Robot autonomous obstacle avoidance and path re-planning control method in dynamic environment

Abstract

The invention discloses a robot autonomous obstacle avoidance and path re-planning control method in a dynamic environment, and the method comprises the steps: 1, setting a map, a path and a data database, and carrying out the movement in an original map and path based on the original data database; step 2, based on an original path set in a map, carrying out anti-theft detection and analysis on an obstacle which is abruptly lost in the moving process; and step 3, based on the original path set in the map, the robot in the dynamic environment can conveniently carry out real-time omnibearing detection on the obstacles which are absent and increased suddenly in the specified path, can analyze, judge and compare the absent obstacles, and can timely send out a missing alarm to personnel, so that the safety of the personnel is improved. The probability of article loss is greatly reduced, the anti-theft effect in an inspection environment is improved, the safety of the environment is improved, meanwhile, path re-planning can be conveniently carried out on the environment where obstacles are suddenly lost and increased, and use is convenient.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic obstacle avoidance and path planning of robots, and in particular to a method for autonomous obstacle avoidance and path replanning control of robots in a dynamic environment. Background Art

[0002] A robot is an intelligent machine that can work semi-autonomously or fully autonomously. Through programming and automated control, the robot's mechanical structure can be controlled, making it easier for the robot to assist humans in their work and lives, reducing the burden of human work while also improving the efficiency and accuracy of production and processing. In order to improve the safety of the robot's movement during its movement, it is necessary to improve the robot's autonomous obstacle avoidance capabilities and its ability to replan its route.

[0003] Patent application publication number CN117970925A discloses a real-time obstacle avoidance and dynamic path planning method and system for robots. The system comprises environmental perception, obstacle representation, and autonomous motion. The environmental perception component includes an image acquisition module, a calibration module, and a multi-sensor fusion mechanism. The obstacle representation component first classifies static and dynamic obstacles, then performs posture recognition, builds an obstacle model, and simplifies the model boundary. For dynamic obstacles, the trajectory and range are predicted, and the characteristic outline of the obstacle is extracted. Autonomous motion includes obstacle avoidance decision-making and motion control. If a dynamic obstacle is present, collision detection and trajectory replanning algorithms are used to adjust the robot's movement path in real time within the local environment. Based on the planned path, motion control is then output to the robot, achieving obstacle avoidance, path selection, and motion. Through the coordination of these modules and mechanisms, a reliable, fast, highly autonomous, and high-precision real-time obstacle avoidance system is achieved.

[0004] The above-mentioned comparative documents can only enable the robot to achieve obstacle avoidance function in the set path through the cooperation of various modules. It is unable to more accurately detect and judge and analyze obstacles that are suddenly lost in the map path. It is easy to cause the problem that obstacles in the map path cannot be quickly discovered and recovered. It is not conducive to reducing the probability of item loss and improving the protection of property safety. At the same time, it is unable to automatically re-plan the path in an environment where obstacles are suddenly missing or added, and cannot improve the intelligence and convenience of robot use. Summary of the Invention

[0005] The purpose of the present invention is to provide a robot autonomous obstacle avoidance and path replanning control method in a dynamic environment, which solves the problem that the comparison file cannot perform more accurate detection and judgment analysis of obstacles suddenly lost in the map path, and it is easy to cause the obstacles in the map path to be lost and cannot be quickly discovered and recovered, which is not conducive to reducing the probability of item loss and improving the protection of property safety. At the same time, it is not possible to automatically re-plan the path in an environment where obstacles are suddenly missing or added, and it is not possible to improve the intelligence and convenience of robot use.

[0006] The present invention solves the above-mentioned technical problems through the following technical solutions, which include: Step 1: Set up the map, route and data database, and move within the original map and route based on the original data database; Step 2: Based on the original path set in the map, anti-theft detection and analysis are performed for obstacles that are suddenly missing during movement; Step 3: Based on the original path set in the map, detect, analyze and compare obstacles that suddenly appear during movement; Step 4: Mark missing and added obstacles and their locations, automatically add route channels and avoid obstacles, and replan the route. Step 5: Based on the information of stopping movement during obstacle avoidance, deploy the stable stop system and prevent accidental collision of the vehicle body during obstacle avoidance.

[0007] Preferably, based on the step 2: the point information is updated synchronously during the movement of the robot, and the information detected at the corresponding point is transmitted to the data database for comparison, and the RRT algorithm is started to analyze and compare the data; after the laser radar detects that the obstacle in the corresponding point is missing, the human infrared sensor uses infrared rays to sense whether there is human body heat and whether the distance to the obstacle increases, to determine whether it is a human; if it is a human, the robot continues to move.

[0008] Preferably, based on the step 2: after the human infrared sensor detects that the person is not a human, after detecting that the obstacle in the corresponding point is missing, the point information and the information of the missing obstacle at the corresponding point are transmitted to the data database; the data database queries the information of the specific obstacle in the corresponding point to find out the specific name of the missing obstacle; the obstacle missing alarm information and the point information of the missing obstacle are sent to the remote control terminal to remind people and items to prevent theft; and the point information is marked.

[0009] Preferably, based on the step 2: after the personnel remotely checks, they give feedback to the robot through remote control to continue driving, and the robot continues to drive; after the personnel remotely checks, they give feedback to the robot through remote control to wait, and the robot waits on the spot and sounds an alarm to prompt the outside world, giving relevant personnel a clear direction prompt sound, reducing the risk of personnel going to the wrong location and improving the efficiency of checking; after the personnel checks, they give feedback to the robot to continue driving, and the robot continues to drive.

[0010] Preferably, based on the step three: when an obstacle is detected at the corresponding point, the robot stops moving, and uses the human infrared sensor to sense whether there is human body heat and whether the distance to the obstacle changes, to determine whether it is a human; if it is a human, the alarm issues an avoidance prompt, the person leaves, and the robot passes; if the person does not avoid, the robot replans the route to avoid it.

[0011] Preferably, based on the step three: if it is a fixed obstacle, the monitoring device photographs and scans the added obstacle; the photographed and scanned information is transmitted to the data database, matched with objects of corresponding shape and appearance in the data database, and the name of the object is determined; the point where the obstacle is added is marked, and the specific obstacle object at the newly marked point is entered into the data database.

[0012] Preferably, based on the step four: the data database gives feedback to the control system after receiving the missing and added information; the control system transmits information to control the control system and the laser radar to start at the same time, and combined with the artificial potential field method, the laser radar starts to detect the distance around the detected point, and combines the original path to plan new channel information that can be passed; at the same time, the new channel information is transmitted to the control system, the control system is started, and the robot passes through the new channel; the new channel information is transmitted to the data database, and the path is replanned.

[0013] Preferably, based on the step five: after the robot stops moving due to the addition of an obstacle, the control system transmits information to the stabilization system; the stabilization system controls the deployment of the stabilization structure to increase the stability of the bottom of the robot; when the data database receives missing and added information to make the robot move again, the stabilization system controls the recovery of the stabilization structure; the control system changes direction, the robot continues to move, and the speed control system controls the robot to move forward at a low speed; after crossing the newly added obstacle point, it accelerates and then travels at a constant speed to prevent accidental collision with the obstacle when turning when avoiding the obstacle.

[0014] Preferably, based on the step one: determining the moving direction; controlling the system to change the direction and move forward; using a laser radar to measure the distance to the surrounding obstacles during the movement.

[0015] Preferably, the point information is updated synchronously during the movement of the robot, and the information detected in the corresponding point is transmitted to the data database for comparison, and the RRT algorithm is started to analyze and compare the data; the laser radar detects whether the obstacle in the point is missing, determines whether the missing obstacle is a human, issues a missing alarm and marks the missing obstacle point; when an obstacle is detected at the corresponding point, the robot stops moving, determines whether it is a human or a fixed obstacle, and makes an avoidance move; after receiving the missing and added information, the data database combines the artificial potential field method and the laser radar to re-plan the path; after the obstacle forces the robot to stop, the stabilization system increases the stability of the bottom of the robot that stops suddenly, and combines the RRT algorithm, the artificial potential field method, the laser radar, the control system and the speed control system to provide anti-collision protection for the robot that avoids obstacles.

[0016] Compared with the prior art, the beneficial effects of the present invention are: the present invention can realize the effect that the robot in a convenient dynamic environment can perform real-time and all-round detection of obstacles that are suddenly missing or added within the specified route range, can analyze and judge whether the missing obstacles are objects and compare them, and can promptly issue missing alarms to personnel, greatly reducing the probability of lost items, which is beneficial to improving the anti-theft effect in the patrol environment and improving the safety of the environment. At the same time, it can realize the effect of convenient automatic re-planning of paths in environments where obstacles are suddenly missing or added, which can improve the intelligence and convenience of robot use. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the overall process of the present invention; Figure 2 This is a schematic flow chart of step 2 of the present invention; Figure 3 This is a partial flow diagram of step 2 of the present invention; Figure 4 This is a schematic flow chart of step three of the present invention; Figure 5 This is a schematic flow chart of step four of the present invention; Figure 6 This is a schematic diagram of step five of the present invention; Figure 7 This is a schematic diagram of step 1 of the present invention; Figure 8 This is a flow chart of step two, step three, step four and step five of the present invention. DETAILED DESCRIPTION

[0018] The above and other technical features and advantages of the present invention are described in more detail below with reference to the accompanying drawings.

[0019] The present invention provides a technical solution: a robot autonomous obstacle avoidance and path replanning control method in a dynamic environment, such as Figure 1-8 As shown, it includes step 1: setting a map, path and data database, and moving within the original map and path based on the original data database, which can facilitate the robot to move according to the set path; step 2: based on the original path set in the map, anti-theft detection and analysis of obstacles that suddenly disappear during movement are performed, which can facilitate the robot to quickly detect and analyze the situation in the path; step 3: based on the original path set in the map, obstacles that suddenly appear during movement are detected, analyzed and compared, which can quickly make judgments on changes in the path, which is beneficial to the safety of the robot during movement; step 4: based on the missing and added obstacles and the point information of the missing and added obstacles, information marking is performed, route channels and obstacle avoidance are automatically added, and the route is re-planned, which can facilitate and quickly obtain a new path map; step 5: based on the stop movement information during obstacle avoidance movement, a stable stop system is deployed and accidental collision of the body during obstacle avoidance is prevented, which is beneficial to better safety protection of the robot.

[0020] Input the data database into the robot system, and the various modules and systems inside the robot can cooperate with each other to control the mechanical structure of the robot, making it easier for the robot to move. The robot moves according to the path in the set map. The point information is updated synchronously during the movement of the robot. At the same time, it can detect obstacles in the points and pass the information detected in the corresponding points to the data database for comparison. The data database can query and compare the information, and the RRT algorithm starts to analyze and compare the data. The laser radar detects whether the obstacles in the points are missing. After detecting that there are missing obstacles in the points, the human infrared sensor can be used to monitor the missing obstacles and determine whether the missing obstacles are humans. If they are humans, the robot continues to move forward. If they are not humans, the robot can issue a missing alarm and mark the missing obstacle points, and can check the personnel. Better reminders make it easier for personnel to quickly reach the location where obstacles are missing and to quickly find missing items; when an obstacle is detected at the corresponding point, the robot stops moving and uses the human infrared sensor to determine whether it is a human or a fixed obstacle and make avoidance. If it is a human, the robot will issue an obstacle avoidance alarm to facilitate humans to avoid the robot on the route. If humans do not have the conditions to avoid, the robot will autonomously plan the route to avoid them; after receiving the missing and added information, the data database will combine the artificial potential field method and lidar to re-plan the path, which is beneficial to improve the intelligence of the robot and make the robot more flexible in action; after the obstacle forces the robot to stop, the stabilization system increases the stability of the bottom of the robot that stops suddenly, and combines the RRT algorithm, artificial potential field method, lidar, control system and speed control system to provide anti-collision protection for the obstacle-avoiding robot, which is beneficial to increase the service life of the robot.

[0021] like Figure 1-3 As shown, based on step two: the point information is updated synchronously during the movement of the robot, and the information detected in the corresponding point is passed to the data database for comparison. The data database stores a large amount of data information, which can compare different detected objects encountered in the map. At the same time, the RRT algorithm starts to analyze and compare the data, which can improve the efficiency of information comparison; after the laser radar detects that the obstacle in the corresponding point is missing, the control system can control the human infrared sensor to start. The human infrared sensor uses infrared to sense whether there is human heat and whether the distance to the obstacle increases, and judges whether it is a human; if it is a human, the robot continues to move, and the missing human body cannot block the robot when it moves. Based on step two: if it is not a human after the human infrared sensor detects that the obstacle in the corresponding point is missing, the point information and the corresponding point missing obstacle information are passed to the data database. The data database stores information on each obstacle in the path; the data database The information of the specific obstacles in the point should be queried to find out the specific names of the missing obstacles, which can facilitate the comparison of information and quickly find the names of the detected obstacles; the obstacle missing alarm information and the point information of the missing obstacle are sent to the remote control end to remind personnel to prevent theft of items and improve the security of item theft prevention; the point information is marked and the point information can be passed to the data database, which can improve the richness of the information in the data database and facilitate the comparison of subsequent obstacles encountered. Based on step 2: after the personnel remotely checks, they give feedback to the robot through remote control to continue driving. At this time, the personnel can search according to the information of the items stored in the robot, and the robot continues to drive and avoid obstacles; after the personnel remotely checks, they give feedback to the robot through remote control to wait, and the robot waits in place and issues an alarm to prompt the outside world, giving relevant personnel a clear direction prompt sound, reducing the risk of personnel going to the wrong location and improving viewing efficiency; after the personnel check, they give feedback to the robot to continue driving, and the robot continues to drive.

[0022] like Figure 1 and Figure 4As shown, based on step three: when an obstacle is detected at the corresponding point, the robot stops moving, and uses the human infrared sensor to sense whether there is human body heat and whether the distance to the obstacle changes, to determine whether it is a human; if it is a human, the alarm issues an avoidance prompt, the person leaves, and the robot passes; the person does not avoid, and the person does not have the conditions to avoid, so the robot replans the route to avoid, and the robot needs to use the program in step four, based on step three: if it is a fixed obstacle, the monitoring equipment shoots and scans the added obstacle; the shooting and scanning information is transmitted to the data database, matched with the objects of the corresponding shape and appearance in the data database, and the name of the object is determined; the point where the obstacle is added is marked, and the specific obstacle objects at the newly marked point are entered into the data database, so that the robot can subsequently compare other obstacles appearing in the path.

[0023] like Figure 1 and Figure 5 As shown, based on step four: after receiving the missing and added information, the data database gives feedback to the control system to facilitate the control system to make instructions; the control system transmits information to control the control system and the lidar to start at the same time, and combined with the artificial potential field method, the lidar starts to detect the distance around the detected point, and combines the original path to plan new channel information that can be passed, so that the robot can move forward from the new channel; at the same time, the new channel information is transmitted to the control system, the control system is started, and the robot passes through the new channel; the new channel information is transmitted to the data database, and the path is replanned to facilitate the robot to continue moving.

[0024] like Figure 1 and Figure 6 As shown, based on step five: after the robot stops moving due to the addition of an obstacle, the control system transmits information to the stabilization system; the stabilization system controls the expansion of the stabilization structure to increase the stability of the bottom of the robot and prevent the robot from tipping over when it makes an emergency stop to avoid obstacles; when the data database receives missing and added information to make the robot move again, the stabilization system controls the recovery of the stabilization structure; the control system changes direction, the robot continues to move, and the speed control system controls the robot to move forward at a low speed; after crossing the newly added obstacle point, it accelerates and then travels at a constant speed to prevent accidental collision with obstacles when turning when avoiding obstacles, and to prevent the edge of the robot from colliding with obstacles when turning.

[0025] like Figure 1 and Figure 7 As shown, based on step one: determining the moving direction, so that the robot can determine the direction of travel; the control system changes the direction and moves forward; during the movement, the laser radar is used to measure the distance to the surrounding obstacles, so as to facilitate real-time detection of obstacles in the path.

[0026] The above are only preferred embodiments of the present invention and are merely illustrative, not restrictive, of the present invention. Those skilled in the art will appreciate that many changes, modifications, and even equivalents may be made to the embodiments within the spirit and scope of the claims, all of which fall within the scope of protection of the present invention.

Claims

1. A robot autonomous obstacle avoidance and path replanning control method in a dynamic environment, characterized by: include: Step 1: Set up the map, route and data database, and move within the original map and route based on the original data database; Step 2: Based on the original path set in the map, anti-theft detection and analysis are performed for obstacles that are suddenly missing during movement; Step 3: Based on the original path set in the map, detect, analyze and compare obstacles that suddenly appear during movement; Step 4: Mark missing and added obstacles and their locations, automatically add route channels and avoid obstacles, and replan the route. Step 5: Based on the information of stopping movement during obstacle avoidance, deploy the stable stop system and prevent accidental collision of the vehicle body during obstacle avoidance.

2. The method for autonomous obstacle avoidance and path replanning of a robot in a dynamic environment as claimed in claim 1, characterized in that: Based on the step 2: the point information is updated synchronously during the movement of the robot, and the information detected at the corresponding point is transmitted to the data database for comparison, and the RRT algorithm is started to analyze and compare the data; after the laser radar detects the absence of the obstacle at the corresponding point, the human infrared sensor uses infrared rays to sense whether there is human body heat and whether the distance to the obstacle increases, to determine whether it is a human; if it is a human, the robot continues to move.

3. The method for controlling a robot's autonomous obstacle avoidance and path replanning in a dynamic environment as claimed in claim 2, wherein: Based on the step 2: after the human infrared sensor detects that the person is not a human, after detecting that the obstacle in the corresponding point is missing, the point information and the information of the missing obstacle at the corresponding point are transmitted to the data database; the data database queries the information of the specific obstacle in the corresponding point to find out the specific name of the missing obstacle; the obstacle missing alarm information and the point information of the missing obstacle are sent to the remote control terminal to remind people and items to prevent theft; and the point information is marked.

4. The method for controlling robot autonomous obstacle avoidance and path replanning in a dynamic environment as claimed in claim 3, characterized in that: Based on the step 2: after the personnel remotely checks, they give feedback to the robot through remote control to continue driving, and the robot continues to drive; after the personnel remotely checks, they give feedback to the robot through remote control to wait, and the robot waits in place and sounds an alarm to prompt the outside world, giving relevant personnel a clear direction prompt sound, reducing the risk of personnel going to the wrong location and improving inspection efficiency; after the personnel checks, they give feedback to the robot to continue driving, and the robot continues to drive.

5. The method for controlling robot autonomous obstacle avoidance and path replanning in a dynamic environment as claimed in claim 4, characterized in that: Based on the step three: when an obstacle is detected at the corresponding point, the robot stops moving and uses the human infrared sensor to sense whether there is human body heat and whether the distance to the obstacle changes, to determine whether it is a human; if it is a human, the alarm issues an avoidance prompt, the person leaves, and the robot passes; if the person does not avoid, the robot replans the route to avoid it.

6. The method for controlling a robot's autonomous obstacle avoidance and path replanning in a dynamic environment as claimed in claim 5, wherein: Based on step three: if it is a fixed obstacle, the monitoring device photographs and scans the added obstacle; the photographed and scanned information is transmitted to the data database, matched with objects of corresponding shape and appearance in the data database, and the name of the object is determined; the point where the obstacle is added is marked, and the specific obstacle object at the newly marked point is entered into the data database.

7. The method for controlling a robot's autonomous obstacle avoidance and path replanning in a dynamic environment as claimed in claim 6, wherein: Based on the step 4: the data database provides feedback to the control system after receiving the missing and added information; The control system transmits information to control the control system and the laser radar at the same time. Combined with the artificial potential field method, the laser radar starts to detect the distance around the detection point, and combines the original path to plan new channel information that can be passed through; at the same time, the new channel information is transmitted to the control system, the control system is started, and the robot passes through the new channel; the new channel information is transmitted to the data database to replan the path.

8. The method for controlling a robot's autonomous obstacle avoidance and path replanning in a dynamic environment as claimed in claim 7, wherein: Based on the step 5: after the robot stops moving due to the addition of an obstacle, the control system transmits information to the stabilization system; the stabilization system controls the stabilization structure to deploy, increasing the stability of the bottom of the robot; when the data database receives the missing and added information to enable the robot to move again, the stabilization system controls the stabilization structure to retract; The control system changes direction, the robot continues to move, and the speed control system controls the robot to move forward at a low speed; after crossing the newly added obstacle point, it accelerates and then travels at a constant speed to prevent accidental collision with obstacles when turning when avoiding obstacles.

9. The method for controlling robot autonomous obstacle avoidance and path replanning in a dynamic environment as claimed in claim 8, characterized in that: Based on the step 1: determining the moving direction; The control system changes direction and moves forward; during the movement, the laser radar is used to measure the distance to the surrounding obstacles.

10. The method for controlling robot autonomous obstacle avoidance and path replanning in a dynamic environment according to claim 9, wherein: During the robot's movement, the point information is updated synchronously, and the information detected at the corresponding point is transmitted to the data database for comparison. At the same time, the RRT algorithm is activated to analyze and compare the data. The lidar detects whether the obstacle at the point is missing, determines whether the missing obstacle is a human, issues a missing alarm, and marks the missing obstacle point. If an obstacle is detected at the corresponding point, the robot stops moving, determines whether it is a human or a fixed obstacle, and makes an avoidance move. After receiving the missing and added information, the data database combines the artificial potential field method and the lidar to replan the path. After the robot is forced to stop by an obstacle, the stabilization system increases the stability of the bottom of the robot that stops suddenly. Combined with the RRT algorithm, artificial potential field method, lidar, control system and speed control system, it provides anti-collision protection for the robot that avoids obstacles.

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