Intelligent robot navigation control system and method

Abstract

The invention relates to the technical field of navigation and obstacle avoidance, and discloses an intelligent robot navigation control system and method, the system comprises a detection unit, a routing unit, a processing unit and an adjusting unit, the detection unit is responsible for detecting surrounding scene data and generating a map image to obtain a target position coordinate, and the detection unit sends the map image to the routing unit; the routing unit judges the map image in combination with the surrounding scene data to obtain path planning and path prohibition, the processing unit makes a first instruction according to the path planning and sends the first instruction to the adjusting unit, the processing unit makes a second instruction according to the path prohibition and sends the second instruction to the adjusting unit, and the adjusting unit adjusts the driving state of the robot according to the first instruction. The robot is driven to reach the next judgment position until the robot reaches the target position coordinates, the adjusting unit adjusts the driving state of the robot according to the second instruction, the robot is driven to return to the previous judgment position, and the path is forbidden.

Description

Technical Field

[0001] The present invention relates to the technical field of navigation and obstacle avoidance, and in particular to an intelligent robot navigation control system and method. Background Art

[0002] The application of intelligent robots in the field of navigation and obstacle avoidance is gradually increasing, especially in scenes such as entertainment venues, warehousing and logistics, and home services. The navigation control system's avoidance of obstacles is the key to ensuring the safe operation of the robot. However, the existing navigation and obstacle avoidance technology still has some shortcomings and limitations, and many existing navigation systems perform poorly when faced with complex and changing environments. For situations such as obstacle distance, uneven ground, and interference, traditional obstacle avoidance algorithms are prone to failure, resulting in the robot's inability to accurately identify obstacles, affecting its navigation capabilities. Secondly, the existing obstacle avoidance path planning algorithm has low computational efficiency during processing. When faced with multiple obstacles, the system's response strategy is insufficient and cannot effectively handle complex situations, resulting in long navigation time and a lack of flexible intelligent decision-making capabilities.

[0003] Therefore, how to provide an intelligent robot navigation control system and method is a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0004] In view of this, the present invention proposes an intelligent robot navigation control system and method, which aims to solve the problem that when the system faces multiple obstacles, the system's response strategy is insufficient and cannot effectively handle complex situations.

[0005] In one aspect, the present invention provides an intelligent robot navigation control system, comprising: Detection unit, routing unit, processing unit and adjustment unit; Detection unit, routing unit, processing unit and adjustment unit; The detection unit is responsible for detecting surrounding scene data and generating a map image to obtain target location coordinates, and the detection unit sends the map image to the path finding unit; The path finding unit determines the map image in combination with the surrounding scene data to obtain path planning and path prohibition, wherein the path planning includes: the first path planning, the second path planning and the third path planning; When an obstacle appears in front of the robot, the path-finding unit makes a first path planning at the determined position; When obstacles appear in front of and on the left side of the robot, the path-finding unit makes a second path planning at the determined position; When obstacles appear in front of and on the right side of the robot, the path-finding unit makes a third path planning at the determined position; When obstacles appear in front of, on the left and on the right of the robot, the path finding unit prohibits the path at the determined position; The processing unit generates a first instruction for the path planning and sends it to the adjustment unit, and the processing unit generates a second instruction for the path prohibition and sends it to the adjustment unit; The adjustment unit adjusts the driving state of the robot according to the first instruction, and drives the robot to the next determination position until the robot reaches the target position coordinates; The adjustment unit adjusts the driving state of the robot according to the second instruction, drives the robot to return to the last determined position and prohibits this path.

[0006] Further, the surrounding scenario data includes: environmental obstacle data, terrain contour data and external disturbance data; The environmental obstacle data includes: the location of the obstacle and the distance between the obstacle and the robot; The terrain profile data includes: terrain slope, ground material, ground width and ground height change; The external disturbance data includes: environmental humidity change, electromagnetic field interference intensity and communication link performance.

[0007] Furthermore, the detection unit is responsible for detecting surrounding scene data and generating a map image to obtain the target location coordinates, including: The detection unit uses SLAM technology to construct a scene map in real time in the surrounding scene, and the detection unit obtains GPS data and locates its own position coordinates according to the GPS data combined with the scene map; The detection unit uses edge detection technology on the scene map to generate the map image and confirm the target position coordinates.

[0008] Furthermore, the path finding unit makes a first path planning at the determined position, including: The first path includes: a left path to avoid obstacles ahead or a right path to avoid obstacles ahead; The path finding unit determines the first path planning according to the distance deviation; The distance deviation includes: a left distance deviation and a right distance deviation; The left distance deviation is the distance from the left path to the target position coordinates, and the right distance deviation is the distance from the right path to the target position coordinates; When the left distance deviation is greater than the right distance deviation, the right path is selected to avoid the obstacle ahead; When the left distance deviation is less than the right distance deviation, the left path is selected to avoid the obstacle ahead; When the leftward distance deviation is equal to the rightward distance deviation, the path finding unit determines the first path planning according to the width deviation.

[0009] Further, the path finding unit determines the first path planning according to the width deviation, including: The width deviation is obtained by the following formula: ; ; ; in, Indicates the material rating, include and , represents the material score of the left path, the represents the material score of the right path, represents the friction coefficient, represents the hardness coefficient, represents the width of the left path, represents the width of the right path, represents the width deviation of the left path, represents the width deviation of the right path, represents the width weight, represents the material weight, and ; when , select the left path to avoid the obstacle ahead; when , choose the right path to avoid the obstacles ahead.

[0010] Furthermore, the intelligent robot navigation control system comprises: The second path planning selects a rightward path to avoid obstacles, the third path planning selects a leftward path to avoid obstacles, and the path prohibits selecting a backward path to avoid obstacles.

[0011] Further, the processing unit includes: a central processing unit and a servo controller; The central processor converts the path planning into a first control signal, and the central processor converts the path prohibition into a second control signal; The first control signal and the second control signal are transmitted to the servo controller via the IRT mode of Profinet, and the servo controller generates the first instruction and the second instruction corresponding to the first control signal and the second control signal; The servo controller sends the first instruction and the second instruction to the adjustment unit, the first instruction is a first PWM pulse signal, and the second instruction is a second PWM pulse signal.

[0012] Furthermore, the intelligent robot navigation control system comprises: The adjusting unit controls the motors on both sides of the robot to rotate forward according to the first PWM pulse signal. The forward rotation of the motors on both sides includes: the left motor speed is greater than the right motor speed, the left motor speed is less than the right motor speed, and the left motor speed is equal to the right motor speed; When the speed of the left motor is greater than that of the right motor, the robot rotates to the right; When the rotation speed of the left motor is less than that of the right motor, the robot rotates to the left; When the rotation speed of the left motor is equal to the rotation speed of the right motor, the robot moves forward; The adjustment unit controls the motors on both sides of the robot to reverse according to the second PWM pulse signal. The motors on both sides are reversed so that the speed of the left motor is equal to the speed of the right motor and the rotation directions are opposite. At this time, the robot moves backward.

[0013] Furthermore, the intelligent robot navigation control system comprises: The adjusting unit controls the robot to move backward to return to the last determined position and prohibits this path according to the second PWM pulse signal; When the processing unit determines that the robot cannot reach the target position coordinates after executing the second PWM pulse signal, the processing unit generates a third PWM pulse signal, and the adjustment unit controls the robot to move backward and return to the last determined position according to the third PWM pulse signal and prohibits this path until the robot reaches the target position coordinates.

[0014] Compared with the prior art, the beneficial effect of the present invention is that the detection unit collects the surrounding scene data in real time, generates a map image and accurately locates the target position. It ensures that the intelligent robot can obtain timely information feedback in a changing environment, and then conducts effective path planning. The path-finding unit quickly makes corresponding path planning according to different situations, which not only improves the adaptability of the intelligent robot, but also ensures that it can flexibly respond to emergencies in complex scenes. The processing unit converts the generated path planning into specific instructions and sends it to the adjustment unit. When encountering obstacles, the adjustment unit quickly adjusts the driving state of the robot according to the instructions. The real-time feedback and adjustment mechanism reduces the time when the intelligent robot is stagnant due to obstacles and improves its work efficiency. When the robot faces multiple obstacle layouts, the processing unit will issue a second instruction in time to drive the intelligent robot back to the previous judgment position and prohibit the path, effectively avoiding invalid attempts, ensuring that the intelligent robot can quickly find a new path and avoid wasting time. By integrating the functions of detection, judgment, processing and adjustment, the safety and efficiency of the intelligent robot driving in complex environments are improved.

[0015] On the other hand, the present application also provides an intelligent robot navigation control method for applying the above intelligent robot navigation control system, comprising: Detecting surrounding scene data and generating a map image to obtain target location coordinates, and judging the map image in combination with the surrounding scene data to obtain path planning and path prohibition, wherein the path planning includes: the first path planning, the second path planning, and the third path planning; When an obstacle appears in front of the robot, the first path planning is made at the determined position; When obstacles appear in front of and on the left side of the robot, a second path planning is made at the determined position; When obstacles appear in front of and on the right side of the robot, a third path planning is made at the determined position; When obstacles appear in front of, on the left and on the right of the robot, a path prohibition is made at the determined position; Making a first instruction for the path planning and making a second instruction for the path prohibition; adjusting the driving state of the robot according to the first instruction, driving the robot to the next determination position, until the robot reaches the target position coordinates; The driving state of the robot is adjusted according to the second instruction, the robot is driven to return to the last determined position and this path is prohibited.

[0016] It is understandable that the above-mentioned intelligent robot navigation control system and method have the same beneficial effects, which will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A functional block diagram of an intelligent robot navigation control system provided by an embodiment of the present invention; Figure 2 The present invention provides a flowchart of an intelligent robot navigation control method. DETAILED DESCRIPTION

[0018] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0019] In some embodiments of the present application, see Figure 1 As shown, an intelligent robot navigation control system includes: a detection unit, a path finding unit, a processing unit and an adjustment unit. The detection unit is responsible for detecting surrounding scene data and generating a map image to obtain the target position coordinates. The detection unit sends the map image to the path finding unit. The path finding unit judges the map image in combination with the surrounding scene data to obtain path planning and path prohibition. The path planning includes: a first path planning, a second path planning and a third path planning. When an obstacle appears in front of the robot, the path finding unit makes a first path planning at the judgment position. When an obstacle appears in front of and on the left side of the robot, the path finding unit makes a second path planning at the judgment position. When an obstacle appears in front of and on the right side of the robot, the path finding unit makes a third path planning at the judgment position. When an obstacle appears in front of, on the left side and on the right side of the robot, the path finding unit makes a path prohibition at the judgment position. The processing unit makes a first instruction for the path planning and sends it to the adjustment unit. The processing unit makes a second instruction for the path prohibition and sends it to the adjustment unit. The adjustment unit adjusts the driving state of the robot according to the first instruction, drives the robot to the next judgment position, until the robot reaches the target position coordinates. The adjustment unit adjusts the driving state of the robot according to the second instruction, drives the robot back to the previous judgment position and prohibits this path.

[0020] Specifically, the detection unit is responsible for real-time detection of the surrounding environmental data. The detection unit generates map images through laser radar, ultrasonic sensors and cameras to determine the target position coordinates, providing basic information for subsequent path planning. The path-finding unit is responsible for path planning based on the map image and surrounding scene data provided by the detection unit, and formulates the corresponding travel path under different obstacle conditions. The path planning of the path-finding unit is an intelligent decision and can be adjusted in real time to cope with changes in obstacles. The processing unit is responsible for receiving the path planning from the path-finding unit and generating corresponding instructions according to different planning situations. The processing unit is the decision-making core of the system, ensuring that the robot can avoid obstacles according to real-time instructions. For the first, second or third path planning, the processing unit generates a first instruction to guide the adjustment unit to adjust the driving state of the intelligent robot so that it moves along the planned path. For path prohibition, the processing unit will issue a second instruction to guide the robot to return to the last judgment position and prohibit the path to avoid collision and repeated entry into this path.

[0021] It is understandable that the processing unit ensures a quick response in complex environments and ensures the safety of the robot through real-time data analysis. The adjustment unit is responsible for adjusting the driving state of the robot according to the instructions sent by the processing unit to ensure that the robot moves along the planned path. When the first instruction is received, the adjustment unit activates the drive system to move the robot toward the next judgment position until the intelligent robot reaches the target position coordinates. When the second instruction is received, the adjustment unit instructs the robot to return to the previous judgment position and prohibits the path to ensure that the robot no longer crosses known obstacle areas. The adjustment unit controls the motor accordingly by responding to the instructions of the processing unit in real time, ensuring the smoothness of the movement of the intelligent robot.

[0022] In some embodiments of the present application, the surrounding scene data includes: environmental obstacle data, terrain contour data and external disturbance data. The environmental obstacle data includes: the location of the obstacle and the distance between the obstacle and the robot. The terrain contour data includes: terrain slope, ground material, ground width and ground height changes. The external disturbance data includes: environmental humidity changes, electromagnetic field interference intensity and communication link performance.

[0023] Specifically, the acquisition of surrounding scenario data is the key to ensuring the safe and efficient operation of the robot. Environmental obstacle data is information about various obstacles that the robot may encounter during movement, including the location of the obstacle and the distance between the obstacle and the robot. Obstacles can be fixed or mobile. Through lidar and cameras, the robot can obtain the location data of obstacles in real time and mark them on the map image. Distance information helps the intelligent robot evaluate the safe space between the obstacle and the obstacle. When the robot approaches an obstacle, the robot is guided to choose an appropriate obstacle avoidance strategy by calculating the distance to the obstacle. Terrain contour data provides the terrain characteristics of the environment in which the intelligent robot is located, which helps it to navigate effectively in complex terrain. External disturbance data are factors that may affect the normal operation of the intelligent robot. Monitoring external disturbance data helps enhance the adaptability of the intelligent robot in complex environments.

[0024] It is understandable that the slope of the terrain directly affects the robot's motion performance. On steep slopes, the intelligent robot needs to increase power to overcome its own gravity, and needs to slow down when going downhill to prevent loss of control. By monitoring the slope of the terrain in real time, it can be ensured that the intelligent robot is traveling on a safe path. Different ground materials will have different effects on the traction and stability of the intelligent robot. Monitoring the ground material data can help the intelligent robot choose a suitable path and its own driving speed. The width of the ground determines whether the robot can pass through a certain area smoothly. The narrow path will limit the movement of the intelligent robot and cannot pass through a certain area smoothly. Therefore, while avoiding obstacles, it is also necessary to avoid narrow paths and monitor changes in ground height to avoid falls or collisions caused by the intelligent robot during driving. In addition, changes in environmental humidity can affect the performance of the electrical components of the intelligent robot. Changes in humidity can cause distortion of sensor signals and affect the accuracy of environmental perception. Electromagnetic interference can affect the communication and sensor functions of the intelligent robot, resulting in data loss or errors. By monitoring changes in environmental humidity, electromagnetic field interference intensity, and communication link performance, the detection unit can generate surrounding scene data in real time, enhancing the autonomous navigation and obstacle avoidance capabilities of the intelligent robot.

[0025] In some embodiments of the present application, the detection unit is responsible for detecting the surrounding scene data and generating a map image to obtain the target position coordinates, including: the detection unit uses SLAM technology to build a scene map in the surrounding scene in real time, the detection unit obtains GPS data and locates its own position coordinates based on the GPS data and the scene map, and the detection unit uses edge detection technology to generate a map image for the scene map and confirm the target position coordinates.

[0026] It is understandable that the detection unit adopts the simultaneous positioning and mapping SLAM technology. SLAM technology can effectively construct a situation map by continuously updating the surrounding situation data. As the intelligent robot moves, the detection unit will continue to collect new surrounding situation data and update the situation map. In addition, the detection unit integrates GPS positioning to improve the accuracy of positioning and ensure the accuracy of its own position coordinates. The use of SLAM technology and GPS positioning in the detection unit not only enhances the autonomous navigation capability of the intelligent robot, but also provides a reliable foundation for subsequent path planning and decision-making. Edge detection can identify significant features in the situation map, thereby extracting the main outline of the surrounding situation, making the situation map more intuitive, helping the intelligent robot to confirm the target location coordinates, and facilitating subsequent processing and analysis. The multi-level data processing and real-time update mechanism of the detection unit enable the intelligent robot to complete navigation tasks efficiently and accurately.

[0027] In some embodiments of the present application, the path-finding unit makes a first path planning at the determined position, including: the first path includes: a left path to avoid obstacles ahead or a right path to avoid obstacles ahead, the path-finding unit determines the first path planning according to the distance deviation, the distance deviation includes: a left distance deviation and a right distance deviation, the left distance deviation is the distance from the left path to the target position coordinates, the right distance deviation is the distance from the right path to the target position coordinates, when the left distance deviation is greater than the right distance deviation, the right path is selected to avoid obstacles ahead, when the left distance deviation is less than the right distance deviation, the left path is selected to avoid obstacles ahead, when the left distance deviation is equal to the right distance deviation, the path-finding unit determines the first path planning according to the width deviation.

[0028] It can be understood that when the path-finding unit determines the first path planning, it will evaluate the distance deviation of the two paths. The left distance deviation and the right distance deviation represent the distance that the intelligent robot takes to reach the target position coordinates along the left and right paths, respectively, starting from the current judgment position. By calculating the deviation, the path-finding unit can evaluate which path is more suitable. When the left distance deviation is greater than the right distance deviation, it means that the distance to the target is farther through the left path. At this time, the path-finding unit will choose the right path to avoid obstacles ahead. On the contrary, if the left distance deviation is less than the right distance deviation, it means that the distance to the target on the left path is closer, and the path-finding unit will choose the left path to avoid. In certain cases, when the left distance deviation is equal to the right distance deviation, the path-finding unit cannot rely on the distance deviation to make a decision, so the width deviation needs to be further considered. The width deviation refers to the difference in the passable width between the two paths. The path-finding unit will further determine which path is safer based on the width deviation.

[0029] In some embodiments of the present application, the path finding unit determines the first path plan according to the width deviation, including: The width deviation is given by the following formula: ; ; ; in, Indicates the material rating, include and , represents the material score of the left path, represents the material score of the right path, represents the friction coefficient, represents the hardness coefficient, Indicates the width of the left path, Indicates the width of the right path, Indicates the width deviation of the left path, Indicates the width deviation of the right path, represents the width weight, represents the material weight, and ,when , choose the left path to avoid the obstacles ahead. , choose the right path to avoid obstacles ahead.

[0030] It is understandable that after the width deviation is obtained, the path-finding unit will make a decision based on the calculation results. If the calculated left width deviation is greater than or equal to the right width deviation, the robot will choose the left path to avoid the obstacle in front. Conversely, if the left width deviation is less than the right width deviation, the right path will be chosen to avoid. Ensure that the intelligent robot can move safely in a complex environment. By comprehensively considering the path width, material score and weight, the path-finding unit can make a more intelligent path selection, enhance its adaptability and flexibility, and ensure that the intelligent robot can effectively avoid obstacles and continue to move forward in a complex space.

[0031] In some embodiments of the present application, the intelligent robot navigation and control system includes: the second path planning selects a right path to avoid obstacles, the third path planning selects a left path to avoid obstacles, and the path prohibits selecting a backward path to avoid obstacles.

[0032] It is understandable that when the intelligent robot encounters obstacles in front and on the left, the second path planning will choose to avoid the path to the right to ensure that the robot can smoothly bypass the obstacles and continue to move forward. When the intelligent robot encounters obstacles in front and on the right, the third path planning will choose to avoid the path to the left to ensure that the robot can smoothly bypass the obstacles and continue to move forward. When the intelligent robot encounters obstacles in front, on the left and on the right, the path prohibition adopts a retreat strategy to ensure that the robot safely withdraws to the walking area. The path-finding unit enables the intelligent robot to effectively avoid obstacles, thereby improving the operating efficiency and safety of the intelligent robot.

[0033] In some embodiments of the present application, the processing unit includes: a central processing unit and a servo controller, the central processing unit converts path planning into a first control signal, the central processing unit converts path prohibition into a second control signal, the first control signal and the second control signal are transmitted to the servo controller via the IRT mode of Profinet, the servo controller makes corresponding first instructions and second instructions according to the first control signal and the second control signal, the servo controller sends the first instruction and the second instruction to the adjustment unit, the first instruction is a first PWM pulse signal, and the second instruction is a second PWM pulse signal.

[0034] It is understandable that the central processing unit is responsible for receiving the path planning data and path prohibition data from the path-finding unit and converting them into corresponding first control signals and second control signals. The generated first control signal and second control signal are efficiently transmitted through the IRT mode of Profinet. Profinet is a communication protocol for automation networks. The IRT mode provides high-bandwidth and low-latency data transmission capabilities to ensure that the signal can be transmitted to the servo controller quickly and accurately. When the servo controller receives the first control signal and the second control signal from the central processing unit, the servo controller issues the corresponding first instruction and the second instruction, which are transmitted to the adjustment unit in the form of PWM pulse signals. After the adjustment unit receives the instruction of the PWM pulse signal, it will adjust the driving state of the intelligent robot accordingly according to the actual situation, thereby realizing the navigation control of the intelligent robot and improving the efficient obstacle avoidance ability of the intelligent robot.

[0035] In some embodiments of the present application, an intelligent robot navigation control system includes: an adjustment unit controls the forward rotation of motors on both sides of the robot according to a first PWM pulse signal, and the forward rotation of the motors on both sides includes: the left motor speed is greater than the right motor speed, the left motor speed is less than the right motor speed, and the left motor speed is equal to the right motor speed. When the left motor speed is greater than the right motor speed, the robot turns right, when the left motor speed is less than the right motor speed, the robot turns left, and when the left motor speed is equal to the right motor speed, the robot moves forward. The adjustment unit controls the reverse rotation of the motors on both sides of the robot according to a second PWM pulse signal. The reverse rotation of the motors on both sides is that the left motor speed is equal to the right motor speed and the rotation direction is opposite, and the robot moves backward.

[0036] It is understandable that the adjustment unit will control the forward rotation of the motors on both sides according to the first PWM pulse signal instruction, and the speed of the motor will be adjusted according to the path planning. When the path-finding unit selects the first path planning, the motor drives the robot to turn left or right according to the first path planning. When the path-finding unit selects the second path planning, the motor drives the robot to turn right according to the second path planning. When the path-finding unit selects the third path planning, the motor drives the robot to turn left according to the third path planning. After the intelligent robot makes the rotation adjustment, it moves forward. In addition, the adjustment unit will control the reverse rotation of the motors on both sides according to the second PWM pulse signal instruction. When the path-finding unit selects path prohibition, the motor drives the robot to move backward according to path prohibition. The difference in motor speed enables the intelligent robot to flexibly respond to different navigation needs.

[0037] In some embodiments of the present application, the intelligent robot navigation control system includes: an adjustment unit controls the robot to move backward to a previous judgment position and prohibits this path according to a second PWM pulse signal; when the processing unit determines that the robot cannot reach the target position coordinates after executing the second PWM pulse signal, the processing unit generates a third PWM pulse signal; and the adjustment unit controls the robot to move backward to a previous judgment position and prohibits this path according to the third PWM pulse signal until the robot reaches the target position coordinates.

[0038] It can be understood that when the adjustment unit controls the robot to move backward according to the second PWM pulse signal instruction, the robot will return to the last judgment position and be prohibited from continuing to move along the path to avoid entering this path again. If the returned last judgment position can only move along this path, the target position coordinates can never be reached. At this time, the processing unit will generate a third PWM pulse signal. The third PWM pulse signal is used to instruct the adjustment unit to continue to control the intelligent robot to move backward and return to the last judgment position, while maintaining the prohibition of the path until the robot selects a suitable path to reach the target position coordinates, ensuring that the intelligent robot can achieve smooth movement in a changing environment.

[0039] In summary, the beneficial effects of the present invention are: the detection unit collects surrounding scene data in real time, generates a map image and accurately locates the target position. It ensures that the intelligent robot can obtain timely information feedback in a changing environment, and then conducts effective path planning. The path-finding unit quickly makes corresponding path planning according to different situations, which not only improves the adaptability of the intelligent robot, but also ensures that it can flexibly respond to emergencies in complex scenes. The processing unit converts the generated path planning into specific instructions and sends it to the adjustment unit. When encountering obstacles, the adjustment unit quickly adjusts the driving state of the robot according to the instructions. The real-time feedback and adjustment mechanism reduces the time when the intelligent robot is stagnant due to obstacles and improves its work efficiency. When the robot faces multiple obstacle layouts, the processing unit will issue a second instruction in time to drive the intelligent robot back to the previous judgment position and prohibit the path, effectively avoiding invalid attempts, ensuring that the intelligent robot can quickly find a new path and avoid wasting time. By integrating the functions of detection, judgment, processing and adjustment, the safety and efficiency of the intelligent robot driving in a complex environment are improved.

[0040] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides an intelligent robot navigation control method for applying the above intelligent robot navigation control system, including: S100: Detecting surrounding scene data and generating a map image to obtain target location coordinates, and judging the map image in combination with the surrounding scene data to obtain path planning and path prohibition, wherein the path planning includes: the first path planning, the second path planning and the third path planning; S200: When an obstacle appears in front of the robot, a first path planning is made at the determined position; When obstacles appear in front of and on the left side of the robot, a second path planning is made at the determined position; When obstacles appear in front of and on the right side of the robot, a third path planning is made at the determined position; When obstacles appear in front of, on the left and on the right of the robot, a path prohibition is made at the determined position; S300: issuing a first instruction for the path planning and issuing a second instruction for the path prohibition; S400: adjusting the driving state of the robot according to the first instruction, driving the robot to the next determination position, until the robot reaches the target position coordinates; The driving state of the robot is adjusted according to the second instruction, the robot is driven to return to the last determined position and this path is prohibited.

[0041] It is understandable that the surrounding scene data is detected and a map image is generated to obtain the target position coordinates, and the path planning judgment is made based on the surrounding scene data. Different path planning is performed according to the location of the obstacle: when an obstacle appears in front of the robot, the first path planning is executed; when obstacles appear in front and on the left, the second path planning is performed; when obstacles appear in front and on the right, the third path planning is performed; when the front and both sides are blocked by obstacles, a path prohibition is made, and corresponding instructions are generated according to the path planning and path prohibition. According to the first instruction, the driving state of the robot is adjusted to drive it to the next judgment position until it reaches the target position. Executing the second instruction drives the robot back to the previous judgment position and prohibits driving along the path. Ensure that the intelligent robot can autonomously navigate and flexibly avoid obstacles in complex environments.

[0042] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0043] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0044] These computer program instructions may also be stored in a computer-readable storage device that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable storage device produce a product including an instruction device that implements the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0045] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0046] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An intelligent robot navigation control system, characterized in that: include: Detection unit, routing unit, processing unit and adjustment unit; The detection unit is responsible for detecting surrounding scene data and generating a map image to obtain target location coordinates, and the detection unit sends the map image to the path finding unit; The path finding unit determines the map image in combination with the surrounding scene data to obtain path planning and path prohibition, wherein the path planning includes: the first path planning, the second path planning and the third path planning; When an obstacle appears in front of the robot, the path-finding unit makes a first path planning at the determined position; When obstacles appear in front of and on the left side of the robot, the path-finding unit makes a second path planning at the determined position; When obstacles appear in front of and on the right side of the robot, the path-finding unit makes a third path planning at the determined position; When obstacles appear in front of, on the left and on the right of the robot, the path finding unit prohibits the path at the determined position; The processing unit generates a first instruction for the path planning and sends it to the adjustment unit, and the processing unit generates a second instruction for the path prohibition and sends it to the adjustment unit; The adjustment unit adjusts the driving state of the robot according to the first instruction, and drives the robot to the next determination position until the robot reaches the target position coordinates; The adjustment unit adjusts the driving state of the robot according to the second instruction, drives the robot to return to the last determined position and prohibits this path.

2. The intelligent robot navigation control system according to claim 1, characterized in that: The surrounding scenario data includes: environmental obstacle data, terrain contour data and external disturbance data; The environmental obstacle data includes: the location of the obstacle and the distance between the obstacle and the robot; The terrain profile data includes: terrain slope, ground material, ground width and ground height change; The external disturbance data includes: environmental humidity change, electromagnetic field interference intensity and communication link performance.

3. The intelligent robot navigation control system according to claim 2, characterized in that: The detection unit is responsible for detecting surrounding scene data and generating a map image to obtain the target location coordinates, including: The detection unit uses SLAM technology to construct a scene map in real time in the surrounding scene, and the detection unit obtains GPS data and locates its own position coordinates according to the GPS data combined with the scene map; The detection unit uses edge detection technology on the scene map to generate the map image and confirm the target position coordinates.

4. The intelligent robot navigation control system according to claim 1, characterized in that: The path finding unit makes a first path planning at the determined position, including: The first path includes: a left path to avoid obstacles ahead or a right path to avoid obstacles ahead; The path finding unit determines the first path planning according to the distance deviation; The distance deviation includes: a left distance deviation and a right distance deviation; The left distance deviation is the distance from the left path to the target position coordinates, and the right distance deviation is the distance from the right path to the target position coordinates; When the left distance deviation is greater than the right distance deviation, the right path is selected to avoid the obstacle ahead; When the left distance deviation is less than the right distance deviation, the left path is selected to avoid the obstacle ahead; When the leftward distance deviation is equal to the rightward distance deviation, the path finding unit determines the first path planning according to the width deviation.

5. The intelligent robot navigation control system according to claim 4, characterized in that: The path finding unit determines the first path planning according to the width deviation, including: The width deviation is obtained by the following formula: ； ； ； in, Indicates the material rating, include and , represents the material score of the left path, the represents the material score of the right path, represents the friction coefficient, represents the hardness coefficient, represents the width of the left path, represents the width of the right path, represents the width deviation of the left path, represents the width deviation of the right path, represents the width weight, represents the material weight, and ; when , select the left path to avoid the obstacle ahead; when , choose the right path to avoid the obstacles ahead.

6. The intelligent robot navigation control system according to claim 5, characterized in that: include: The second path planning selects a rightward path to avoid obstacles, the third path planning selects a leftward path to avoid obstacles, and the path prohibits selecting a backward path to avoid obstacles.

7. The intelligent robot navigation control system according to claim 6, characterized in that: The processing unit includes: a central processing unit and a servo controller; The central processor converts the path planning into a first control signal, and the central processor converts the path prohibition into a second control signal; The first control signal and the second control signal are transmitted to the servo controller via the IRT mode of Profinet, and the servo controller generates the first instruction and the second instruction corresponding to the first control signal and the second control signal; The servo controller sends the first instruction and the second instruction to the adjustment unit, the first instruction is a first PWM pulse signal, and the second instruction is a second PWM pulse signal.

8. The intelligent robot navigation control system according to claim 7, characterized in that: include: The adjusting unit controls the motors on both sides of the robot to rotate forward according to the first PWM pulse signal. The forward rotation of the motors on both sides includes: the left motor speed is greater than the right motor speed, the left motor speed is less than the right motor speed, and the left motor speed is equal to the right motor speed; When the speed of the left motor is greater than that of the right motor, the robot rotates to the right; When the rotation speed of the left motor is less than that of the right motor, the robot rotates to the left; When the rotation speed of the left motor is equal to the rotation speed of the right motor, the robot moves forward; The adjustment unit controls the motors on both sides of the robot to reverse according to the second PWM pulse signal. The motors on both sides are reversed so that the speed of the left motor is equal to the speed of the right motor and the rotation directions are opposite. At this time, the robot moves backward.

9. The intelligent robot navigation control system according to claim 8, characterized in that: include: The adjusting unit controls the robot to move backward to return to the last determined position and prohibits this path according to the second PWM pulse signal; When the processing unit determines that the robot cannot reach the target position coordinates after executing the second PWM pulse signal, the processing unit generates a third PWM pulse signal, and the adjustment unit controls the robot to move backward and return to the last determined position according to the third PWM pulse signal and prohibits this path until the robot reaches the target position coordinates.

10. An intelligent robot navigation control method, used for applying the intelligent robot navigation control system according to any one of claims 1 to 9, characterized in that: include: Detecting surrounding scene data and generating a map image to obtain target location coordinates, and judging the map image in combination with the surrounding scene data to obtain path planning and path prohibition, wherein the path planning includes: the first path planning, the second path planning, and the third path planning; When an obstacle appears in front of the robot, the first path planning is made at the determined position; When obstacles appear in front of and on the left side of the robot, a second path planning is made at the determined position; When obstacles appear in front of and on the right side of the robot, a third path planning is made at the determined position; When obstacles appear in front of, on the left and on the right of the robot, a path prohibition is made at the determined position; Making a first instruction for the path planning and making a second instruction for the path prohibition; adjusting the driving state of the robot according to the first instruction, driving the robot to the next determination position, until the robot reaches the target position coordinates; The driving state of the robot is adjusted according to the second instruction, the robot is driven to return to the last determined position and this path is prohibited.

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