Trajectory planning and control method and device for inspection robots

Abstract

This application discloses a trajectory planning and control method and device for an inspection robot, relating to the field of robotics technology. The method includes: acquiring environmental perception information and robotic arm state information of the inspection robot, initializing trajectory parameters of the initial motion trajectory to obtain processed trajectory parameters; inputting the processed trajectory parameters into a multi-objective optimization model for iterative calculation to obtain an optimized motion trajectory; determining a first motion trajectory based on obstacle information encountered by the robotic arm during its movement along the optimized motion trajectory; determining a second motion trajectory based on trajectory deviation information encountered by the robotic arm during its movement along the first motion trajectory; and controlling the robotic arm to move according to the second motion trajectory, so that the inspection robot completes its inspection task. This application solves the problem in related technologies where it is difficult to provide good path planning for inspection robots, resulting in poor performance in completing inspection tasks.

Description

Technical Field

[0001] This application relates to the field of robotics technology, and more specifically, to a trajectory planning and control method and device for an inspection robot. Background Technology

[0002] Track-guided inspection robots are an important component of modern industrial automation and intelligence, and are widely used in inspection tasks in industries such as power, railway, and pipeline. These robots move in specific environments along predetermined trajectories to perform tasks such as inspection, monitoring, and maintenance. Six-degree-of-freedom robotic arms are a key piece of equipment widely used in industrial automation, intelligent manufacturing, and service robots. Their six degrees of freedom correspond to the position (X, Y, Z) and posture (rotation around the X, Y, and Z axes) of the robotic arm in three-dimensional space, enabling it to flexibly and accurately complete various complex operational tasks.

[0003] Furthermore, trajectory planning is a core technology in robot motion control, aiming to design a motion path for the robot from a starting point to an ending point. Traditional trajectory planning methods mainly include Dijkstra's algorithm (an algorithm for calculating the shortest path from a source node to all other nodes in a graph) and dynamic programming. These methods can provide good path planning results in simple environments, but in complex and variable environments, they often exhibit the following shortcomings:

[0004] 1. Low trajectory accuracy: Traditional control methods fail to fully consider the dynamic characteristics of the robotic arm and real-time changes in the environment, resulting in low trajectory accuracy.

[0005] 2. Limited obstacle avoidance capability: In terms of obstacle avoidance, traditional control methods lack intelligent processing and are prone to colliding with obstacles in complex environments, affecting the smooth progress of inspection tasks.

[0006] Therefore, traditional trajectory planning methods are difficult to provide good path planning for inspection robots, which leads to poor performance in completing inspection tasks.

[0007] There is currently no effective solution to the problem that related technologies cannot provide good path planning for inspection robots, resulting in poor performance in completing inspection tasks. Summary of the Invention

[0008] The main purpose of this application is to provide a trajectory planning and control method and device for inspection robots, so as to solve the problem in related technologies that it is difficult to provide good path planning for inspection robots, resulting in poor performance of inspection robots in completing inspection tasks.

[0009] To achieve the above objectives, according to one aspect of this application, a trajectory planning and control method for an inspection robot is provided. The method includes: acquiring environmental perception information and robotic arm status information of an inspection robot; initializing trajectory parameters of an initial motion trajectory based on the environmental perception information and robotic arm status information to obtain processed trajectory parameters, wherein the initial motion trajectory represents the initial planned motion trajectory of the robotic arm of the inspection robot; inputting the processed trajectory parameters into a multi-objective optimization model for iterative calculation to obtain an optimized motion trajectory, and controlling the robotic arm to move according to the optimized motion trajectory, wherein the multi-objective optimization model is a model constructed using a genetic algorithm or a particle swarm optimization algorithm, based on the time information, energy consumption information, smoothness, and obstacle avoidance information of the robotic arm's execution trajectory; determining a first motion trajectory based on obstacle information encountered by the robotic arm during its movement according to the optimized motion trajectory, and controlling the robotic arm to move according to the first motion trajectory; determining a second motion trajectory based on trajectory deviation information encountered by the robotic arm during its movement according to the first motion trajectory, and controlling the robotic arm to move according to the second motion trajectory, so that the inspection robot completes the inspection task.

[0010] Further, determining the first motion trajectory based on obstacle information encountered by the robotic arm during its movement according to the optimized motion trajectory includes: detecting whether there are obstacles during the movement of the robotic arm according to the optimized motion trajectory; if there are obstacles during the movement of the robotic arm according to the optimized motion trajectory, adjusting the optimized motion trajectory to obtain the first motion trajectory; if there are no obstacles during the movement of the robotic arm according to the optimized motion trajectory, not adjusting the optimized motion trajectory, and using the optimized motion trajectory as the first motion trajectory.

[0011] Furthermore, if there are obstacles while the robotic arm is moving according to the optimized motion trajectory, the optimized motion trajectory is adjusted, and the calculation formula for the first motion trajectory is obtained as follows: Where N represents the degrees of freedom of the robotic arm, and η i The weights for optimizing the obstacle avoidance path for the i-th degree of freedom, Ω i Let X be the spatial region Ω where obstacles can exist in the i-th degree of freedom. i A vector at any position, d i (X) is the obstacle distance function at position X in the intelligent obstacle avoidance system, representing the distance of the obstacle from the robotic arm. Let k be an exponential decay function of the distance to the obstacle.i Determines the decay rate, For in the spatial region Ω i The average obstacle avoidance effect within the area.

[0012] Further, determining the second motion trajectory based on the trajectory deviation information of the robotic arm during its movement according to the first motion trajectory includes: acquiring the current position and posture information of the robotic arm, and determining the current motion trajectory executed by the robotic arm based on the current position and posture information of the robotic arm; determining whether there is a deviation between the current motion trajectory executed by the robotic arm and the first motion trajectory; if there is a deviation between the current motion trajectory executed by the robotic arm and the first motion trajectory, adjusting the current motion parameters of the robotic arm to obtain the second motion trajectory; if there is no deviation between the current motion trajectory executed by the robotic arm and the first motion trajectory, not adjusting the current motion parameters of the robotic arm, and using the first motion trajectory as the second motion trajectory.

[0013] Furthermore, if there is a deviation between the current motion trajectory executed by the robotic arm and the first motion trajectory, the current motion parameters of the robotic arm are adjusted, and the calculation formula for the second motion trajectory is obtained as follows: Where N represents the degrees of freedom of the robotic arm. Let r be the weight parameter for the i-th degree of freedom. i s is the target position vector in the error compensation mechanism. i (t) is the actual position vector, representing the current position of the robotic arm obtained by fusing data collected from multiple sensors, ||r i (t)-s i (t)|| 2 The square of the Euclidean distance between the target position and the actual position represents the magnitude of the error.

[0014] Furthermore, the calculation formula for the multi-objective optimization model is as follows: Where N represents the degrees of freedom of the robotic arm, and α i Let t be the weight parameter for the i-th degree of freedom, and t be the time variable, t0-t f For the time range of trajectory planning, p i (t) represents the current position of the robotic arm in the i-th degree of freedom, which changes with time t, q i (t) represents the planned position of the robotic arm in the i-th degree of freedom, which changes with time t. The square root of the trajectory deviation represents the smoothness and optimization level of the trajectory.

[0015] Furthermore, controlling the robotic arm to move according to the second motion trajectory so that the inspection robot can complete the inspection task includes: generating a control command according to the second motion trajectory; and controlling the robotic arm to move according to the control command so that the inspection robot can complete the inspection task.

[0016] Furthermore, after controlling the robotic arm to move according to the second motion trajectory based on the control command, the method further includes: acquiring information about the robotic arm moving according to the second motion trajectory to obtain motion state information of the robotic arm and environmental change information of the robotic arm; adjusting the second motion trajectory based on the motion state information of the robotic arm and environmental change information of the robotic arm to obtain a target motion trajectory; and controlling the robotic arm to move according to the target motion trajectory so that the inspection robot completes the inspection task.

[0017] Furthermore, acquiring the environmental perception information and robotic arm status information of the inspection robot includes: acquiring data information related to the inspection robot collected by multiple sensors to obtain multiple data information, wherein the multiple sensors include: a vision sensor, a force sensor, and an acceleration sensor; fusing the multiple data information to obtain the environmental perception information and robotic arm status information of the inspection robot.

[0018] To achieve the above objectives, according to another aspect of this application, a trajectory planning and control device for an inspection robot is provided. The device includes: a first processing unit, configured to acquire environmental perception information and robotic arm status information of the inspection robot, and initialize the trajectory parameters of the initial motion trajectory based on the environmental perception information and robotic arm status information of the inspection robot to obtain processed trajectory parameters, wherein the initial motion trajectory represents the initial planned motion trajectory of the robotic arm of the inspection robot; a second processing unit, configured to input the processed trajectory parameters into a multi-objective optimization model for iterative calculation to obtain an optimized motion trajectory, and control the robotic arm to move according to the optimized motion trajectory, wherein the multi-objective optimization model is a model constructed using a genetic algorithm or a particle swarm optimization algorithm, based on the time information, energy consumption information, smoothness, and obstacle avoidance information of the robotic arm's execution trajectory; a first determining unit, configured to determine a first motion trajectory based on obstacle information encountered by the robotic arm during its movement according to the optimized motion trajectory, and control the robotic arm to move according to the first motion trajectory; a second determining unit, configured to determine a second motion trajectory based on trajectory deviation information encountered by the robotic arm during its movement according to the first motion trajectory, and control the robotic arm to move according to the second motion trajectory so that the inspection robot completes the inspection task.

[0019] Further, the first determining unit includes: a first detection module, used to detect whether there are obstacles during the movement of the robotic arm according to the optimized motion trajectory; a first adjustment module, used to adjust the optimized motion trajectory to obtain the first motion trajectory if there are obstacles during the movement of the robotic arm according to the optimized motion trajectory; and a first determining module, used to not adjust the optimized motion trajectory if there are no obstacles during the movement of the robotic arm according to the optimized motion trajectory, and to use the optimized motion trajectory as the first motion trajectory.

[0020] Furthermore, the calculation formula for the first adjustment module is as follows: Where N represents the degrees of freedom of the robotic arm, and η i The weights for optimizing the obstacle avoidance path for the i-th degree of freedom, Ω i Let X be the spatial region Ω where obstacles can exist in the i-th degree of freedom. i A vector at any position, d i (X) is the obstacle distance function at position X in the intelligent obstacle avoidance system, representing the distance of the obstacle from the robotic arm. Let k be an exponential decay function of the distance to the obstacle. i Determines the decay rate, For in the spatial region Ω i The average obstacle avoidance effect within.

[0021] Further, the second determining unit includes: a first processing module, configured to acquire the current position and posture information of the robotic arm, and determine the current motion trajectory of the robotic arm based on the current position and posture information of the robotic arm; a first judging module, configured to judge whether there is a deviation between the current motion trajectory of the robotic arm and the first motion trajectory; a second adjusting module, configured to adjust the current motion parameters of the robotic arm to obtain the second motion trajectory if there is a deviation between the current motion trajectory of the robotic arm and the first motion trajectory; and a second determining module, configured to not adjust the current motion parameters of the robotic arm and use the first motion trajectory as the second motion trajectory if there is no deviation between the current motion trajectory of the robotic arm and the first motion trajectory.

[0022] Furthermore, the calculation formula for the second adjustment module is as follows: Where N represents the degrees of freedom of the robotic arm. Let r be the weight parameter for the i-th degree of freedom. i s is the target position vector in the error compensation mechanism. i(t) is the actual position vector, representing the current position of the robotic arm obtained by fusing data collected from multiple sensors, ||r i (t)-s i (t)|| 2 The square of the Euclidean distance between the target position and the actual position represents the magnitude of the error.

[0023] Furthermore, the calculation formula for the multi-objective optimization model is as follows: Where N represents the degrees of freedom of the robotic arm, and α i Let t be the weight parameter for the i-th degree of freedom, and t be the time variable, t0-t f For the time range of trajectory planning, p i (t) represents the current position of the robotic arm in the i-th degree of freedom, which changes with time t, q i (t) represents the planned position of the robotic arm in the i-th degree of freedom, which changes with time t. The square root of the trajectory deviation represents the smoothness and optimization level of the trajectory.

[0024] Furthermore, the second determining unit includes: a first generating module, used to generate control commands based on the second motion trajectory; and a first control module, used to control the robotic arm to move according to the second motion trajectory based on the control commands, so that the inspection robot can complete the inspection task.

[0025] Furthermore, the device further includes: a first acquisition unit, configured to acquire information about the robotic arm moving along the second motion trajectory after controlling the robotic arm to move according to the control command, thereby obtaining motion state information of the robotic arm and environmental change information of the robotic arm; a first adjustment unit, configured to adjust the second motion trajectory based on the motion state information of the robotic arm and environmental change information of the robotic arm, thereby obtaining a target motion trajectory; and a first control unit, configured to control the robotic arm to move according to the target motion trajectory, so that the inspection robot can complete the inspection task.

[0026] Furthermore, the first processing unit includes: a first acquisition module, used to acquire data information related to the inspection robot collected by multiple sensors to obtain multiple data information, wherein the multiple sensors include: a vision sensor, a force sensor and an acceleration sensor; and a second processing module, used to perform fusion processing on the multiple data information to obtain environmental perception information and robotic arm status information of the inspection robot.

[0027] This application employs the following steps: acquiring environmental perception information and robotic arm status information of the inspection robot; initializing the trajectory parameters of the initial motion trajectory based on these information to obtain processed trajectory parameters, where the initial motion trajectory represents the initially planned motion trajectory of the robotic arm; inputting the processed trajectory parameters into a multi-objective optimization model for iterative calculation to obtain an optimized motion trajectory; and controlling the robotic arm to move according to the optimized motion trajectory. The multi-objective optimization model is constructed using a genetic algorithm or particle swarm optimization algorithm, based on the time information, energy consumption information, smoothness, and obstacle avoidance information of the robotic arm's execution trajectory; determining a first motion trajectory based on obstacle information encountered by the robotic arm during its movement along the optimized motion trajectory; and controlling the robotic arm to move according to the first motion trajectory; determining a second motion trajectory based on trajectory deviation information encountered by the robotic arm during its movement along the first motion trajectory; and controlling the robotic arm to move according to the second motion trajectory, thereby enabling the inspection robot to complete its inspection task. This solves the problem in related technologies where it is difficult to provide good path planning for inspection robots, resulting in poor performance in completing inspection tasks. By introducing multi-objective optimization algorithms, the time, energy consumption, and smoothness of the trajectory can be optimized simultaneously, ensuring high accuracy and efficiency of the trajectory; real-time error compensation can be achieved by using sensor data fusion and dynamic adjustment, ensuring accurate trajectory execution; and obstacle avoidance paths can be optimized by combining environmental modeling and real-time obstacle avoidance, ensuring the safe and efficient operation of the inspection robot in complex environments, thereby improving the effectiveness of the inspection robot in completing inspection tasks. Attached Figure Description

[0028] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0029] Figure 1 This is a flowchart of a trajectory planning and control method for an inspection robot according to an embodiment of this application;

[0030] Figure 2 This is a schematic diagram of the process steps of a six-degree-of-freedom robotic arm trajectory planning and control method for a track inspection robot in this embodiment;

[0031] Figure 3 This is a schematic diagram of the system framework of a six-degree-of-freedom robotic arm trajectory planning and control method for a track inspection robot in this embodiment;

[0032] Figure 4 This is a schematic diagram of the overall framework of a six-degree-of-freedom robotic arm trajectory planning and control method for a track inspection robot in this embodiment;

[0033] Figure 5 This is a schematic diagram of the trajectory planning and control device for an inspection robot provided according to an embodiment of this application;

[0034] Figure 6 This is a schematic diagram of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0035] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0036] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0037] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0038] It should be noted that all information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this disclosure are information and data authorized by the user or fully authorized by all parties. For example, this system has an interface with relevant users or organizations. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain the relevant information after receiving consent information from the aforementioned user or organization.

[0039] The present invention will now be described in conjunction with preferred implementation steps. Figure 1 This is a flowchart of the trajectory planning and control method for an inspection robot according to an embodiment of this application, such as... Figure 1 As shown, the method includes the following steps:

[0040] Step S101: Obtain the environmental perception information and robotic arm status information of the inspection robot, and initialize the trajectory parameters of the initial motion trajectory based on the environmental perception information and robotic arm status information of the inspection robot to obtain the processed trajectory parameters. The initial motion trajectory represents the initial planned motion trajectory of the robotic arm of the inspection robot.

[0041] For example, sensor data can be collected through sensor modules, and environmental perception information and robotic arm status information of the inspection robot can be generated based on the collected sensor data. Then, the trajectory planning parameters (corresponding to the trajectory parameters of the initial motion trajectory mentioned above) can be initialized based on the environmental perception information and robotic arm status information to obtain the initialized trajectory parameters (corresponding to the processed trajectory parameters mentioned above). The trajectory planning parameters can include parameter information such as target position, initial position, time constraints and energy consumption constraints.

[0042] Step S102: Input the processed trajectory parameters into the multi-objective optimization model for iterative calculation to obtain the optimized motion trajectory, and control the robotic arm to move according to the optimized motion trajectory. The multi-objective optimization model is a model constructed using a genetic algorithm or particle swarm optimization algorithm based on the time information, energy consumption information, smoothness and obstacle avoidance information of the robotic arm's execution trajectory.

[0043] For example, a multi-objective optimization model can be established using a genetic algorithm or a particle swarm optimization algorithm. This multi-objective optimization model can comprehensively consider time, energy consumption, smoothness, and obstacle avoidance performance. Then, the initial trajectory parameters (corresponding to the processed trajectory parameters) can be iteratively calculated using an optimization algorithm (corresponding to the multi-objective optimization model mentioned above) to obtain the globally optimal trajectory (corresponding to the optimized motion trajectory mentioned above). The robot arm of the inspection robot is then controlled to move according to the globally optimal trajectory (corresponding to the optimized motion trajectory mentioned above) generated by the multi-objective optimization model.

[0044] Step S103: Based on the obstacle information encountered by the robotic arm during its movement along the optimized motion trajectory, determine the first motion trajectory and control the robotic arm to move along the first motion trajectory.

[0045] For example, a visual sensor can be used to monitor in real time whether there are obstacles in the environment when the robotic arm moves along the globally optimal trajectory (corresponding to the optimized motion trajectory mentioned above); and based on the monitored situation of whether there are obstacles in the environment when the robotic arm moves along the globally optimal trajectory (corresponding to the optimized motion trajectory mentioned above), the first motion trajectory mentioned above is obtained; and then the robotic arm of the inspection robot is controlled to move along the first motion trajectory mentioned above.

[0046] Step S104: Based on the trajectory deviation information of the robotic arm during the movement of the first motion trajectory, determine the second motion trajectory, and control the robotic arm to move according to the second motion trajectory so that the inspection robot can complete the inspection task.

[0047] For example, a high-precision sensor can be used to monitor the current position and posture of the robotic arm as it moves along the first motion trajectory described above in real time. Then, based on the monitored current position and posture of the robotic arm, it can be determined whether there is a trajectory deviation between the current motion trajectory of the robotic arm and the first motion trajectory described above. Based on the existence of a trajectory deviation between the current motion trajectory of the robotic arm and the first motion trajectory described above, the second motion trajectory described above can be obtained. Then, the robotic arm of the inspection robot can be controlled to move along the second motion trajectory described above, and the inspection robot can complete the inspection task.

[0048] Through the steps S101 to S104 described above, the introduction of a multi-objective optimization algorithm can simultaneously optimize the trajectory's time, energy consumption, and smoothness, ensuring high accuracy and efficiency. It can also utilize sensor data fusion and dynamic adjustment to achieve real-time error compensation, ensuring accurate trajectory execution. Furthermore, it can combine environmental modeling and real-time obstacle avoidance to optimize obstacle avoidance paths, ensuring the safe and efficient operation of the inspection robot in complex environments, thereby improving the robot's performance in completing inspection tasks.

[0049] Optionally, in the trajectory planning and control method for the inspection robot provided in this application embodiment, obtaining the environmental perception information and robotic arm status information of the inspection robot includes: acquiring data information related to the inspection robot collected by multiple sensors to obtain multiple data information, wherein the multiple sensors include: a vision sensor, a force sensor and an acceleration sensor; and fusing the multiple data information to obtain the environmental perception information and robotic arm status information of the inspection robot.

[0050] For example, the data collection and preprocessing of inspection robots can include the following sub-steps:

[0051] Step 1: Activate the sensor module to collect data from the inspection robot's vision, force, and acceleration sensors (corresponding to the various sensors mentioned above) in real time (corresponding to the multiple data information mentioned above);

[0052] Step 2: The data from multiple sensors (corresponding to the multiple data information mentioned above) are fused through the data processing module to generate environmental perception information and robotic arm status information for the inspection robot.

[0053] In summary, by using various types of sensors to collect different data, it is possible to easily obtain data information from the inspection robot.

[0054] Optionally, in the trajectory planning and control method for the inspection robot provided in this application embodiment, the calculation formula for the multi-objective optimization model is: Where N is the degree of freedom of the robotic arm, α i Let t be the weight parameter for the i-th degree of freedom, and t be the time variable, t0-t f For the time range of trajectory planning, p i (t) represents the current position of the robotic arm in the i-th degree of freedom, which changes with time t, q i (t) represents the planned position of the robotic arm in the i-th degree of freedom, which changes with time t. The square root of the trajectory deviation represents the smoothness and optimization level of the trajectory.

[0055] For example, the multi-objective optimization formula can be:

[0056]

[0057] Where N represents the degrees of freedom of the robotic arm, and in this embodiment, the robotic arm of the inspection robot can be a six-degree-of-freedom robotic arm. Therefore, in this embodiment, N, used to represent the degrees of freedom of the robotic arm, can be 6, α i The weighting parameter for the sixth degree of freedom, t, is the time variable, t0-t f The time range for trajectory planning, p i (t) represents the current trajectory position vector of the robotic arm in the i-th degree of freedom, which changes with time t, q i (t) represents the planned trajectory position vector, which changes over time. The square root of the trajectory deviation represents the smoothness and optimization level of the trajectory.

[0058] In summary, by introducing a multi-objective optimization algorithm, we can simultaneously optimize the time, energy consumption, and smoothness of the trajectory, ensuring high accuracy and efficiency.

[0059] Optionally, in the trajectory planning and control method for the inspection robot provided in this application embodiment, determining the first motion trajectory based on obstacle information during the movement of the robotic arm according to the optimized motion trajectory includes: detecting whether there are obstacles during the movement of the robotic arm according to the optimized motion trajectory; if there are obstacles during the movement of the robotic arm according to the optimized motion trajectory, adjusting the optimized motion trajectory to obtain the first motion trajectory; if there are no obstacles during the movement of the robotic arm according to the optimized motion trajectory, not adjusting the optimized motion trajectory, and using the optimized motion trajectory as the first motion trajectory.

[0060] For example, when performing intelligent obstacle avoidance and path adjustment on the robotic arm of an inspection robot, the following sub-steps may be included:

[0061] Step 1: Monitor obstacles in the environment in real time using visual sensors;

[0062] Step 2: If an obstacle is detected, the intelligent obstacle avoidance algorithm adjusts the trajectory in real time (corresponding to the optimized motion trajectory mentioned above) to obtain the first motion trajectory mentioned above, and ensures that the inspection robot's robotic arm avoids the obstacle while maintaining path optimization; if no obstacle is detected, there is no need to adjust the current trajectory of the inspection robot's robotic arm (corresponding to the optimized motion trajectory mentioned above), and the inspection robot's robotic arm can continue to move according to the current running path (corresponding to the optimized motion trajectory mentioned above).

[0063] The above solution combines environmental modeling and real-time obstacle avoidance to optimize obstacle avoidance paths and ensure the safe and efficient operation of the inspection robot in complex environments.

[0064] Optionally, in the trajectory planning and control method for the inspection robot provided in this application embodiment, if there is an obstacle while the robotic arm is moving according to the optimized trajectory, the optimized trajectory is adjusted, and the calculation formula for the first trajectory is obtained as follows: Where N represents the degrees of freedom of the robotic arm, and η i The weights for optimizing the obstacle avoidance path for the i-th degree of freedom, Ω i Let X be the spatial region Ω where obstacles can exist in the i-th degree of freedom. i A vector at any position, d i (X) is the obstacle distance function at position X in the intelligent obstacle avoidance system, representing the distance between the obstacle and the robotic arm. Let k be an exponential decay function of the distance to the obstacle. i Determines the decay rate, For in the spatial region Ω i The average obstacle avoidance effect within.

[0065] For example, the obstacle avoidance path optimization formula is:

[0066]

[0067] Where N represents the degrees of freedom of the robotic arm, and in this embodiment, the robotic arm of the inspection robot can be a six-degree-of-freedom robotic arm. Therefore, in this embodiment, N, used to represent the degrees of freedom of the robotic arm, can be 6, η i Optimize the weights for the obstacle avoidance path in the sixth degree of freedom, Ω i Let X be the spatial region Ω where obstacles may exist in the i-th degree of freedom. i A vector at any position, d i(X) is the obstacle distance function at position X in the intelligent obstacle avoidance system, representing the distance between the obstacle and the robotic arm. Let k be an exponential decay function of the distance to the obstacle. i Determines the decay rate, For in the spatial region Ω i The average obstacle avoidance effect within the area.

[0068] In summary, by using the obstacle avoidance path optimization formula, the obstacle avoidance path of the inspection robot can be optimized, ensuring the safe and efficient operation of the inspection robot in complex environments.

[0069] Optionally, in the trajectory planning and control method for the inspection robot provided in this application embodiment, determining the second motion trajectory based on the trajectory deviation information of the robotic arm during its movement according to the first motion trajectory includes: acquiring the current position and posture information of the robotic arm, and determining the current motion trajectory executed by the robotic arm based on the current position and posture information of the robotic arm; determining whether there is a deviation between the current motion trajectory executed by the robotic arm and the first motion trajectory; if there is a deviation between the current motion trajectory executed by the robotic arm and the first motion trajectory, adjusting the current motion parameters of the robotic arm to obtain the second motion trajectory; if there is no deviation between the current motion trajectory executed by the robotic arm and the first motion trajectory, not adjusting the current motion parameters of the robotic arm, and using the first motion trajectory as the second motion trajectory.

[0070] For example, when performing real-time error detection and compensation on the robotic arm of an inspection robot, the following sub-steps may be included:

[0071] Step 1: High-precision sensors monitor the current position and attitude of the robotic arm in real time;

[0072] Step 2: Based on the monitored current position and posture of the robotic arm, determine whether there is a deviation between the current motion trajectory of the robotic arm and the planned trajectory (corresponding to the first motion trajectory mentioned above); if there is a deviation between the current motion trajectory of the robotic arm and the planned trajectory (corresponding to the first motion trajectory mentioned above), the deviation between the current position and the planned trajectory (corresponding to the first motion trajectory mentioned above) can be calculated through the error compensation module.

[0073] Step 3: Using a feedback control algorithm, adjust the motion parameters of the robotic arm in real time (corresponding to the current motion parameters of the robotic arm mentioned above) to compensate for trajectory deviations caused by environmental changes or errors in the robotic arm itself, and obtain the second motion trajectory mentioned above.

[0074] Step 4: If there is no deviation between the current motion trajectory of the inspection robot's robotic arm and the planned trajectory (corresponding to the first motion trajectory mentioned above), then there is no need to adjust the motion parameters of the robotic arm (corresponding to the current motion parameters of the robotic arm mentioned above), and the robotic arm of the inspection robot can continue to move according to the current motion trajectory.

[0075] The above solution enables real-time dynamic adjustment of the inspection robot's robotic arm, achieving real-time error compensation and ensuring accurate trajectory execution.

[0076] Optionally, in the trajectory planning and control method for the inspection robot provided in this application embodiment, if there is a deviation between the current motion trajectory executed by the robotic arm and the first motion trajectory, the current motion parameters of the robotic arm are adjusted, and the calculation formula for the second motion trajectory is obtained as follows: Where N represents the degrees of freedom of the robotic arm. Let r be the weight parameter for the i-th degree of freedom. i s is the target position vector in the error compensation mechanism. i (t) is the actual position vector, representing the current position of the robotic arm obtained by fusing data collected from multiple sensors, ||r i (t)-s i (t)|| 2 The square of the Euclidean distance between the target position and the actual position represents the magnitude of the error.

[0077] For example, the error compensation formula is:

[0078]

[0079] Where N represents the degrees of freedom of the robotic arm. In this embodiment, the robotic arm of the inspection robot can be a six-degree-of-freedom robotic arm, so N, used to represent the degrees of freedom of the robotic arm in this embodiment, can be 6. r is the weight parameter for the sixth degree of freedom. i s is the target position vector in the error compensation mechanism. i (t) represents the actual position vector, which is the current position of the robotic arm obtained through sensor data fusion. ||r i (t)-s i (t)|| 2 The square of the Euclidean distance between the target position and the actual position represents the magnitude of the error.

[0080] In summary, by using the error compensation formula, real-time compensation can be achieved for the error in the running path of the inspection robot's robotic arm, ensuring accurate execution of the trajectory.

[0081] Optionally, in the trajectory planning and control method for the inspection robot provided in this application embodiment, controlling the robotic arm to move according to the second motion trajectory so that the inspection robot can complete the inspection task includes: generating control commands according to the second motion trajectory; and controlling the robotic arm to move according to the control commands so that the inspection robot can complete the inspection task.

[0082] For example, control commands can be generated based on trajectory planning and error compensation results; then, the control module executes the control commands to drive the robotic arm to move along the optimized trajectory (corresponding to the second motion trajectory mentioned above), ensuring accurate trajectory execution.

[0083] Using the above method, the control module can accurately control the movement of the inspection robot's robotic arm according to the generated control commands.

[0084] Optionally, in the trajectory planning and control method for the inspection robot provided in this application embodiment, after controlling the robotic arm to move according to the second motion trajectory according to the control command, the method further includes: acquiring information about the robotic arm moving according to the second motion trajectory to obtain the motion state information of the robotic arm and the change information of the environment in which the robotic arm is located; adjusting the second motion trajectory according to the motion state information of the robotic arm and the change information of the environment in which the robotic arm is located to obtain the target motion trajectory; and controlling the robotic arm to move according to the target motion trajectory so that the inspection robot can complete the inspection task.

[0085] For example, the system can continuously provide feedback and optimize the movement trajectory of the inspection robot's robotic arm. For instance, the system can continuously monitor the robotic arm's motion state and environmental changes, adjust trajectory planning and error compensation strategies in real time, and obtain the aforementioned target motion trajectory; then, it controls the inspection robot's robotic arm to move according to the aforementioned target motion trajectory, enabling the inspection robot to complete its inspection task.

[0086] By using the above-mentioned solution, and by continuously feeding back and optimizing the movement trajectory of the robotic arm of the inspection robot through the system, it can be ensured that the robotic arm can complete the inspection task accurately and efficiently in a dynamic environment.

[0087] For example, this embodiment relates to the field of robotics technology, and in particular to a trajectory planning and control method for a six-degree-of-freedom robotic arm of a track-inspection robot. Furthermore, the trajectory planning and control method for a six-degree-of-freedom robotic arm of a track-inspection robot provided in this embodiment solves the problems of low trajectory accuracy and limited obstacle avoidance capability of the robotic arm in existing inspection robots.

[0088] For example, Figure 2 This is a schematic diagram of the flow steps of a six-degree-of-freedom robotic arm trajectory planning and control method for a track inspection robot in this embodiment, as shown below. Figure 2As shown, the trajectory planning and control method for a six-degree-of-freedom robotic arm of a track inspection robot in this embodiment includes the following steps:

[0089] S1: Data acquisition and preprocessing;

[0090] S2: Multi-objective optimization trajectory planning;

[0091] S3: Intelligent obstacle avoidance and path adjustment;

[0092] S4: Real-time error detection and compensation;

[0093] S5: Execution of control commands;

[0094] S6: System Feedback and Optimization.

[0095] For example, Figure 3 This is a schematic diagram of the system framework of a six-degree-of-freedom robotic arm trajectory planning and control method for a track inspection robot in this embodiment; Figure 4 This is a schematic diagram of the overall framework of a six-degree-of-freedom robotic arm trajectory planning and control method for a track inspection robot in this embodiment; as shown... Figure 3 and Figure 4 As shown, the trajectory planning and control method for a six-degree-of-freedom robotic arm of a track inspection robot in this embodiment specifically includes the following steps:

[0096] S1: Data acquisition and preprocessing;

[0097] Step S1 includes the following sub-steps:

[0098] Step 1: Start the sensor module to collect data from the vision, force, and acceleration sensors in real time;

[0099] Step 2: The data from multiple sensors are fused through the data processing module to generate environmental perception information and robotic arm status information;

[0100] S2: Multi-objective optimization trajectory planning;

[0101] Step S2 includes the following sub-steps:

[0102] Step 1: Based on environmental perception information, initialize trajectory planning parameters, including target position, initial position, time constraints, and energy consumption constraints;

[0103] Step 2: Use a genetic algorithm or particle swarm optimization algorithm to establish a multi-objective optimization model, comprehensively considering time, energy consumption, smoothness and obstacle avoidance performance;

[0104] Step 3: Obtain the globally optimal trajectory through iterative calculation using an optimization algorithm;

[0105] S3: Intelligent obstacle avoidance and path adjustment;

[0106] Step S3 includes the following sub-steps:

[0107] Step 1: Monitor obstacles in the environment in real time using visual sensors;

[0108] Step 2: If an obstacle is detected, the intelligent obstacle avoidance algorithm adjusts the trajectory in real time to ensure that the obstacle is avoided while maintaining path optimization;

[0109] S4: Real-time error detection and compensation;

[0110] Step S4 includes the following sub-steps:

[0111] Step 1: High-precision sensors monitor the current position and attitude of the robotic arm in real time;

[0112] Step 2: Calculate the deviation between the current position and the planned trajectory using the error compensation module;

[0113] Step 3: Using feedback control algorithms, adjust the motion parameters of the robotic arm in real time to compensate for trajectory deviations caused by environmental changes or errors in the robotic arm itself;

[0114] S5: Execution of control commands;

[0115] Step S5 includes the following sub-steps:

[0116] Step 1: Generate control commands based on trajectory planning and error compensation results;

[0117] Step 2: Execute control commands through the control module to drive the robotic arm to move along the optimized trajectory, ensuring precise trajectory execution;

[0118] S6: System Feedback and Optimization;

[0119] Step S6 includes the following sub-steps:

[0120] Step 1: The system continuously monitors the motion status of the robotic arm and changes in the environment, and adjusts the trajectory planning and error compensation strategies in real time;

[0121] Step 2: Through continuous feedback and optimization, ensure that the robotic arm can accurately and efficiently complete inspection tasks in dynamic environments.

[0122] Furthermore, the multi-objective optimization formula is:

[0123]

[0124] Where N represents the degrees of freedom of the robotic arm, which is 6 in this embodiment, α i The weighting parameter for the sixth degree of freedom, t, is the time variable, t0-tf The time range for trajectory planning, p i (t) represents the current trajectory position vector of the robotic arm in the i-th degree of freedom, which changes with time t, q i (t) represents the planned trajectory position vector, which changes over time. The square root of the trajectory deviation represents the smoothness and optimization level of the trajectory.

[0125] In addition, the error compensation formula is:

[0126]

[0127] Where N represents the degrees of freedom of the robotic arm, which is 6 in this embodiment. r is the weight parameter for the sixth degree of freedom. i s is the target position vector in the error compensation mechanism. i (t) represents the actual position vector, which is the current position of the robotic arm obtained through sensor data fusion. ||r i (t)-s i (t)|| 2 The square of the Euclidean distance between the target position and the actual position represents the magnitude of the error.

[0128] Furthermore, the obstacle avoidance path optimization formula is:

[0129]

[0130] Where N represents the degrees of freedom of the robotic arm, which is 6 in this embodiment, and η i Optimize the weights for the obstacle avoidance path in the sixth degree of freedom, Ω i Let X be the spatial region Ω where obstacles may exist in the i-th degree of freedom. i A vector at any position, d i (X) is the obstacle distance function at position X in the intelligent obstacle avoidance system, representing the distance between the obstacle and the robotic arm. Let k be an exponential decay function of the distance to the obstacle. i Determines the decay rate, For in the spatial region Ω i The average obstacle avoidance effect within the area.

[0131] Therefore, this embodiment provides a trajectory planning and control method for a six-degree-of-freedom robotic arm of a track inspection robot, which includes the following steps: S1: data acquisition and preprocessing; S2: multi-objective optimized trajectory planning; S3: intelligent obstacle avoidance and path adjustment; S4: real-time error detection and compensation; S5: execution of control commands; S6: system feedback and optimization. Thus, by introducing a multi-objective optimization algorithm, this embodiment can simultaneously optimize the trajectory's time, energy consumption, and smoothness, ensuring high accuracy and efficiency; it can utilize sensor data fusion and dynamic adjustment to achieve real-time error compensation, ensuring accurate trajectory execution; and it can combine environmental modeling and real-time obstacle avoidance to optimize obstacle avoidance paths, ensuring the robot's safe and efficient operation in complex environments.

[0132] For example, to verify the effectiveness and innovation of the trajectory planning and control method for a six-degree-of-freedom robotic arm of a track inspection robot in this embodiment, a series of experiments were designed. The experiments used a track inspection robot with a six-degree-of-freedom robotic arm. During the experiments, the robotic arm needed to execute a predetermined trajectory and respond to dynamic changes in the environment. The experiments compared the effects of using traditional trajectory planning methods and using the trajectory planning method in this embodiment.

[0133] Furthermore, the hardware used in the experiment included a robotic arm, sensors, and a controller. The software used included traditional trajectory planning algorithm software, the multi-objective optimization trajectory planning and control software used in this embodiment, and data acquisition and analysis tools. Additionally, data recording included the robotic arm's actual motion trajectory, error conditions, obstacle avoidance success rate, and energy consumption data throughout the entire process. Table 1 records the data obtained from the experiment.

[0134] Table 1

[0135]

[0136] By comparing the experimental data recorded in Table 1, the following conclusions can be drawn:

[0137] Average trajectory deviation: The method in this embodiment significantly outperforms traditional methods in terms of accuracy in trajectory planning and execution. Due to the use of multi-objective optimization and real-time error compensation mechanisms, the method in this embodiment can execute the predetermined trajectory more accurately.

[0138] Obstacle avoidance success rate: The method in this embodiment has advantages in obstacle avoidance path optimization. By combining intelligent obstacle avoidance and path optimization, the method in this embodiment can avoid obstacles more efficiently, ensuring the safe movement of the robotic arm;

[0139] Energy consumption: The method in this embodiment improves energy efficiency by optimizing energy consumption parameters so that the robotic arm consumes less energy during the execution of its trajectory.

[0140] Trajectory execution time: The method in this embodiment also performs well in terms of time optimization. By comprehensively considering time factors, the robotic arm can complete the predetermined task faster.

[0141] Therefore, the method in this embodiment outperforms traditional methods in several key indicators of trajectory planning. Through multi-objective optimization, real-time error compensation, and intelligent obstacle avoidance path optimization, the method in this embodiment achieves high precision, high efficiency, and high safety in trajectory planning. The method provided in this embodiment not only improves the performance of the track inspection robot but also provides more reliable technical support for practical applications.

[0142] In summary, the trajectory planning and control method for the inspection robot provided in this application acquires the environmental perception information and robotic arm state information of the inspection robot, and initializes the trajectory parameters of the initial motion trajectory based on the environmental perception information and robotic arm state information to obtain processed trajectory parameters. The initial motion trajectory represents the initially planned motion trajectory of the robotic arm of the inspection robot. The processed trajectory parameters are then input into a multi-objective optimization model for iterative calculation to obtain an optimized motion trajectory. The robotic arm is then controlled to move according to the optimized motion trajectory. The multi-objective optimization model employs a genetic algorithm or particle swarm optimization. The algorithm is based on a model constructed using time, energy consumption, smoothness, and obstacle avoidance information of the robotic arm's trajectory. It determines a first motion trajectory based on obstacle information encountered during the robotic arm's movement along the optimized trajectory and controls the robotic arm to move along this trajectory. Then, based on trajectory deviation information during the first motion trajectory, a second motion trajectory is determined, and the robotic arm is controlled to move along this second trajectory, enabling the inspection robot to complete its inspection task. This solves the problem in related technologies where it is difficult to provide good path planning for inspection robots, resulting in poor performance. By introducing a multi-objective optimization algorithm, the time, energy consumption, and smoothness of the trajectory can be optimized simultaneously, ensuring high accuracy and efficiency. Real-time error compensation can be achieved through sensor data fusion and dynamic adjustment, ensuring accurate trajectory execution. Furthermore, by combining environmental modeling and real-time obstacle avoidance, the obstacle avoidance path can be optimized, ensuring the safe and efficient operation of the inspection robot in complex environments, thereby improving the robot's performance in completing inspection tasks.

[0143] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0144] This application also provides a trajectory planning and control device for an inspection robot. It should be noted that the trajectory planning and control device for the inspection robot in this application can be used to execute the trajectory planning and control method for the inspection robot provided in this application. The trajectory planning and control device for the inspection robot provided in this application will be described below.

[0145] Figure 5 This is a schematic diagram of a trajectory planning and control device for an inspection robot according to an embodiment of this application. Figure 5 As shown, the device includes: a first processing unit 501, a second processing unit 502, a first determining unit 503, and a second determining unit 504.

[0146] Specifically, the first processing unit 501 is used to acquire the environmental perception information and the robotic arm status information of the inspection robot, and to initialize the trajectory parameters of the initial motion trajectory based on the environmental perception information and the robotic arm status information of the inspection robot to obtain the processed trajectory parameters. The initial motion trajectory is represented as the initial planned motion trajectory of the robotic arm of the inspection robot.

[0147] The second processing unit 502 is used to input the processed trajectory parameters into the multi-objective optimization model for iterative calculation to obtain the optimized motion trajectory, and control the robotic arm to move according to the optimized motion trajectory. The multi-objective optimization model is a model constructed using a genetic algorithm or a particle swarm optimization algorithm, based on the time information, energy consumption information, smoothness and obstacle avoidance information of the robotic arm's execution trajectory.

[0148] The first determining unit 503 is used to determine the first motion trajectory based on the obstacle information encountered by the robotic arm during its movement according to the optimized motion trajectory, and to control the robotic arm to move according to the first motion trajectory.

[0149] The second determining unit 504 is used to determine the second motion trajectory based on the trajectory deviation information of the robotic arm during the process of moving according to the first motion trajectory, and to control the robotic arm to move according to the second motion trajectory so that the inspection robot can complete the inspection task.

[0150] In summary, the trajectory planning and control device for the inspection robot provided in this application embodiment acquires the environmental perception information and robotic arm status information of the inspection robot through the first processing unit 501, and initializes the trajectory parameters of the initial motion trajectory based on the environmental perception information and robotic arm status information to obtain the processed trajectory parameters. The initial motion trajectory represents the initially planned motion trajectory of the robotic arm of the inspection robot. The second processing unit 502 inputs the processed trajectory parameters into a multi-objective optimization model for iterative calculation to obtain the optimized motion trajectory, and controls the robotic arm to move according to the optimized motion trajectory. The multi-objective optimization model employs a genetic algorithm or particle swarm optimization. The algorithm is based on a model constructed using time, energy consumption, smoothness, and obstacle avoidance information of the robotic arm's trajectory. A first determining unit 503 determines a first motion trajectory based on obstacle information encountered during the robotic arm's movement along the optimized trajectory and controls the robotic arm to move along that trajectory. A second determining unit 504 determines a second motion trajectory based on trajectory deviation information encountered during the robotic arm's movement along the first trajectory and controls the robotic arm to move along that trajectory, enabling the inspection robot to complete its inspection task. This solves the problem in related technologies where it is difficult to provide good path planning for inspection robots, resulting in poor performance. By introducing a multi-objective optimization algorithm, the time, energy consumption, and smoothness of the trajectory can be optimized simultaneously, ensuring high accuracy and efficiency. Sensor data fusion and dynamic adjustment can be used to achieve real-time error compensation, ensuring accurate trajectory execution. Combining environmental modeling and real-time obstacle avoidance optimizes the obstacle avoidance path, ensuring the safe and efficient operation of the inspection robot in complex environments, thereby improving the robot's performance in completing inspection tasks.

[0151] Optionally, in the trajectory planning and control device for the inspection robot provided in this application embodiment, the first determining unit includes: a first detection module, used to detect whether there are obstacles during the process of the robotic arm moving according to the optimized motion trajectory; a first adjustment module, used to adjust the optimized motion trajectory to obtain a first motion trajectory if there are obstacles during the process of the robotic arm moving according to the optimized motion trajectory; and a first determining module, used to not adjust the optimized motion trajectory if there are no obstacles during the process of the robotic arm moving according to the optimized motion trajectory, and to use the optimized motion trajectory as the first motion trajectory.

[0152] Optionally, in the trajectory planning and control device for the inspection robot provided in this application embodiment, the calculation formula of the first adjustment module is: Where N represents the degrees of freedom of the robotic arm, and η i The weights for optimizing the obstacle avoidance path for the i-th degree of freedom, Ω iLet X be the spatial region Ω in which obstacles can exist in the i-th degree of freedom. i A vector at any position, d i (X) is the obstacle distance function at position X in the intelligent obstacle avoidance system, representing the distance between the obstacle and the robotic arm. Let k be an exponential decay function of the distance to the obstacle. i Determines the decay rate, For in the spatial region Ω i The average obstacle avoidance effect within the area.

[0153] Optionally, in the trajectory planning and control device for the inspection robot provided in this application embodiment, the second determining unit includes: a first processing module, used to acquire the current position and posture information of the robotic arm, and determine the current motion trajectory executed by the robotic arm based on the current position and posture information of the robotic arm; a first judging module, used to judge whether there is a deviation between the current motion trajectory executed by the robotic arm and the first motion trajectory; a second adjusting module, used to adjust the current motion parameters of the robotic arm to obtain a second motion trajectory if there is a deviation between the current motion trajectory executed by the robotic arm and the first motion trajectory; and a second determining module, used to not adjust the current motion parameters of the robotic arm and use the first motion trajectory as the second motion trajectory if there is no deviation between the current motion trajectory executed by the robotic arm and the first motion trajectory.

[0154] Optionally, in the trajectory planning and control device for the inspection robot provided in this application embodiment, the calculation formula of the second adjustment module is: Where N represents the degrees of freedom of the robotic arm. Let r be the weight parameter for the i-th degree of freedom. i s is the target position vector in the error compensation mechanism. i (t) is the actual position vector, representing the current position of the robotic arm obtained by fusing data collected from multiple sensors, ||r i (t)-s i (t)|| 2 The square of the Euclidean distance between the target position and the actual position represents the magnitude of the error.

[0155] Optionally, in the trajectory planning and control device for the inspection robot provided in this application embodiment, the calculation formula for the multi-objective optimization model is: Where N is the degree of freedom of the robotic arm, α i Let t be the weight parameter for the i-th degree of freedom, and t be the time variable, t0-t f For the time range of trajectory planning, p i (t) represents the current position of the robotic arm in the i-th degree of freedom, which changes with time t, q i(t) represents the planned position of the robotic arm in the i-th degree of freedom, which changes with time t. The square root of the trajectory deviation represents the smoothness and optimization level of the trajectory.

[0156] Optionally, in the trajectory planning and control device for the inspection robot provided in this application embodiment, the second determining unit includes: a first generating module, used to generate control commands based on the second motion trajectory; and a first control module, used to control the robotic arm to move according to the second motion trajectory based on the control commands, so that the inspection robot can complete the inspection task.

[0157] Optionally, in the trajectory planning and control device for the inspection robot provided in this application embodiment, the device further includes: a first acquisition unit, used to acquire information about the robotic arm moving along the second motion trajectory after controlling the robotic arm to move according to the control command, thereby obtaining motion state information of the robotic arm and change information of the environment in which the robotic arm is located; a first adjustment unit, used to adjust the second motion trajectory according to the motion state information of the robotic arm and the change information of the environment in which the robotic arm is located, to obtain a target motion trajectory; and a first control unit, used to control the robotic arm to move along the target motion trajectory so that the inspection robot can complete the inspection task.

[0158] Optionally, in the trajectory planning and control device for the inspection robot provided in this application embodiment, the first processing unit includes: a first acquisition module, used to acquire data information related to the inspection robot collected by multiple sensors to obtain multiple data information, wherein the multiple sensors include: a vision sensor, a force sensor and an acceleration sensor; and a second processing module, used to perform fusion processing on the multiple data information to obtain environmental perception information and robotic arm status information of the inspection robot.

[0159] The trajectory planning and control device of the inspection robot includes a processor and a memory. The first processing unit 501, the second processing unit 502, the first determining unit 503, and the second determining unit 504 mentioned above are all stored in the memory as program units. The processor executes the program units stored in the memory to realize the corresponding functions.

[0160] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters can improve the efficiency of the inspection robot in completing its inspection tasks.

[0161] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0162] This invention provides a computer-readable storage medium storing a program thereon, which, when executed by a processor, implements a trajectory planning and control method for the inspection robot.

[0163] This invention provides a processor for running a program, wherein the program executes a trajectory planning and control method for the inspection robot.

[0164] like Figure 6 As shown, this embodiment of the invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: acquiring environmental perception information and robotic arm status information of the inspection robot, and initializing the trajectory parameters of the initial motion trajectory based on the environmental perception information and robotic arm status information of the inspection robot to obtain processed trajectory parameters, wherein the initial motion trajectory represents the initial planned motion trajectory of the robotic arm of the inspection robot; inputting the processed trajectory parameters into a multi-objective optimization model for iterative calculation to obtain an optimized motion trajectory, and controlling the robotic arm to move according to the optimized motion trajectory, wherein the multi-objective optimization model is a model constructed using a genetic algorithm or a particle swarm optimization algorithm based on the time information, energy consumption information, smoothness, and obstacle avoidance information of the robotic arm's execution trajectory; determining a first motion trajectory based on obstacle information during the robotic arm's movement according to the optimized motion trajectory, and controlling the robotic arm to move according to the first motion trajectory; determining a second motion trajectory based on trajectory deviation information during the robotic arm's movement according to the first motion trajectory, and controlling the robotic arm to move according to the second motion trajectory so that the inspection robot completes the inspection task.

[0165] When the processor executes the program, it also performs the following steps: determining the first motion trajectory based on obstacle information during the process of the robotic arm moving according to the optimized motion trajectory includes: detecting whether there are obstacles during the process of the robotic arm moving according to the optimized motion trajectory; if there are obstacles during the process of the robotic arm moving according to the optimized motion trajectory, the optimized motion trajectory is adjusted to obtain the first motion trajectory; if there are no obstacles during the process of the robotic arm moving according to the optimized motion trajectory, the optimized motion trajectory is not adjusted, and the optimized motion trajectory is used as the first motion trajectory.

[0166] When the processor executes the program, it also performs the following steps: If there are obstacles while the robotic arm is moving along the optimized trajectory, the optimized trajectory is adjusted, and the calculation formula for the first trajectory is obtained as follows: Where N represents the degrees of freedom of the robotic arm, and η iThe weights for optimizing the obstacle avoidance path for the i-th degree of freedom, Ω i Let X be the spatial region Ω where obstacles can exist in the i-th degree of freedom. i A vector at any position, d i (X) is the obstacle distance function at position X in the intelligent obstacle avoidance system, representing the distance between the obstacle and the robotic arm. Let k be an exponential decay function of the distance to the obstacle. i Determines the decay rate, For in the spatial region Ω i The average obstacle avoidance effect within.

[0167] When the processor executes the program, it also performs the following steps: determining the second motion trajectory based on the trajectory deviation information of the robotic arm during its movement according to the first motion trajectory includes: acquiring the current position and posture information of the robotic arm, and determining the current motion trajectory of the robotic arm based on the current position and posture information of the robotic arm; determining whether there is a deviation between the current motion trajectory of the robotic arm and the first motion trajectory; if there is a deviation between the current motion trajectory of the robotic arm and the first motion trajectory, adjusting the current motion parameters of the robotic arm to obtain the second motion trajectory; if there is no deviation between the current motion trajectory of the robotic arm and the first motion trajectory, not adjusting the current motion parameters of the robotic arm, and using the first motion trajectory as the second motion trajectory.

[0168] When the processor executes the program, it also performs the following steps: If there is a deviation between the current motion trajectory of the robotic arm and the first motion trajectory, the current motion parameters of the robotic arm are adjusted, and the calculation formula for the second motion trajectory is obtained as follows: Where N represents the degrees of freedom of the robotic arm. Let r be the weight parameter for the i-th degree of freedom. i s is the target position vector in the error compensation mechanism. i (t) is the actual position vector, representing the current position of the robotic arm obtained by fusing data collected from multiple sensors, ||r i (t)-s i (t)|| 2 The square of the Euclidean distance between the target position and the actual position represents the magnitude of the error.

[0169] When the processor executes the program, it also performs the following steps: The calculation formula for the multi-objective optimization model is: Where N is the degree of freedom of the robotic arm, α i Let t be the weight parameter for the i-th degree of freedom, and t be the time variable, t0-t f For the time range of trajectory planning, p i(t) represents the current position of the robotic arm in the i-th degree of freedom, which changes with time t, q i (t) represents the planned position of the robotic arm in the i-th degree of freedom, which changes with time t. The square root of the trajectory deviation represents the smoothness and optimization level of the trajectory.

[0170] When the processor executes the program, it also performs the following steps: controlling the robotic arm to move according to the second motion trajectory so that the inspection robot can complete the inspection task, including: generating control instructions according to the second motion trajectory; and controlling the robotic arm to move according to the control instructions so that the inspection robot can complete the inspection task.

[0171] When the processor executes the program, it also performs the following steps: after controlling the robotic arm to move according to the second motion trajectory according to the control instructions, it further includes: acquiring information about the robotic arm moving according to the second motion trajectory, obtaining the motion state information of the robotic arm and the change information of the environment in which the robotic arm is located; adjusting the second motion trajectory according to the motion state information of the robotic arm and the change information of the environment in which the robotic arm is located, to obtain the target motion trajectory; and controlling the robotic arm to move according to the target motion trajectory so that the inspection robot can complete the inspection task.

[0172] When the processor executes the program, it also performs the following steps: acquiring environmental perception information and robotic arm status information of the inspection robot, including: acquiring data information related to the inspection robot collected by multiple sensors to obtain multiple data information, wherein the multiple sensors include: vision sensor, force sensor and acceleration sensor; fusing the multiple data information to obtain environmental perception information and robotic arm status information of the inspection robot.

[0173] The devices mentioned in this article can be servers, PCs, tablets, mobile phones, etc.

[0174] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program with the following steps: acquiring environmental perception information and robotic arm status information of the inspection robot, and initializing the trajectory parameters of the initial motion trajectory based on the environmental perception information and robotic arm status information of the inspection robot to obtain processed trajectory parameters, wherein the initial motion trajectory represents the initial planned motion trajectory of the robotic arm of the inspection robot; inputting the processed trajectory parameters into a multi-objective optimization model for iterative calculation to obtain an optimized motion trajectory, and controlling the robotic arm to move according to the optimized motion trajectory, wherein the multi-objective optimization model is a model constructed using a genetic algorithm or a particle swarm optimization algorithm, based on the time information, energy consumption information, smoothness, and obstacle avoidance information of the robotic arm's execution trajectory; determining a first motion trajectory based on obstacle information during the robotic arm's movement according to the optimized motion trajectory, and controlling the robotic arm to move according to the first motion trajectory; determining a second motion trajectory based on trajectory deviation information during the robotic arm's movement according to the first motion trajectory, and controlling the robotic arm to move according to the second motion trajectory so that the inspection robot completes the inspection task.

[0175] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0176] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0177] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0178] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0179] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0180] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0181] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0182] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0183] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0184] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A trajectory planning and control method for an inspection robot, characterized in that, include: The environmental perception information and robotic arm status information of the inspection robot are acquired, and the trajectory parameters of the initial motion trajectory are initialized based on the environmental perception information and robotic arm status information of the inspection robot to obtain the processed trajectory parameters. The initial motion trajectory is represented by the initial planned motion trajectory of the robotic arm of the inspection robot. The processed trajectory parameters are input into a multi-objective optimization model for iterative calculation to obtain an optimized motion trajectory. The robotic arm is then controlled to move according to the optimized motion trajectory. The multi-objective optimization model is a model constructed using a genetic algorithm or a particle swarm optimization algorithm, based on the time information, energy consumption information, smoothness, and obstacle avoidance information of the robotic arm's execution trajectory. Based on the obstacle information encountered by the robotic arm during its movement along the optimized motion trajectory, a first motion trajectory is determined, and the robotic arm is controlled to move along the first motion trajectory. Based on the trajectory deviation information of the robotic arm during the movement of the first motion trajectory, a second motion trajectory is determined, and based on the second motion trajectory, the robotic arm is controlled to move so that the inspection robot can complete the inspection task. The determination of the first motion trajectory, based on obstacle information encountered by the robotic arm during its movement according to the optimized motion trajectory, includes: The system detects whether there are obstacles while the robotic arm is moving according to the optimized motion trajectory. If there is an obstacle while the robotic arm is moving according to the optimized motion trajectory, the optimized motion trajectory is adjusted to obtain the first motion trajectory. If there are no obstacles during the movement of the robotic arm according to the optimized motion trajectory, the optimized motion trajectory will not be adjusted, and the optimized motion trajectory will be used as the first motion trajectory. If an obstacle exists during the movement of the robotic arm along the optimized trajectory, the optimized trajectory is adjusted, and the calculation formula for the first trajectory is obtained as follows: ； in, Let be the degrees of freedom of the robotic arm. For the first The weights for obstacle avoidance path optimization with each degree of freedom. In the first A spatial region in which obstacles can exist in a degree of freedom. For spatial regions A vector at any position, Location in intelligent obstacle avoidance system The obstacle distance function represents the distance between the obstacle and the robotic arm. The parameter is an exponential decay function of the obstacle distance. Determines the decay rate, In the space region The average obstacle avoidance effect within the area.

2. The method according to claim 1, characterized in that, Based on the trajectory deviation information of the robotic arm during its movement according to the first motion trajectory, the second motion trajectory is determined as follows: The current position and posture information of the robotic arm are obtained, and the current motion trajectory of the robotic arm is determined based on the current position and posture information of the robotic arm. Determine whether there is a deviation between the current motion trajectory executed by the robotic arm and the first motion trajectory; If there is a deviation between the current motion trajectory executed by the robotic arm and the first motion trajectory, the current motion parameters of the robotic arm are adjusted to obtain the second motion trajectory; If there is no deviation between the current motion trajectory of the robotic arm and the first motion trajectory, then the current motion parameters of the robotic arm are not adjusted, and the first motion trajectory is used as the second motion trajectory.

3. The method according to claim 2, characterized in that, If there is a deviation between the current motion trajectory executed by the robotic arm and the first motion trajectory, the current motion parameters of the robotic arm are adjusted, and the calculation formula for the second motion trajectory is obtained as follows: ； in, Let be the degrees of freedom of the robotic arm. For the first Weight parameters for each degree of freedom This refers to the target position vector in the error compensation mechanism. The actual position vector represents the current position of the robotic arm, obtained by fusing data collected from multiple sensors. The square of the Euclidean distance between the target position and the actual position represents the magnitude of the error.

4. The method according to claim 1, characterized in that, The calculation formula for the multi-objective optimization model is as follows: ； in, Let be the degrees of freedom of the robotic arm. For the first Weight parameters for each degree of freedom For time variables, For the time range of trajectory planning, Indicates that the robotic arm is in the first... The current position in each degree of freedom, over time. change, Indicates that the robotic arm is in the first... The planned location in a degree of freedom, over time change, The square root of the trajectory deviation represents the smoothness and optimization level of the trajectory.

5. The method according to claim 1, characterized in that, Based on the second motion trajectory, controlling the robotic arm to move so that the inspection robot completes the inspection task includes: Based on the second motion trajectory, control commands are generated; According to the control command, the robotic arm is controlled to move along the second motion trajectory so that the inspection robot can complete the inspection task.

6. The method according to claim 5, characterized in that, After controlling the robotic arm to move along the second motion trajectory according to the control command, the method further includes: Information on the movement of the robotic arm according to the second motion trajectory is obtained to obtain the motion state information of the robotic arm and the change information of the environment in which the robotic arm is located; Based on the motion state information of the robotic arm and the change information of the environment in which the robotic arm is located, the second motion trajectory is adjusted to obtain the target motion trajectory; The robotic arm is controlled to move according to the target trajectory so that the inspection robot can complete the inspection task.

7. The method according to claim 1, characterized in that, The environmental perception information and robotic arm status information obtained by the inspection robot include: Data related to the inspection robot collected by multiple sensors are obtained to obtain multiple data information, wherein the multiple sensors include: a vision sensor, a force sensor and an acceleration sensor; The multiple data information are fused to obtain the environmental perception information and robotic arm status information of the inspection robot.

8. A trajectory planning and control device for an inspection robot, characterized in that, The method applied to any one of claims 1 to 7 includes: The first processing unit is used to acquire the environmental perception information and robotic arm status information of the inspection robot, and initialize the trajectory parameters of the initial motion trajectory based on the environmental perception information and robotic arm status information of the inspection robot to obtain the processed trajectory parameters. The initial motion trajectory is represented by the initial planned motion trajectory of the robotic arm of the inspection robot. The second processing unit is used to input the processed trajectory parameters into a multi-objective optimization model for iterative calculation to obtain an optimized motion trajectory, and control the robotic arm to move according to the optimized motion trajectory. The multi-objective optimization model is a model constructed using a genetic algorithm or a particle swarm optimization algorithm, based on the time information, energy consumption information, smoothness and obstacle avoidance information of the robotic arm's execution trajectory. The first determining unit is used to determine a first motion trajectory based on obstacle information encountered by the robotic arm during its movement according to the optimized motion trajectory, and to control the robotic arm to move according to the first motion trajectory. The second determining unit is used to determine the second motion trajectory based on the trajectory deviation information of the robotic arm during the movement of the first motion trajectory, and to control the robotic arm to move according to the second motion trajectory so that the inspection robot can complete the inspection task.

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