robot in complex environments to address the shortcomings of the background technology.

[0006] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: an embedded system for dynamic path planning of a disinfection robot for complex environments, comprising a data acquisition module, a dynamic map building module, an intelligent planning module, an obstacle avoidance module, and an intelligent adjustment module; Data acquisition module: used to collect environmental data in real time; Dynamic map building module: uses real-time positioning and mapping technology to build dynamic maps; Intelligent planning module: performs optimal global path planning based on the constructed dynamic map and calculates the optimal global path from the starting point to the target point; Obstacle avoidance module: Through the local obstacle avoidance algorithm, the robot avoids sudden dynamic obstacles while maintaining the original path and dynamically adjusts the robot's route; Intelligent adjustment module: adjusts the robot's movement speed and direction in real time based on the optimal global path and dynamic adjustment results.

[0007] Preferably, the obstacle avoidance module simulates the force field by an artificial potential field method, wherein the target point is regarded as an attraction source, the obstacle is regarded as a repulsion source, and the robot is acted upon by two forces; Based on the robot's current position, the combined force between the target point and the obstacle is calculated, and then an obstacle avoidance path is generated, and the forward direction is adjusted in real time; The robot monitors the dynamic changes of obstacles through real-time sensor data and calculates new obstacle avoidance paths through obstacle avoidance algorithms. The robot adjusts its movement direction based on real-time data.

[0008] Preferably, the obstacle avoidance module simulates the force field by the artificial potential field method, and the steps are: assuming the current position of the robot is , the target point position is , the obstacle position is , then the total force on the robot is Expressed as: ,in: It's attraction, It is the repulsive force; Calculate a new obstacle avoidance path through the obstacle avoidance algorithm : ,in: is the angle the robot needs to adjust, is the current direction of the robot's movement.

[0009] Preferably, the intelligent planning module uses the constructed dynamic map to evaluate obstacles and free spaces in the environment, and calculates the optimal global path from the starting point to the target point through the Dijkstra algorithm; Evaluate the impact of dynamic obstacles in real time and dynamically adjust path planning.

[0010] Preferably, the optimal global path from the starting point to the target point is calculated by the Dijkstra algorithm, and the expression is: ,in: From the starting point to the node The shortest path distance, From the starting point to the node The shortest known path distance, It is a slave node To Node The weight of the edge.

[0011] Preferably, the dynamic map building module utilizes real-time positioning and mapping technology to simultaneously build a dynamic map and determine the position of the robot; Estimate position based on control inputs and sensor data, and build a dynamic map based on current position and environmental information obtained by sensors; LiDAR is used to obtain point cloud data of the surrounding environment. The point cloud is composed of a three-dimensional data set of measurement points, each of which contains its spatial position (x, y, z) and reflection intensity.

[0012] Preferably, the dynamic map building module uses real-time positioning and mapping technology to simultaneously build a dynamic map, which is expressed as: ,in: Indicates the robot at time State estimation, including position and attitude, It's the robot in time The control input, is the process noise, which represents the error produced in the actual motion.

[0013] Preferably, the impact of dynamic obstacles is evaluated in real time, and the expression is: ,in: Is the first The optimized distance function of points, is the weight coefficient of the dynamic obstacle, Represents the impact of dynamic obstacles on node i.

[0014] Preferably, the intelligent adjustment module obtains the motion state of the robot and performs real-time analysis to obtain the position and posture of the robot; Based on the real-time motion state of the robot, the intelligent adjustment module uses the motion control algorithm to dynamically adjust the robot's movement speed and direction. Through PID control, the robot's movement speed is adjusted in real time according to the deviation between the robot and the target path. and rotation speed .

[0015] Preferably, the embedded system further includes an evaluation module: when the robot moves along the optimal global path, it synchronously performs the disinfection task, monitors the disinfection effect in real time, and adjusts the disinfection intensity and duration according to the disinfection effect.

[0016] In the above technical solution, the technical effects and advantages provided by the present invention are: The present invention uses a data acquisition module to collect environmental data in real time. The dynamic map creation module uses real-time positioning and mapping technology to construct a dynamic map. The intelligent planning module performs optimal global path planning based on the constructed dynamic map, calculating the optimal global path from the starting point to the target point. The obstacle avoidance module uses a local obstacle avoidance algorithm to avoid sudden dynamic obstacles while maintaining the original path, and dynamically adjusts the robot's route. The intelligent adjustment module adjusts the robot's movement speed and direction in real time based on the optimal global path and dynamic adjustment results. By combining global path planning with intelligent control, the robot can not only plan a preliminary path but also adjust the path in real time according to the dynamic environment during execution, effectively improving the robot's adaptability in dynamic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a framework diagram of the embedded system of the present invention.

[0019] Figure 2 This is an operation flow chart of the embedded system of the present invention. DETAILED DESCRIPTION

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0021] Example 1: Please refer to Figure 1 As shown, the dynamic path planning embedded system for the disinfection robot in complex environments described in this embodiment includes a data acquisition module, a dynamic map building module, an intelligent planning module, an obstacle avoidance module and an intelligent adjustment module.

[0022] Data Acquisition Module: The disinfection robot first needs to perceive its environment and obtain information about surrounding obstacles, people, and objects. By integrating multiple sensors (lidar, ultrasonic sensors, infrared sensors, cameras, etc.), the robot can collect environmental data in real time and perform preliminary analysis. This sensor information provides the basis for subsequent path planning, intelligent control, and obstacle avoidance. This environmental data is then sent to the dynamic map creation module.

[0023] Dynamic Mapping Module: The robot uses real-time positioning and mapping technologies (such as SLAM) to build a dynamic map. By analyzing environmental data in real time, the system can perceive dynamic changes and promptly update the map to ensure that the map accurately reflects obstacles, people, and other changing factors in the environment. The dynamic map is then sent to the intelligent planning module.

[0024] Intelligent Planning Module: Based on the constructed dynamic map, the robot executes optimal global path planning, calculating the optimal global path from the starting point to the destination. This process not only considers static obstacles but also the potential impact of real-time dynamic obstacles. The Intelligent Control Module dynamically adjusts the path plan to adapt to environmental changes, ensuring the robot's continuous and efficient progress in complex environments. The planned optimal global path is then transmitted to the Obstacle Avoidance Module and the Intelligent Control Module.

[0025] Obstacle Avoidance Module: As the robot executes its optimal global path, it must avoid any new obstacles it encounters in real time. Using local obstacle avoidance algorithms (such as the artificial potential field method), the robot can avoid sudden dynamic obstacles while maintaining its original path. An intelligent correction system dynamically adjusts the robot's path based on real-time sensor data, sending the results of these adjustments to the intelligent adjustment module.

[0026] Intelligent Adjustment Module: After completing path planning, the robot uses the motion control system to precisely execute tasks based on the optimal global path and dynamic adjustments. The intelligent motion control algorithm adjusts the robot's speed and direction in real time based on its actual motion state and environmental feedback, ensuring path accuracy and task efficiency.

[0027] The embedded system also includes an evaluation module: As the robot follows the optimal global path, disinfection tasks are executed simultaneously. The disinfection system monitors disinfection effectiveness in real time through sensors such as UV sensors and air quality monitoring, adjusting parameters such as disinfection intensity and duration based on the results. An intelligent feedback mechanism ensures that disinfection tasks meet expected standards. The introduction of an intelligent feedback control mechanism dynamically adjusts disinfection strategies based on real-time data to ensure optimal disinfection performance in different environments.

[0028] The specific workflow of the embedded system is: Disinfection robots first need to sense their environment and acquire information about surrounding obstacles, people, and objects. By integrating multiple sensors (lidar, ultrasonic sensors, infrared sensors, cameras, etc.), the robots can collect environmental data in real time and perform preliminary analysis. This sensor-collected information provides the foundation for subsequent path planning, intelligent control, and obstacle avoidance.

[0029] The robot will use real-time positioning and mapping technologies (such as SLAM) to build dynamic maps. By analyzing environmental data in real time, the system can perceive dynamic changes and update the map in a timely manner to ensure that the map accurately reflects obstacles, people, and other changing factors in the environment.

[0030] Based on the constructed dynamic map, the robot executes optimal global path planning, calculating the optimal global path from its starting point to its destination. This path planning not only considers static obstacles but also the potential impact of real-time dynamic obstacles. The intelligent control module dynamically adjusts the path plan to adapt to environmental changes, ensuring the robot's continuous and efficient progress in complex environments.

[0031] As the robot executes its optimal global path, it must avoid any new obstacles it encounters in real time. Local obstacle avoidance algorithms, such as the artificial potential field method, allow the robot to avoid unexpected dynamic obstacles while maintaining its original path. An intelligent correction system dynamically adjusts the robot's path based on real-time sensor data.

[0032] After completing path planning, the robot uses the motion control system to precisely execute the task based on the optimal global path and dynamic adjustments. Intelligent motion control algorithms adjust the robot's speed and direction in real time based on its actual motion state and environmental feedback, ensuring path accuracy and task efficiency.

[0033] As the robot follows the optimal global path, disinfection tasks are executed simultaneously. The disinfection system monitors disinfection effectiveness in real time through sensors such as UV sensors and air quality monitoring, adjusting parameters such as disinfection intensity and duration based on the results. An intelligent feedback mechanism ensures that disinfection tasks meet expected standards. By incorporating an intelligent feedback control mechanism, disinfection strategies are dynamically adjusted based on real-time data, ensuring optimal disinfection performance in different environments.

[0034] This application uses a data acquisition module to collect environmental data in real time. The dynamic map building module uses real-time positioning and mapping technology to build a dynamic map. The intelligent planning module performs optimal global path planning based on the constructed dynamic map and calculates the optimal global path from the starting point to the target point. The obstacle avoidance module uses a local obstacle avoidance algorithm to avoid sudden dynamic obstacles while maintaining the original path and dynamically adjust the robot's route. The intelligent adjustment module adjusts the robot's movement speed and direction in real time based on the optimal global path and dynamic adjustment results. By combining global path planning with intelligent control, the robot can not only plan a preliminary path, but also adjust the path in real time according to the dynamic environment during execution, effectively improving the robot's adaptability in dynamic environments.

[0035] Data Acquisition Module: The disinfection robot first needs to perceive its environment and obtain information about surrounding obstacles, people, and objects. By integrating multiple sensors (lidar, ultrasonic sensors, infrared sensors, cameras, etc.), the robot can collect environmental data in real time and perform preliminary analysis. This sensor information provides the basis for subsequent path planning, intelligent control, and obstacle avoidance. This environmental data is then sent to the dynamic map creation module.

[0036] The data acquisition module is a core component of the disinfection robot system. It integrates multiple sensors to perceive the environment in real time, providing essential data for subsequent path planning, intelligent control, and obstacle avoidance. By sensing and collecting data from its surroundings, the robot acquires information about key elements such as obstacles, people, and objects, and sends this data to the subsequent dynamic mapping module for further processing.

[0037] First, the disinfection robot needs to perceive the structure and dynamic changes of its surroundings. To achieve this, the robot integrates a variety of sensors, including LiDAR, ultrasonic sensors, infrared sensors, and cameras. Each type of sensor has unique advantages and can complement each other.

[0038] Light Detection and Ranging (LiDAR) Sensors: LiDAR is widely used to obtain high-precision three-dimensional data of the surrounding environment, particularly for environmental mapping and obstacle detection. LiDAR calculates distance by emitting a laser beam and measuring the time it takes for the beam to reflect back (i.e., time of flight), generating a detailed point cloud of the environment. The output is a 3D dataset consisting of numerous points, each containing information about the position (x, y, z) and reflection intensity. This point cloud data provides the foundation for dynamic mapping.

[0039] Ultrasonic sensors: Ultrasonic sensors measure the distance to objects by emitting high-frequency sound waves and receiving echoes. They are suitable for close-range obstacle detection, especially in confined spaces. Due to their low cost and small size, ultrasonic sensors are often used to complement the short-range limitations of LiDAR.

[0040] Infrared sensors: These sensors measure thermal radiation and help robots identify and detect heat sources, such as humans or other high-temperature objects. Their advantage lies in their ability to operate effectively in low-light conditions and adverse weather conditions, making them suitable for environments with significant lighting fluctuations.

[0041] Cameras (visual sensors): Cameras capture images to provide visual information about the environment. Computer vision technology can be used to detect and identify complex environmental features (such as people, equipment, and obstacles), providing important support for subsequent path planning and obstacle avoidance.

[0042] After data acquisition, the data from various sensors must be preprocessed and fused for further use. Due to the differences in data types and accuracy of different sensors, preprocessing and fusion are important steps in the data acquisition module.

[0043] Data filtering and denoising: Because sensors are affected by environmental interference, such as reflected light, temperature changes, or electromagnetic noise, raw data may contain noise. Filtering algorithms (such as Kalman filtering or median filtering) can remove noise and smooth the data to improve data accuracy.

[0044] Sensor data fusion: Sensor fusion is the process of combining information from different sensors to create a more comprehensive and accurate description of the environment. For example, combining the 3D point cloud data provided by LiDAR with the image data from the camera can provide more accurate environmental perception. In this process, commonly used algorithms include weighted average fusion and Extended Kalman Filter (EKF). Assume that the LiDAR data at a certain moment is , the camera data is , the fused data It can be expressed by the following weighted formula: ,in, and is the weighting coefficient, which represents the reliability of each sensor data. The result of data fusion provides a comprehensive data input for subsequent processing.

[0045] Collected data is transmitted to a central processing unit or computing platform via a high-speed bus or wireless network (such as Wi-Fi or Bluetooth), ensuring real-time data integrity. The data acquisition module collaborates with other modules (such as the dynamic map creation module and the intelligent planning module) through an inter-module communication protocol, ensuring that all system components operate synchronously. The real-time data provided by the sensor module directly affects the accuracy of map construction and path planning.

[0046] The data acquisition module, through the collaborative work of multiple sensors, provides the disinfection robot with highly accurate environmental perception data. Through effective data preprocessing and fusion, as well as obstacle detection, the robot acquires a real-time understanding of its surroundings, providing the necessary foundational data for subsequent dynamic map updates, path planning, and obstacle avoidance decisions. The accuracy and real-time performance of the data acquisition module directly impact the performance of the entire robot system, ensuring the robot's ability to autonomously perform disinfection tasks in complex and dynamic environments.

[0047] Dynamic Mapping Module: The robot uses real-time positioning and mapping technologies (such as SLAM) to build a dynamic map. By analyzing environmental data in real time, the system can perceive dynamic changes and promptly update the map to ensure that the map accurately reflects obstacles, people, and other changing factors in the environment. The dynamic map is then sent to the intelligent planning module.

[0048] The dynamic map creation module is a key component of the disinfection robot's navigation system. It is designed to build and update a dynamic map of the robot's surroundings using real-time localization and mapping technologies (such as SLAM). By analyzing sensor data in real time, this module can perceive and respond to dynamic changes in the environment, ensuring that the created map always reflects obstacles, people, and other factors that may change. The dynamic map not only provides accurate information for path planning but also provides real-time data support for obstacle avoidance and task execution. The core goal of this module is to maintain the robot's high degree of autonomy and adaptability in complex environments through efficient real-time processing and map updates.

[0049] During the dynamic map creation process, the disinfection robot uses Simultaneous-Localization-and-Mapping (SLAM) technology to simultaneously construct a dynamic map and determine the robot's position. The key to SLAM technology is that during the exploration process, the robot needs to estimate its own position and continuously update the map of the environment, and these two processes are carried out simultaneously. The basic principle of the SLAM process can be expressed as follows: ,in: Indicates the robot at time The state estimation of , It's the robot in time control input (such as speed, angle, etc.). is the process noise, which indicates the error that may occur in actual motion. Through SLAM, the robot can The robot estimates its position using sensor data (such as lidar or vision data) and builds a dynamic map based on its current position and the surrounding information obtained by the sensors. At every moment, the robot's position information and map data are continuously updated to adapt to changes in the environment.

[0050] To build an accurate dynamic map during SLAM, the robot uses LiDAR or other sensors to acquire point cloud data of its surroundings. A point cloud is a three-dimensional dataset consisting of a large number of measured points, each containing its spatial position (x, y, z) and possibly other attributes (such as reflectance). In real-time environments, point cloud data forms the basis for map updates.

[0051] For each frame of laser scanning results, the robot uses a real-time point cloud processing algorithm to extract valid obstacle points, and then filters and removes noise to ensure data accuracy. For example, common point cloud filtering methods include: VoxelGridFilter: Reduces data density and removes redundant points by dividing the space into small grids.

[0052] Ground profile extraction and removal: By identifying ground points, removing point cloud data on the ground, and focusing on detecting obstacles.

[0053] Through these processed point cloud data, the SLAM algorithm can efficiently update the robot's position and mark the location and shape of obstacles on the map.

[0054] Unlike traditional static mapping techniques, dynamic mapping requires special attention to the detection and updating of dynamic obstacles in the environment. Dynamic obstacles (such as pedestrians and mobile devices) are constantly changing in the environment and must be detected and updated from the dynamic map in a timely manner.

[0055] To this end, the robot uses a motion detection algorithm to identify dynamic objects. By comparing point cloud data from consecutive frames, the robot can determine which points are from dynamic objects and which are static obstacles. This process can be described by the following formula: ,in: is the current time With the previous time The difference in point cloud data. and The point cloud data for the current frame and the previous frame are represented respectively. By calculating the difference between the point clouds of each frame, the robot can identify moving obstacles and remove them from the static dynamic map or mark them as dynamic obstacles separately, preventing them from being mistakenly identified as fixed obstacles during the path planning process.

[0056] To improve the efficiency and accuracy of map updates, disinfection robots typically maintain both a global map and a local map. The global map represents the general structure of the entire environment, while the local map provides detailed information about the robot's immediate surroundings. As the robot moves, the local map is continuously updated and periodically merged with the global map. Local map updates are achieved through the following methods: ,in: is the local map at the current moment, The global map is updated based on the accumulation of local maps and environmental changes. A map splicing algorithm (such as image optimization or consistency algorithm) merges the changes in multiple local maps to update the global map.

[0057] Once the dynamic map is updated in real time, the system transmits the updated map information to the intelligent planning module. The intelligent planning module recalculates the global path and obstacle avoidance path based on the latest dynamic map to ensure that the robot can perform tasks efficiently and safely in the current environment.

[0058] The dynamic map creation module utilizes SLAM technology integrated with sensor data to achieve efficient and accurate environmental perception and map updates. Through real-time analysis of the environment, the robot can flexibly respond to obstacles, people, and other factors in a dynamically changing environment, ensuring that the map always reflects the true state of the environment. This module not only provides accurate foundational data for path planning but also provides real-time feedback for obstacle avoidance and task execution, making it a core component of the disinfection robot's efficient and autonomous operation.

[0059] Intelligent Planning Module: Based on the constructed dynamic map, the robot executes optimal global path planning, calculating the optimal global path from the starting point to the destination. This process not only considers static obstacles but also the potential impact of real-time dynamic obstacles. The Intelligent Control Module dynamically adjusts the path plan to adapt to environmental changes, ensuring the robot's continuous and efficient progress in complex environments. The planned optimal global path is then transmitted to the Obstacle Avoidance Module and the Intelligent Control Module.

[0060] The intelligent planning module is a core component of the disinfection robot's autonomous navigation system. Its primary task is to calculate and execute optimal global path planning based on a constructed dynamic map and real-time perception data. In complex environments, path planning not only needs to account for static obstacles but also must assess the impact of dynamic obstacles in real time to ensure the robot can efficiently and safely move from its starting point to its destination. The goal of the intelligent planning module is to enable the robot to continue advancing in complex and changing environments while continuously optimizing the path, avoiding collisions, and ensuring the successful completion of the mission.

[0061] Global path planning is the primary task of the intelligent planning module. Its goal is to find the optimal path from the starting point to the destination. During this step, the robot uses a constructed dynamic map to assess obstacles and free space in the environment and calculate the optimal global path from the starting point to the destination. Common global path planning algorithms include the A* algorithm and the Dijkstra algorithm, both of which can search for a path with the minimum distance within a known map of the environment.

[0062] Dijkstra algorithm: Dijkstra algorithm is a classic graph search algorithm used to find the shortest path from the starting point to the target point. It only relies on the shortest distance from the starting point to each node for calculation and is suitable for scenarios without clear target information. Its path distance formula is: ,in: From the starting point to the node The shortest path distance. From the starting point to the node The shortest known path distance. It is a slave node To Node The weight of the edge (e.g. time).

[0063] In real-world applications, robots often need to operate in dynamic environments, where dynamic obstacles (such as moving people and other equipment) may appear or change position at any time. To ensure the real-time and effectiveness of path planning, the intelligent planning module must evaluate and incorporate the impact of these dynamic obstacles in real time during the global path planning process. This process typically involves continuous environmental monitoring and anticipation of dynamic obstacles.

[0064] The real-time impact of dynamic obstacles can be quantified by the following formula: ,in: Is the first The optimized distance function of each point (i.e., the path distance after considering dynamic obstacles). From the starting point to the node The shortest path distance. is the weight coefficient of the dynamic obstacle, which controls the influence of the dynamic obstacle on the path planning distance. represents the impact of a dynamic obstacle on node i, typically calculated as the distance between the dynamic obstacle and the current node. Based on this formula, the intelligent planning module can update the impact of obstacles based on real-time sensor data and dynamically adjust the path plan to avoid encountering unforeseen obstacles.

[0065] After the optimal global path is calculated, the intelligent planning module will not only consider the impact of static and dynamic obstacles, but also optimize the path according to factors such as task requirements, robot status and environmental changes. The core goal of path optimization is to maximize task efficiency while ensuring the stability and safety of the robot. For example, the smoothness of the path and the executable nature of the path are two important optimization indicators. Path optimization can be performed through the following methods: Path smoothing: Use methods such as curve fitting or Bezier curves to smooth sharp turns and unnecessary path segments in the path to ensure the stability of the robot in actual motion. Dynamic path adjustment: During execution, the robot may encounter new obstacles or environmental changes. At this time, the intelligent control module will re-evaluate the path based on the new environmental information and adjust the path in real time to avoid collisions or path failures. The mathematical expression of path optimization can be described by the distance function: ,in: is the total optimized distance of the path. Is the first The optimized distance function of the points. Is the first The weight of each point indicates its importance in the optimization. Through this distance function, path optimization can comprehensively consider multiple factors to ensure that the optimal path is not only the path with the lowest distance but also the most stable and feasible during execution.

[0066] Once the intelligent planning module calculates and optimizes the optimal global path, the results are transmitted to the obstacle avoidance module and the intelligent adjustment module. Based on the optimal path and real-time obstacle information, the obstacle avoidance module adjusts the robot's obstacle avoidance strategy during movement, ensuring collision-free execution of the path. The intelligent adjustment module dynamically adjusts the robot's speed and acceleration based on its current state (such as position, speed, and direction) to ensure accurate path execution and efficient task completion.

[0067] The Intelligent Planning Module combines global path planning, dynamic obstacle assessment, and path optimization technologies to enable the robot to navigate efficiently and safely in complex, dynamically changing environments. The module's core task is to calculate and adjust the optimal path based on real-time dynamic maps and obstacle information, while optimizing the path's feasibility, smoothness, and efficiency. Working in conjunction with the Obstacle Avoidance Module and the Intelligent Adjustment Module, the Intelligent Planning Module ensures the robot can flexibly and accurately complete disinfection tasks in a changing environment.

[0068] Obstacle Avoidance Module: As the robot executes its optimal global path, it must avoid any new obstacles it encounters in real time. Using local obstacle avoidance algorithms (such as the artificial potential field method), the robot can avoid sudden dynamic obstacles while maintaining its original path. An intelligent correction system dynamically adjusts the robot's path based on real-time sensor data, sending the results of these adjustments to the intelligent adjustment module.

[0069] The obstacle avoidance module is a key component of the disinfection robot's autonomous navigation system. Its primary task is to detect and avoid any new obstacles in real time as the robot executes its optimal global path, ensuring safe and smooth movement in complex and dynamic environments. Using a local obstacle avoidance algorithm and an intelligent correction system, the module dynamically adjusts the path based on real-time sensor data to avoid collisions and optimize path execution. This module not only ensures the robot's path safety but also improves overall mission execution efficiency.

[0070] The local obstacle avoidance algorithm is the core technology of the obstacle avoidance module. Its main function is to calculate a local path in real time to avoid new obstacles while the robot executes the global path, ensuring that the robot can circumvent dynamic obstacles without deviating from the globally planned path. Common local obstacle avoidance algorithms include artificial potential field, Voronoi diagram, and dynamic window methods. Among them, the artificial potential field method is widely used in obstacle avoidance systems due to its simple calculation and strong real-time performance.

[0071] The artificial potential field method simulates an imaginary force field, where the target point is considered an attraction source and the obstacle is considered a repulsion source. The robot is acted upon by two forces: one is the attraction from the target point, and the other is the repulsion from the obstacle. Based on the current position of the robot, the combined force of the two is calculated to generate an obstacle avoidance path. Assume that the current position of the robot is , the target point position is , the obstacle position is , then the total force on the robot is It can be expressed as: ,in: is the attractive force, usually the gravitational force between the target point and the robot, and is calculated as: ,in, is the repulsive force, coming from obstacles, which usually decreases with increasing distance. The calculation formula is: ,in: and are the coefficients of attraction and repulsion, controlling the magnitude of the forces. This is the minimum safe distance to ensure the robot does not collide with obstacles. Through the synthesized force field, the robot can avoid collisions with obstacles and adjust its direction in real time without deviating from the global path.

[0072] During actual operation, robots will constantly encounter new obstacles, especially in complex environments or crowded areas. The obstacle avoidance module uses real-time sensor data (such as LiDAR, ultrasound, and cameras) to monitor the dynamic changes of obstacles and calculate new avoidance paths using obstacle avoidance algorithms. The robot then adjusts its movement based on this real-time data to avoid collisions.

[0073] The key to real-time obstacle avoidance and dynamic path adjustment is to quickly respond to and calculate a new route based on sensor data. Assume that the robot’s current position is , the perception distance of the new obstacle is , the angle between the robot and the obstacle is , the robot's adjusted travel path can be calculated using the following formula: ,in: This is the angle the robot needs to adjust, which is dynamically calculated based on the relative position and distance of the current obstacle. This dynamic path adjustment ensures that the robot can respond to new obstacles in real time and optimize its path to avoid deviating from the mission goal.

[0074] The role of the intelligent correction system is to make detailed adjustments to the robot's trajectory based on real-time sensor data during the path execution process. The intelligent correction system not only adjusts the robot's direction based on the results of the local obstacle avoidance algorithm, but also comprehensively considers the robot's status (such as speed, acceleration, energy consumption, etc.) to ensure that the robot's movement during the path execution process is safe and efficient. For example, if the robot is performing a task, its current speed is , the target speed is , the robot's current position is , the intelligent correction system will dynamically adjust the speed and direction according to the real-time position of the obstacle and the motion state of the robot: ,in: This is the speed adjustment coefficient used to control the dynamic adjustment of the robot's speed. At the same time, the correction system also adjusts the robot's acceleration appropriately to avoid excessive speed changes caused by sudden obstacles, ensuring smooth and stable movement.

[0075] After the obstacle avoidance module completes dynamic path adjustment, the adjusted obstacle avoidance path and motion control instructions are transmitted to the intelligent adjustment module. The intelligent adjustment module further adjusts the robot's motion based on its status (such as current position, speed, and direction) to ensure efficient and accurate path execution. Furthermore, the intelligent adjustment module adjusts the robot's motion strategy and control parameters in real time based on task requirements and environmental changes to ensure mission objectives are achieved.

[0076] The obstacle avoidance module, through the collaboration of local obstacle avoidance algorithms and an intelligent correction system, effectively handles new obstacles encountered by the robot while executing its global path. Leveraging obstacle avoidance algorithms such as the artificial potential field method, the robot can dynamically adjust its motion path to avoid collisions while maintaining steady progress based on its globally planned path. Through the intelligent correction system, the obstacle avoidance module further optimizes the robot's trajectory and speed control, improving its motion stability and mission execution efficiency.

[0077] Intelligent Adjustment Module: After completing path planning, the robot uses the motion control system to precisely execute tasks based on the optimal global path and dynamic adjustments. The intelligent motion control algorithm adjusts the robot's speed and direction in real time based on its actual motion state and environmental feedback, ensuring path accuracy and task efficiency.

[0078] The intelligent adjustment module is a key component of the disinfection robot's autonomous navigation system. Its primary function is to ensure the robot executes tasks through a precise motion control system, based on the optimal global path and real-time dynamic adjustments. The intelligent adjustment module not only controls the robot's motion but also dynamically adjusts control parameters such as speed, direction, and acceleration based on the robot's current motion state and environmental feedback, ensuring efficient task execution and accurate path. Through intelligent motion control algorithms, the robot can flexibly adapt to changes in complex environments, ensuring successful task completion.

[0079] The primary task of the intelligent control module is to monitor and provide feedback on the robot's current motion state in real time. The robot's motion state usually includes parameters such as position, speed, direction, acceleration, etc. The intelligent control module obtains this information through sensor data (such as encoders, IMU sensors, lidar, etc.) and performs real-time analysis. This data will serve as input to guide subsequent motion adjustment and control. The current position of the robot can be expressed as ,in is the plane coordinate of the robot. The robot's direction (direction angle) relative to the reference coordinate system. The robot's speed can be determined by its linear velocity. and angular velocity Denote the forward speed and rotation speed of the robot respectively. ,Through real-time monitoring of these motion states, the ,intelligent adjustment module can obtain the precise position and posture of the ,robot, and provide data support for the subsequent motion ,control algorithm.

[0080] Based on the real-time motion state of the robot, the intelligent adjustment module uses a motion control algorithm to dynamically adjust the robot's movement speed and direction. The core goal of the motion control algorithm is to calculate the required speed and direction based on the deviation between the robot's current position and the target path, so that the robot can move smoothly along the optimal path. Common control algorithms include PID control (proportional-integral-differential control) and fuzzy control. PID control adjusts the robot's motion state by weighting the position error, velocity error, and acceleration error and calculating the control variable. The output of the PID controller can be expressed as: ,in: is the current position error, defined as the difference between the target position and the actual position. They are the proportional, integral and differential coefficients, respectively, which determine the response speed of the control system to the error. The control quantity is the speed or direction that the robot needs to adjust. Through PID control, the intelligent adjustment module can adjust the robot's movement speed in real time according to the deviation between the robot and the target path. and rotation speed , to ensure that the robot drives along the optimal path as accurately as possible.

[0081] In addition to adjusting speed and direction, the intelligent control module also needs to dynamically adjust the robot's acceleration to ensure smooth path execution. Excessive acceleration may cause the robot to lose balance or make sudden turns during path adjustment, while too low acceleration will prevent the robot from responding to environmental changes or dynamic obstacles in a timely manner.

[0082] The acceleration of the robot can be expressed as , which is the rate of change of speed over time. The intelligent adjustment module adjusts the acceleration in real time according to the robot's motion state to ensure that the robot's motion is smooth and meets the task requirements. For example, if the robot needs to change from the current speed to the Accelerate to target speed , the acceleration can be calculated by the following formula , the expression is: ,in: is the current speed, is the target speed, is the time interval that represents the time required to reach the target speed. By adjusting the acceleration, the intelligent regulation module ensures that the robot moves at the appropriate speed and direction while avoiding instability caused by sudden acceleration changes.

[0083] During mission execution, robots often face environmental changes, such as encountering new obstacles, changing mission targets, or changing paths. The Intelligent Adjustment Module dynamically adjusts the robot's path and motion strategy based on real-time environmental feedback, ensuring the robot's stable progress in this ever-changing environment.

[0084] The Intelligent Adjustment Module uses data from the Obstacle Avoidance Module to adjust the robot's path. If the robot encounters a new obstacle or other unexpected situation during execution, the Intelligent Adjustment Module dynamically calculates a new speed and direction based on the obstacle's location and the need for dynamic path adjustment, ensuring the robot avoids the obstacle and returns to the optimal path.

[0085] The ultimate goal of the intelligent adjustment module is to ensure the robot's efficient task completion through precise motion control. In complex environments, the robot may need to adjust its motion strategy based on task priorities and changes in target location. For example, when the robot approaches the target area, it can reduce speed to improve execution accuracy; while when the target area is farther away, it can increase speed to save time.

[0086] The intelligent control module ensures efficient task execution by adjusting motion parameters, taking into account task objectives, robot status, and environmental factors. After task completion, the intelligent control module transmits feedback information to other system modules to support task evaluation and subsequent task planning.

[0087] The intelligent regulation module monitors the robot's motion in real time and, in combination with motion control algorithms and acceleration adjustment strategies, precisely controls the robot's movement in complex environments. Using algorithms such as PID control, the intelligent regulation module ensures the robot's smooth and efficient execution of tasks, avoiding path deviations caused by environmental changes or dynamic obstacles. The module's flexibility and precision are key to ensuring the disinfection robot's efficient and autonomous execution, ensuring path accuracy and task efficiency in complex and changing environments.

[0088] The embedded system also includes an evaluation module: As the robot follows the optimal global path, disinfection tasks are executed simultaneously. The disinfection system monitors disinfection effectiveness in real time through sensors such as UV sensors and air quality monitoring, adjusting parameters such as disinfection intensity and duration based on the results. An intelligent feedback mechanism ensures that disinfection tasks meet expected standards. The introduction of an intelligent feedback control mechanism dynamically adjusts disinfection strategies based on real-time data to ensure optimal disinfection performance in different environments.

[0089] The evaluation module is a crucial component of the disinfection robot's embedded system. It monitors the effectiveness of disinfection tasks in real time and dynamically adjusts disinfection strategies based on actual results. This module utilizes a variety of sensors (such as UV sensors and air quality monitoring sensors) to capture key data from the disinfection process. Through an intelligent feedback mechanism, it precisely adjusts parameters such as disinfection intensity and duration to ensure that the disinfection effect meets the desired standards in different environments. The evaluation module not only optimizes disinfection efficiency but also ensures the quality of the disinfection process based on real-time feedback, ensuring that every area is fully and effectively disinfected.

[0090] During the disinfection process, the system uses various sensors to monitor disinfection effectiveness in real time. For example, ultraviolet (UV) sensors monitor the intensity of UV radiation to ensure the robot provides sufficient UV radiation for disinfection within the target area. Air quality sensors monitor changes in microbial concentrations in the air during the disinfection process, indirectly assessing disinfection effectiveness. Using these sensors, the evaluation module captures real-time data from the disinfection process and uses this data to determine the completion of the disinfection task.

[0091] For UV disinfection, there is usually a certain relationship between UV intensity and disinfection effect. Assume that the intensity measured by the UV sensor is , and its disinfection effect The relationship between can be described by the following formula: ,in: It is the ultraviolet disinfection effect, and the unit is sterilization rate or disinfection degree. The UV intensity measured by the UV sensor. The coefficient represents the proportionality between UV intensity and disinfection effectiveness, representing the relationship between UV intensity and sterilization rate. Through this real-time monitoring, the evaluation module can determine the UV irradiation effect in the current disinfection area and determine whether the disinfection intensity or duration needs to be adjusted based on preset standards.

[0092] After monitoring the disinfection effect in real time, the evaluation module will adjust the disinfection strategy through the intelligent feedback mechanism. The intelligent feedback mechanism dynamically adjusts various parameters in the disinfection process, including ultraviolet intensity and disinfection duration, based on real-time data and the disinfection standards set by the system. This mechanism can automatically optimize the disinfection process based on environmental changes and actual feedback to ensure that the disinfection effect always meets the expected standards in different environments. For example, in air quality monitoring, suppose the robot measures the bacterial concentration in the air in the current area through the air quality sensor. , the target concentration is , then the intelligent feedback mechanism can adjust the disinfection intensity or time according to the concentration difference. The adjustment strategy can be optimized by the following formula: ,in: is the change in the duration of disinfection to adjust it. It is the currently monitored bacterial concentration in the air. is the predetermined target bacterial concentration. It is the adjustment coefficient, which reflects the relationship between the change in bacterial concentration and the disinfection time. If the current bacterial concentration is higher than the target concentration, the intelligent feedback mechanism will automatically extend the disinfection time. , to ensure that the disinfection effect can meet expectations.

[0093] Based on real-time monitoring data, the evaluation module can flexibly adjust the disinfection intensity and duration. In a dynamic environment, the complexity and disinfection requirements of the disinfection areas may vary greatly. For example, some areas with higher bacterial concentrations may require longer UV exposure or stronger disinfection intensity; while other areas may only require shorter exposure times. The intelligent feedback mechanism continuously adjusts these parameters to ensure the efficient execution of the disinfection task and the optimization of the disinfection effect. The adjustment of disinfection intensity and duration is usually achieved through the following formula: ,in: is the adjusted disinfection time. It is the default disinfection time (disinfection time under standard circumstances). is the currently measured bacterial concentration in the air. is the target bacterial concentration. is an adjustment factor that controls the nonlinear relationship between disinfection time and changes in bacterial concentration. By adjusting the disinfection time, the evaluation module can ensure optimal disinfection performance in different environments and avoid over-disinfection or under-disinfection.

[0094] During the task execution, the evaluation module continuously assesses the disinfection effectiveness of each area and optimizes it based on real-time data. For example, in complex environments, some areas may face issues such as insufficient lighting or obstructions, resulting in less-than-expected disinfection results. Based on sensor data and environmental factors (such as light intensity and obstacle distribution), the evaluation module dynamically adjusts the disinfection strategy to ensure that all areas are fully disinfected.

[0095] The intelligent feedback control mechanism uses real-time data and environmental factors to provide precise adjustments to ensure optimal disinfection results. These adjustments typically include adjustments to disinfection intensity, duration, and routing, ensuring the desired disinfection results are consistently achieved under varying environmental conditions.

[0096] Once the disinfection task is complete, the evaluation module generates a task report based on the final feedback on the disinfection effect and transmits the report data to the upper-level system or user. The report includes key information such as the disinfection area, disinfection time, disinfection intensity, and disinfection effect. This data provides a basis for subsequent task evaluation, optimization, and system upgrades.

[0097] The evaluation module monitors disinfection effectiveness in real time and, in conjunction with an intelligent feedback mechanism, dynamically adjusts disinfection intensity and duration to ensure optimal disinfection performance under varying environmental conditions. By providing real-time feedback and adjustments to key parameters such as UV intensity and air quality, the evaluation module ensures disinfection quality and optimizes efficiency. This intelligent feedback mechanism ensures the disinfection process can flexibly adapt to environmental changes, thereby improving task completion and optimizing disinfection strategies, providing the robot with a precise and efficient disinfection solution.

[0098] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0099] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. Dynamic path planning embedded system for disinfection robots in complex environments, featuring: It includes data acquisition module, dynamic map building module, intelligent planning module, obstacle avoidance module and intelligent adjustment module; Data acquisition module: used to collect environmental data in real time; Dynamic map building module: uses real-time positioning and mapping technology to build dynamic maps; Intelligent planning module: performs optimal global path planning based on the constructed dynamic map and calculates the optimal global path from the starting point to the target point; Obstacle avoidance module: Through the local obstacle avoidance algorithm, the robot avoids sudden dynamic obstacles while maintaining the original path and dynamically adjusts the robot's route; Intelligent adjustment module: adjusts the robot's movement speed and direction in real time based on the optimal global path and dynamic adjustment results.

2. The dynamic path planning embedded system for disinfection robots in complex environments according to claim 1 is characterized by: The obstacle avoidance module simulates the force field through the artificial potential field method, in which the target point is regarded as the attraction source and the obstacle is regarded as the repulsion source, and the robot is subjected to the action of two forces; Based on the robot's current position, the combined force between the target point and the obstacle is calculated, and then an obstacle avoidance path is generated, and the forward direction is adjusted in real time; The robot monitors the dynamic changes of obstacles through real-time sensor data and calculates new obstacle avoidance paths through obstacle avoidance algorithms. The robot adjusts its movement direction based on real-time data.

3. The embedded system for dynamic path planning of disinfection robots in complex environments according to claim 2 is characterized by: The obstacle avoidance module simulates the force field by artificial potential field method, and the steps are as follows: suppose the current position of the robot is , the target point position is , the obstacle position is , then the total force on the robot is Expressed as: ,in: It's attraction, It is the repulsive force; Calculate a new obstacle avoidance path through the obstacle avoidance algorithm : ,in: is the angle the robot needs to adjust, is the current direction of the robot's movement.

4. The embedded system for dynamic path planning of disinfection robots in complex environments according to claim 3 is characterized by: The intelligent planning module uses the constructed dynamic map to evaluate the obstacles and free spaces in the environment and calculates the optimal global path from the starting point to the target point through the Dijkstra algorithm; Evaluate the impact of dynamic obstacles in real time and dynamically adjust path planning.

5. The embedded system for dynamic path planning of disinfection robots in complex environments according to claim 4 is characterized in that: The optimal global path from the starting point to the target point is calculated by the Dijkstra algorithm, and the expression is: ,in: From the starting point to the node The shortest path distance, From the starting point to the node The shortest known path distance, It is a slave node To Node The weight of the edge.

6. The embedded system for dynamic path planning of disinfection robots in complex environments according to claim 5 is characterized by: The dynamic map building module uses real-time positioning and mapping technology to simultaneously build a dynamic map and determine the position of the robot; Estimate position based on control inputs and sensor data, and build a dynamic map based on current position and environmental information obtained by sensors; LiDAR is used to obtain point cloud data of the surrounding environment. The point cloud is composed of a three-dimensional data set of measurement points, each of which contains its spatial position (x, y, z) and reflection intensity.

7. The embedded system for dynamic path planning of disinfection robots in complex environments according to claim 6, characterized in that: The dynamic map building module uses real-time positioning and mapping technology to simultaneously build a dynamic map, which is expressed as: ,in: Indicates the robot at time State estimation, including position and attitude, It's the robot in time The control input, is the process noise, which represents the error produced in the actual motion.

8. The embedded system for dynamic path planning of disinfection robots in complex environments according to claim 5, characterized in that: The impact of dynamic obstacles is evaluated in real time. The expression is: ,in: Is the first The optimized distance function of points, is the weight coefficient of the dynamic obstacle, Represents the impact of dynamic obstacles on node i.

9. The embedded system for dynamic path planning of disinfection robots in complex environments according to claim 1, characterized in that: The intelligent adjustment module obtains the robot's motion state and performs real-time analysis to obtain the robot's position and posture; Based on the real-time motion state of the robot, the intelligent adjustment module uses the motion control algorithm to dynamically adjust the robot's movement speed and direction. Through PID control, the robot's movement speed is adjusted in real time according to the deviation between the robot and the target path. and rotation speed .

10. The embedded system for dynamic path planning of disinfection robots in complex environments according to claim 9, characterized in that: The embedded system also includes an evaluation module: when the robot moves along the optimal global path, it synchronously performs the disinfection task, monitors the disinfection effect in real time, and adjusts the disinfection intensity and duration according to the disinfection effect.

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