Mechanical arm obstacle avoidance method and system based on state feedback

Abstract

The invention relates to the technical field of automation equipment, and discloses a mechanical arm obstacle avoidance method and system based on state feedback, which are used for solving the problem that a mechanical arm cannot accurately select an optimal preferential obstacle to avoid when a plurality of obstacles have the same distance from the mechanical arm. Obstacles around the mechanical arm are measured, the size and shape conditions of the obstacles are obtained, obstacle shape obstruction coefficients are obtained through analysis, movement data of the obstacles are obtained, obstacle movement influence coefficients are obtained through evaluation, priority evasion indexes are obtained through comprehensive analysis, and the obstacles which are preferentially evaded are selected according to the priority evasion indexes. The obstacle avoidance distance is adjusted, the actual obstacle avoidance distance is obtained, and when the distance between the mechanical arm and the obstacle reaches the actual obstacle avoidance distance, a path for avoiding collision with the obstacle is generated; new obstacle information changes are continuously detected, the path is adjusted in real time, the optimal obstacle avoidance route is effectively obtained, and the working efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automation equipment, and more specifically to a method and system for avoiding obstacles of a robotic arm based on state feedback. Background Art

[0002] Robotic arm obstacle avoidance is a technology that detects obstacles in the environment through sensors and adjusts the robot's motion trajectory through path planning or control algorithms to avoid collisions with obstacles. It can generate a safe path based on the current robot's target task, ensuring that the robot can complete its work efficiently and safely in complex or dynamic environments.

[0003] For example, the robot arm obstacle avoidance method and robot arm obstacle avoidance system announced by the invention patent with announcement number: CN115723121A include: a modeling step, a step of collecting and evaluating coordinates, a step of obtaining control variables, a step of establishing an occupancy function, and a step of finding an obstacle avoidance posture. The present invention pre-stores the data obtained from executing the modeling step, the step of establishing evaluation coordinates, the step of obtaining control variables, and the step of establishing an occupancy function into a database, so that when the subsequent robot arm performs a task, it can quickly evaluate whether a collision will occur. If the evaluation shows that a collision will occur, the step of finding an obstacle avoidance posture is executed to avoid the obstacle. The present invention adopts a non-contact anti-collision design, which can improve the shortcomings faced by the existing contact anti-collision design.

[0004] For example, the dual neural network obstacle avoidance control method for an omnidirectional mobile manipulator announced by the invention patent with the announcement number CN117182912A includes: designing the expected motion trajectory of the speed level of the omnidirectional mobile manipulator according to the designer's needs, proposing a hybrid solution for omnidirectional mobile manipulator trajectory tracking and obstacle avoidance with joint and joint speed restrictions based on the kinematic model of the omnidirectional mobile manipulator, and designing the original dual neural network based on linear variational inequalities. The present invention solves the problem of stable and accurate tracking expectations of omnidirectional mobile manipulators in environments with static or moving point obstacles, and verifies the effectiveness of the algorithm through simulation experiments.

[0005] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0006] In the prior art, obstacle avoidance by a robotic arm is generally performed by detecting the distance between the robotic arm and the obstacle and performing obstacle avoidance processing based on the distance. However, if there are two or more obstacles at the same distance from the robotic arm, the robotic arm cannot accurately select the best obstacle to avoid first, resulting in the inability to generate the best obstacle avoidance route, thereby reducing work efficiency. Summary of the invention

[0007] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and system for robot arm obstacle avoidance based on state feedback to solve the problems existing in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for avoiding obstacles of a robot arm based on state feedback comprises the following steps: Step 1: When the robot arm is in operation, a laser radar is used to measure obstacles around the robot arm to obtain the volume and shape of the obstacles, and the obstacle shape obstruction coefficient is obtained according to the volume and shape of the obstacles; Step 2: The movement data of the obstacle is obtained by using the laser radar, and the movement data includes the movement speed and the movement direction, and the obstacle movement influence coefficient is obtained according to the movement data evaluation; Step 3: The method for obtaining the priority avoidance index according to the obstacle shape obstruction coefficient and the obstacle movement influence coefficient is PA=a1×SO+a2×MI, wherein PA represents the priority avoidance index, and SO represents the obstacle shape shape obstruction coefficient, MI is represented as the obstacle movement influence coefficient, a1 and a2 are represented as the weight coefficients of the obstacle shape obstruction coefficient and the obstacle movement influence coefficient; Step 4: Select the obstacle to be avoided first according to the priority avoidance index; Step 5: Adjust the obstacle avoidance distance according to the priority avoidance index of the obstacle to be avoided first, and obtain the actual obstacle avoidance distance; Step 6: When the robot arm reaches the actual obstacle avoidance distance from the obstacle, use the path planning algorithm according to the obstacle to be avoided first, and generate a path to avoid collision with the obstacle; Step 7: During the movement of the robot arm to avoid obstacles according to the path, continuously detect new obstacle information changes and adjust the path in real time.

[0010] Preferably, the steps of using a laser radar to measure obstacles around the robot arm to obtain the volume and shape of the obstacles are as follows: the laser radar generates point cloud data of the obstacle by emitting laser pulses to the measurement points and measuring the time difference of the return signals; using the laser radar to perform multi-point measurement on the obstacle to obtain the obstacle point cloud data; connecting each point in the obstacle point cloud data to reconstruct the three-dimensional shape of the obstacle and obtain the volume of the obstacle; dividing the three-dimensional shape of the obstacle into n three-dimensional spaces on average, each of which is an equal-sized cube, recorded as an obstacle subspace, screening out obstacle subspaces with surfaces in contact with the outside world, recorded as effective obstacle subspaces; calculating the spatial irregularity of each effective obstacle subspace, clustering the spatial irregularity of each effective obstacle subspace using the K-means clustering method, and calculating the obstacle irregularity based on the clustering results.

[0011] Preferably, the step of calculating the spatial irregularity of each effective obstacle subspace is: obtaining the actual volume of the obstacle in each effective obstacle subspace, obtaining the surface area of ​​the obstacle in each effective obstacle subspace that is actually in contact with the outside world, and obtaining the body surface ratio according to the actual volume and the actual surface area in contact with the outside world; obtaining the sphericity according to the actual volume and the actual surface area in contact with the outside world; the method for obtaining the spatial irregularity according to the body surface ratio and the sphericity is: Where SI represents spatial irregularity, rt represents body surface ratio, and sy represents sphericity.

[0012] Preferably, the steps of clustering the spatial irregularity of each effective obstacle subspace using the K-means clustering method are as follows: Step 1.1: Using spatial irregularity as a clustering feature, using the spatial irregularity of all effective obstacle subspaces as a data set, and using the spatial irregularity in the data set as a data point; Step 1.2: Using the silhouette coefficient method to determine the optimal number of clusters K of the data set; Step 1.3: Randomly selecting K data points in the data set as initial cluster centers, for each data point, calculating its Euclidean distance to each initial cluster center, for each data point, traversing the K initial cluster centers, and assigning it to the cluster cluster corresponding to the nearest initial cluster center; Step 1.4: After traversing all data points, obtaining initial cluster clusters, for each initial cluster cluster, performing mean calculation on the data points therein to obtain a new cluster center; Step 1.5: Repeating steps 1.3 and 1.4 until the cluster center no longer changes, obtaining a final cluster cluster and a final cluster center.

[0013] Preferably, the step of calculating the obstacle irregularity based on the clustering results is: calculating the ratio of the number of data points in each final clustering cluster to the total number in the data set to obtain the weight of each final clustering cluster; and performing weighted summation of the weight of each final clustering cluster and the final clustering center to obtain the obstacle irregularity.

[0014] Preferably, the step of obtaining the obstacle shape obstruction coefficient according to the volume and shape of the obstacle is:

[0015] The method to obtain the obstacle shape obstruction coefficient based on the volume and irregularity of the obstacle is: Where SO represents the obstacle shape obstruction coefficient, IO represents the obstacle irregularity, and Ov represents the volume of the obstacle.

[0016] Preferably, the step of obtaining the obstacle movement influence coefficient according to the movement data evaluation is: using a laser radar to obtain the movement speed of the obstacle; using the laser radar to obtain the angle between the relative movement direction of the obstacle and the movement direction of the robot arm; the method of obtaining the obstacle movement influence coefficient according to the movement speed of the obstacle and the angle between the relative movement direction of the obstacle and the movement direction of the robot arm is MI=V o ×cos(θ), where MI is the obstacle movement influence coefficient, V o It is expressed as the moving speed of the obstacle, and θ is the angle between the relative moving direction of the obstacle and the moving direction of the robot arm.

[0017] Preferably, the step of selecting the obstacle to be avoided first according to the priority avoidance index is: comparing the priority avoidance indexes of the obstacles, and selecting the obstacle with the largest priority avoidance index as the obstacle to be avoided first.

[0018] Preferably, the step of adjusting the obstacle avoidance distance according to the priority avoidance index of the obstacle to be avoided is: setting the initial obstacle avoidance distance and the priority avoidance index threshold, and obtaining the actual obstacle avoidance distance according to the priority avoidance index and the initial obstacle avoidance distance. Among them, AD 实际 Indicated as the actual obstacle avoidance distance, AD 初始 It is represented as the initial obstacle avoidance distance, PA is represented as the priority avoidance index, and PA0 is represented as the priority avoidance index threshold.

[0019] Preferably, a robot arm obstacle avoidance system based on state feedback comprises: an obstacle data acquisition module, which is used to collect obstacle information and transmit the obstacle information to an obstacle selection module; an obstacle selection module, which is used to receive the obstacle information transmitted by the obstacle data acquisition module, analyze the obstacle information, select obstacles to be avoided first according to the analysis results, and transmit the selected obstacles to be avoided first to an obstacle avoidance distance adjustment module; an obstacle avoidance distance adjustment module, which is used to receive the obstacles to be avoided first transmitted by the obstacle selection module, and adjust the obstacle avoidance distance according to the obstacle information to be avoided first, obtain the actual obstacle avoidance distance, and transmit it to the obstacle avoidance module; an obstacle avoidance module, which is used to receive the actual obstacle avoidance distance transmitted by the obstacle avoidance distance adjustment module, and perform obstacle avoidance processing according to the actual obstacle avoidance distance.

[0020] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0021] 1. When the robot is in operation, use the laser radar to measure the obstacles around the robot to obtain the volume and shape of the obstacles, and analyze the obstacle shape obstruction coefficient based on the volume and shape of the obstacles. By obtaining the volume and shape of the obstacles, the robot can more accurately judge the actual degree of obstruction of the obstacles to its movement, especially in front of complex and irregular obstacles, which can effectively avoid mistaken obstacle avoidance or path planning failure caused by misjudgment of shape. The real-time calculated shape obstruction coefficient can reflect the impact of obstacles on the robot path in different directions, so that the robot can give priority to avoiding high-impedance obstacles in a dynamic environment, and improve the flexibility of the obstacle avoidance strategy.

[0022] 2. Use LiDAR to obtain the movement data of obstacles, including movement speed and direction, and evaluate the movement influence coefficient of obstacles based on the movement data. According to the movement speed and direction of obstacles, the robot arm can dynamically predict the future position of obstacles and respond in advance, effectively reducing the risk of collision. Especially in high-speed or complex movement environments, the movement influence coefficient can help the robot arm identify which obstacles have a greater impact on its path, thereby optimizing path selection.

[0023] 3. Select the obstacle to be avoided first according to the priority avoidance index. The priority avoidance index comprehensively considers factors such as the size, distance, shape, and speed of the obstacle, so as to ensure that the robot arm avoids the obstacle that poses the greatest threat to its movement. This can achieve more reasonable obstacle avoidance decisions and avoid repeated obstacle avoidance or path waste caused by unclear judgment.

[0024] 4. According to the priority avoidance index of the obstacles screened out for priority avoidance, the obstacle avoidance distance is adjusted to obtain the actual obstacle avoidance distance. The actual obstacle avoidance distance is dynamically adjusted according to the priority avoidance index, so that the robot arm can choose the appropriate obstacle avoidance distance in different situations. For high-risk obstacles, increasing the obstacle avoidance distance can provide a larger safety buffer; for low-risk obstacles, the obstacle avoidance distance can be reduced to ensure that space and time are not wasted. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A flow chart of a robotic arm obstacle avoidance method based on state feedback provided in an embodiment of the present application.

[0026] Figure 2 A structural diagram of a robotic arm obstacle avoidance system based on state feedback provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. In addition, the forms of the various structures recorded in the following embodiments are merely illustrative. The state feedback-based robotic arm obstacle avoidance method and system involved in the present invention are not limited to the various structures recorded in the following embodiments. All other implementations obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] The present invention provides a method for avoiding obstacles of a robotic arm based on state feedback, comprising the following steps:

[0029] When the robot arm detects two or more obstacles in operation and the distances are the same,

[0030] Step 1: When the robot arm is in operation, use the laser radar to measure the obstacles around the robot arm to obtain the volume and shape of the obstacles, and obtain the obstacle shape obstruction coefficient based on the volume and shape of the obstacles;

[0031] LiDAR is a sensor that measures the distance and shape of objects by emitting laser beams and receiving reflected signals. It scans the surrounding environment through a rapidly rotating laser transmitter and uses time difference and the speed of light to calculate the position, size and shape information of obstacles. LiDAR has the characteristics of high precision and high resolution, and can generate detailed three-dimensional point cloud data in complex environments. Therefore, it is often used for environmental perception and obstacle detection when the robot arm avoids obstacles.

[0032] In this embodiment, it should be specifically explained that the steps of using a laser radar to measure obstacles around the robot arm and obtain the volume and shape of the obstacles are as follows:

[0033] LiDAR generates point cloud data of obstacles by emitting laser pulses to the measurement points and measuring the time difference of the return signals;

[0034] Use laser radar to measure obstacles at multiple points to obtain obstacle point cloud data;

[0035] Connect each point in the obstacle point cloud data to reconstruct the three-dimensional shape of the obstacle and obtain the volume of the obstacle;

[0036] Divide the three-dimensional shape of the obstacle into n three-dimensional spaces on average. Each three-dimensional space is an equal-sized cube, which is recorded as the obstacle subspace. Filter out the obstacle subspace with the surface in contact with the outside world, which is recorded as the effective obstacle subspace.

[0037] The spatial irregularity of each effective obstacle subspace is calculated, and the spatial irregularity of each effective obstacle subspace is clustered using the K-means clustering method. The obstacle irregularity is calculated based on the clustering results.

[0038] By screening out the obstacle subspaces with surfaces in contact with the outside world, we can focus only on the parts with strong obstacle avoidance relevance. When avoiding obstacles, the robot arm mainly needs to pay attention to the external surfaces that may collide with it. Even if the shape of the internal sub-area is complex, it will not affect the movement path of the arm. Therefore, only calculating the spatial irregularity of the exposed area can more accurately reflect the geometric features that have a direct impact on the obstacle avoidance task; if the spatial irregularity of all sub-areas is calculated, especially those of the internal area, it may increase unnecessary computational burden. Calculating only the exposed area can significantly reduce the amount of calculation and improve the real-time and response speed of the system; since the internal sub-areas will not be exposed to the robot arm's sensors (such as lidar or cameras), calculating their shape irregularity has no practical significance in the obstacle avoidance scenario. Therefore, by focusing on calculating the shape irregularity of the exposed area, the analysis can be more focused on information that is useful for the task.

[0039] In this embodiment, it should be specifically explained that the steps of calculating the spatial irregularity of each effective obstacle subspace are:

[0040] Obtain the actual volume of the obstacle in each effective obstacle subspace, obtain the actual surface area of ​​the obstacle in contact with the outside world in each effective obstacle subspace, and obtain the body surface ratio according to the actual volume and the actual surface area in contact with the outside world: Where rt is the volume-to-surface ratio, ve is the actual volume, and sa is the actual surface area in contact with the outside world. A higher ratio of volume to surface area usually indicates a more regular shape, while a lower ratio indicates an irregular shape.

[0041] The method to obtain sphericity based on the actual volume and the actual surface area in contact with the outside world is: Where sy represents the sphericity. A sphericity close to 1 indicates that the shape is close to a sphere, and a smaller value indicates a more irregular shape.

[0042] The method to obtain spatial irregularity based on body surface ratio and sphericity is: Where SI represents spatial irregularity, rt represents body surface ratio, and sy represents sphericity.

[0043] In this embodiment, it should be specifically explained that the steps of clustering the spatial irregularity of each effective obstacle subspace using the K-means clustering method are as follows:

[0044] Step 1.1: Take spatial irregularity as the clustering feature, take the spatial irregularity of all valid obstacle subspaces as the data set, and the spatial irregularity in the data set as the data point;

[0045] Step 1.2: Use the silhouette coefficient method to determine the optimal number of clusters K for the data set;

[0046] The silhouette coefficient method is a method used to evaluate clustering quality and determine the optimal number of clusters K. It evaluates the compactness of the point within the cluster to which it belongs and the separation from the nearest neighbor cluster by calculating the silhouette coefficient of each sample point. The value range of the silhouette coefficient is from -1 to 1. The higher the value, the better the clustering effect. When the value is close to 1, it means that the point has a high similarity with other points in the cluster and is far away from other clusters; when it is close to 0, it means that the point is located at the boundary of two clusters and may not be suitable for the current cluster; and when it is close to -1, it means that the point may be incorrectly assigned to the current cluster. The key to the silhouette coefficient method is to calculate the average silhouette coefficient of all points under different K, and select the K with the highest average as the optimal number of clusters. This method not only takes into account the compactness within the cluster, but also pays attention to the separation between clusters. Therefore, it can comprehensively measure the clustering effect, and is especially suitable for evaluating the internal consistency of clusters and the differences between clusters.

[0047] Step 1.3: Randomly select K data points in the data set as the initial cluster centers. For each data point, calculate the Euclidean distance from it to each initial cluster center. The calculation formula is: Where d(P,α) represents the Euclidean distance from the data point to the cluster center, where P represents the data point and α represents the initial cluster center. For each data point, traverse the K initial cluster centers and assign it to the cluster corresponding to the nearest initial cluster center.

[0048] Euclidean distance is the most intuitive and commonly used distance measurement method between two points. It originates from Euclidean geometry and represents the "straight-line distance" between two points in space. In the K-means clustering algorithm, Euclidean distance is used to calculate the distance between each data point and multiple initial cluster centers, thereby determining which cluster center each point is closest to, thereby completing cluster assignment.

[0049] Step 1.4: After traversing all data points, the initial clusters are obtained. For each initial cluster, the mean of the data points in it is calculated to obtain a new cluster center.

[0050] Step 1.5: Repeat steps 1.3 and 1.4 until the cluster center no longer changes, and obtain the final cluster and the final cluster center.

[0051] In this embodiment, it should be specifically explained that the steps of calculating the obstacle irregularity according to the clustering result are:

[0052] The weight of each final cluster is calculated by calculating the ratio of the number of data points in each final cluster to the total number of data points in the data set;

[0053] The weight of each final cluster is weighted and summed with the final cluster center to get the obstacle irregularity, which is calculated as follows: Where IO represents the irregularity of the obstacle, Q j Represented as the weight of the jth final cluster, It is represented as the jth final cluster center, and K is the optimal number of clusters of the data set.

[0054] In this embodiment, it should be specifically explained that the steps of obtaining the obstacle shape obstruction coefficient according to the volume and shape of the obstacle are as follows:

[0055] The method to obtain the obstacle shape obstruction coefficient based on the volume and irregularity of the obstacle is: Where SO represents the obstacle shape obstruction coefficient, IO represents the obstacle irregularity, and Qv represents the volume of the obstacle.

[0056] Step 2: Use LiDAR to obtain the movement data of the obstacle, including the movement speed and movement direction, and evaluate the obstacle movement influence coefficient based on the movement data;

[0057] In this embodiment, it should be specifically explained that the steps of obtaining the obstacle movement influence coefficient according to the movement data evaluation are as follows:

[0058] Use LiDAR to obtain the moving speed of obstacles;

[0059] Use laser radar to obtain the angle between the relative movement direction of the obstacle and the movement direction of the robotic arm;

[0060] The method to obtain the obstacle movement influence coefficient is MI=V according to the moving speed of the obstacle and the angle between the relative moving direction of the obstacle and the moving direction of the robot arm. o ×cos(θ), where MI is the obstacle movement influence coefficient, V o It is expressed as the moving speed of the obstacle, and θ is the angle between the relative moving direction of the obstacle and the moving direction of the robot arm.

[0061] By calculating the angle θ between the relative movement direction of the obstacle and the movement direction of the robot arm, it can be determined whether the obstacle is approaching the path of the robot arm. The smaller the angle θ is (that is, the closer the movement direction of the obstacle is to the movement direction of the robot arm), the greater the threat of the obstacle to the robot arm. When the movement direction of the obstacle is consistent with the movement direction of the robot arm, the influence coefficient reaches the maximum value. This means that the robot arm needs to give priority to avoiding these more threatening obstacles.

[0062] Step 3: The method of obtaining the priority avoidance index according to the obstacle shape obstruction coefficient and the obstacle movement influence coefficient is PA = a1 × SO + a2 × MI, where PA represents the priority avoidance index, SO represents the obstacle shape obstruction coefficient, and the more complex the shape of the obstacle is and the greater the interference to the movement of the robot arm, the more the system tends to give priority to avoiding the obstacle. In other words, the system will give higher avoidance priority to obstacles with complex shapes or large volumes because they occupy more space, have a long detour path, and are difficult to avoid, ensuring that the robot arm avoids these obstacles first during movement to reduce potential collision risks and path planning difficulties. Through this proportional relationship, the obstacle avoidance system can more intelligently cope with complex environments and select the safest and most effective avoidance path. MI is represented by the obstacle movement influence coefficient. As the movement speed, direction, and uncertainty of the obstacle increase, the system will increase the priority avoidance level of the obstacle. In other words, obstacles with fast movement speed, complex movement trajectory, or unpredictable direction pose a greater threat to the robot arm and are more difficult to avoid, so the system tends to give priority to avoiding these dynamic obstacles. This proportional relationship ensures that the robot arm can respond to the ever-changing dynamic obstacles in the environment in a timely manner, prevent them from entering the robot arm's working path, and reduce the risk of collision. By dynamically adjusting the avoidance priority, the system can handle complex mobile environments more flexibly and safely, and avoid potential conflicts with fast-moving objects. a1 and a2 represent the weight coefficients of the obstacle shape obstruction coefficient and the obstacle movement influence coefficient, and a1+a2=1. The specific values ​​of a1 and a2 are determined by professionals according to the actual situation. For example, a1 and a2 can be 0.4 and 0.6.

[0063] Step 4: Select the obstacle to be avoided first according to the priority avoidance index;

[0064] In this embodiment, it should be specifically explained that the steps of selecting the obstacle to be avoided first according to the priority avoidance index are:

[0065] The priority avoidance indexes of the obstacles are compared, and the obstacle with the largest priority avoidance index is selected as the obstacle to be avoided first.

[0066] Step 5: Adjust the obstacle avoidance distance according to the priority avoidance index of the obstacle screened out for priority avoidance;

[0067] In this embodiment, it should be specifically explained that the steps of adjusting the obstacle avoidance distance according to the priority avoidance index of the obstacle to be avoided first are:

[0068] Set the initial obstacle avoidance distance, and the method to obtain the actual obstacle avoidance distance based on the priority avoidance index and the initial obstacle avoidance distance is: Among them, AD 实际Indicated as the actual obstacle avoidance distance, AD 初始 It is represented as the initial obstacle avoidance distance, PA is represented as the priority avoidance index, and PA0 is represented as the priority avoidance index threshold.

[0069] The priority avoidance index takes into account various factors of obstacles (such as size, shape, speed, dynamic characteristics, etc.). By dynamically adjusting the actual obstacle avoidance distance, the system can adapt to different environments and obstacle types more flexibly. When the detected obstacle has a high priority avoidance index (such as large or fast-moving obstacles), the system can automatically increase the actual obstacle avoidance distance to ensure sufficient space for avoidance. For smaller or stationary obstacles, when the priority avoidance index is low, the actual obstacle avoidance distance can be reduced to improve the movement efficiency of the robot arm.

[0070] In actual applications, different obstacles have different degrees of impact on the path. For low-priority obstacles, the system can choose a shorter detour; for high-priority obstacles, the system will maintain a larger obstacle avoidance distance to ensure safety while avoiding complex multiple detours. Through the dynamic adjustment of the actual obstacle avoidance distance, the system can respond more effectively to obstacles, especially when dealing with fast or sudden obstacles, providing a longer lead time to avoid accidents caused by delayed judgment. Through the calculated actual obstacle avoidance distance, the system can flexibly adjust according to the actual threat level of the obstacle, rather than uniformly taking emergency stops or large detours. This can maintain the continuous movement of the robot arm, improve operational efficiency, and reduce production delays caused by frequent stops.

[0071] Step 6: When the robot arm reaches the actual obstacle avoidance distance from the obstacle, it uses the path planning algorithm to generate a path that avoids collision with the obstacle based on the obstacles that are screened out for priority avoidance. The path planning algorithm can use the A algorithm;

[0072] In this embodiment, it should be specifically explained that the steps of using the path planning algorithm to generate a path that avoids collision with obstacles are:

[0073] Model the work area as a three-dimensional grid map and mark obstacles in the map;

[0074] Use LiDAR to detect obstacles in real time, obtain obstacle information, mark obstacles in the environment map, and update the map to make the area where the obstacle is located an inaccessible area;

[0075] Use the A algorithm for path search, which is a heuristic search algorithm used to find the shortest path from a starting point to an end point in a graph or grid;

[0076] Use the B-spline curve algorithm to smooth the path and make the path smoother. The B-spline curve algorithm is a mathematical method for generating smooth curves, which is widely used in computer graphics and path planning. It defines a piecewise polynomial curve through multiple control points to ensure the smoothness and continuity of the curve. The B-spline curve has the characteristics of local control, that is, each control point only affects the shape of the curve within a specific range, so when a control point is adjusted, the overall curve will not be affected. It can generate a smooth and natural path by adjusting the control points and the order of the curve (usually third order), which is very suitable for smoothing complex paths or trajectories;

[0077] Check whether the points or line segments on the path overlap with obstacles. Use the collision detection algorithm to determine whether each node or line segment in the path intersects with the obstacle. If so, the path segment is considered impassable.

[0078] When a new obstacle is detected or an existing path is blocked by an obstacle, an incremental path planning algorithm is used to recalculate the affected path portion instead of planning from scratch.

[0079] Step 7: When the robot arm is performing obstacle avoidance, it continuously detects new obstacle information or changes in the environment and adjusts the path in real time to ensure the effectiveness of obstacle avoidance.

[0080] In this embodiment, it is necessary to specifically explain that a robot arm obstacle avoidance system based on state feedback includes:

[0081] The obstacle data collection module is used to collect obstacle information and transmit the obstacle information to the obstacle selection module;

[0082] The obstacle selection module is used to receive the obstacle information transmitted by the obstacle data acquisition module, analyze the obstacle information, select the obstacles to be avoided first according to the analysis results, and transmit the obstacles to be avoided first to the obstacle avoidance distance adjustment module;

[0083] The obstacle avoidance distance adjustment module is used to receive the obstacles to be avoided first transmitted by the obstacle selection module, and adjust the obstacle avoidance distance according to the information of the obstacles to be avoided first, so as to obtain the actual obstacle avoidance distance and transmit it to the obstacle avoidance module;

[0084] The obstacle avoidance module is used to receive the actual obstacle avoidance distance transmitted by the obstacle avoidance distance adjustment module and perform obstacle avoidance processing according to the actual obstacle avoidance distance.

[0085] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0086] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A robot arm obstacle avoidance method based on state feedback, characterized in that: The following steps are involved: Step 1: When the robot arm is in operation, use the laser radar to measure the obstacles around the robot arm to obtain the volume and shape of the obstacles, and obtain the obstacle shape obstruction coefficient based on the volume and shape of the obstacles; Step 2: Use LiDAR to obtain the movement data of the obstacle, including the movement speed and movement direction, and evaluate the obstacle movement influence coefficient based on the movement data; Step 3: The method for obtaining the priority avoidance index according to the obstacle shape obstruction coefficient and the obstacle movement influence coefficient is PA=a1×SO+a2×MI, where PA represents the priority avoidance index, SO represents the obstacle shape obstruction coefficient, MI represents the obstacle movement influence coefficient, and a1 and a2 represent the weight coefficients of the obstacle shape obstruction coefficient and the obstacle movement influence coefficient; Step 4: Select the obstacle to be avoided first according to the priority avoidance index; Step 5: Adjust the obstacle avoidance distance according to the priority avoidance index of the obstacle screened out to obtain the actual obstacle avoidance distance; Step 6: When the robot arm reaches the actual obstacle avoidance distance from the obstacle, it uses the path planning algorithm to generate a path that avoids collision with the obstacle based on the obstacles that are screened out for priority avoidance; Step 7: While the robot arm is avoiding obstacles according to the path, it continuously detects new changes in obstacle information and adjusts the path in real time.

2. The method for avoiding obstacles of a robotic arm based on state feedback according to claim 1, characterized in that: The steps of using laser radar to measure obstacles around the robot arm to obtain the volume and shape of the obstacles are as follows: LiDAR generates point cloud data of obstacles by emitting laser pulses to the measurement points and measuring the time difference of the return signals; Use laser radar to measure obstacles at multiple points to obtain obstacle point cloud data; Connect each point in the obstacle point cloud data to reconstruct the three-dimensional shape of the obstacle and obtain the volume of the obstacle; Divide the three-dimensional shape of the obstacle into n three-dimensional spaces on average. Each three-dimensional space is an equal-sized cube, which is recorded as the obstacle subspace. Filter out the obstacle subspace with the surface in contact with the outside world, which is recorded as the effective obstacle subspace. The spatial irregularity of each effective obstacle subspace is calculated, and the spatial irregularity of each effective obstacle subspace is clustered using the K-means clustering method. The obstacle irregularity is calculated based on the clustering results.

3. The method for avoiding obstacles of a robotic arm based on state feedback according to claim 2, characterized in that: The steps for calculating the spatial irregularity of each effective obstacle subspace are: Obtain the actual volume of the obstacle in each effective obstacle subspace, obtain the surface area of ​​the obstacle actually in contact with the outside world in each effective obstacle subspace, and obtain the body surface ratio according to the actual volume and the actual surface area in contact with the outside world; The sphericity is obtained based on the actual volume and the actual surface area in contact with the outside world; The method to obtain spatial irregularity based on body surface ratio and sphericity is: Where SI represents spatial irregularity, rt represents body surface ratio, and sy represents sphericity.

4. The method for avoiding obstacles of a robotic arm based on state feedback according to claim 2, characterized in that: The steps of clustering the spatial irregularity of each effective obstacle subspace using the K-means clustering method are as follows: Step 1.1: Take spatial irregularity as the clustering feature, take the spatial irregularity of all valid obstacle subspaces as the data set, and the spatial irregularity in the data set as the data point; Step 1.2: Use the silhouette coefficient method to determine the optimal number of clusters K for the data set; Step 1.3: Randomly select K data points in the data set as the initial cluster centers. For each data point, calculate its Euclidean distance to each initial cluster center. For each data point, traverse the K initial cluster centers and assign it to the cluster corresponding to the nearest initial cluster center. Step 1.4: After traversing all data points, the initial clusters are obtained. For each initial cluster, the mean of the data points in it is calculated to obtain a new cluster center. Step 1.5: Repeat steps 1.3 and 1.4 until the cluster center no longer changes, and obtain the final cluster and the final cluster center.

5. The method for avoiding obstacles by a robotic arm based on state feedback according to claim 2, characterized in that: The step of calculating the obstacle irregularity according to the clustering result is: The weight of each final cluster is calculated by calculating the ratio of the number of data points in each final cluster to the total number of data points in the data set; The weight of each final cluster is weighted and summed with the final cluster center to obtain the obstacle irregularity.

6. The method for avoiding obstacles by a robotic arm based on state feedback according to claim 1, characterized in that: The step of obtaining the obstacle shape obstruction coefficient according to the volume and shape of the obstacle is: The method to obtain the obstacle shape obstruction coefficient based on the volume and irregularity of the obstacle is: Where SO represents the obstacle shape obstruction coefficient, IO represents the obstacle irregularity, and Ov represents the volume of the obstacle.

7. The method for avoiding obstacles by a robotic arm based on state feedback according to claim 1, characterized in that: The step of evaluating the obstacle movement influence coefficient according to the movement data is as follows: Use LiDAR to obtain the moving speed of obstacles; Use laser radar to obtain the angle between the relative movement direction of the obstacle and the movement direction of the robotic arm; The method to obtain the obstacle movement influence coefficient is MI=V according to the moving speed of the obstacle and the angle between the relative moving direction of the obstacle and the moving direction of the robot arm. o ×cos(θ), where MI is the obstacle movement influence coefficient, V o It is expressed as the moving speed of the obstacle, and θ is the angle between the relative moving direction of the obstacle and the moving direction of the robot arm.

8. The method for avoiding obstacles by a robotic arm based on state feedback according to claim 1, characterized in that: The steps of selecting obstacles to be avoided first according to the priority avoidance index are: The priority avoidance indexes of the obstacles are compared, and the obstacle with the largest priority avoidance index is selected as the obstacle to be avoided first.

9. The method for avoiding obstacles by a robotic arm based on state feedback according to claim 1, characterized in that: The step of adjusting the obstacle avoidance distance according to the priority avoidance index of the obstacle to be avoided is: The method of setting the initial obstacle avoidance distance and the priority avoidance index threshold and obtaining the actual obstacle avoidance distance based on the priority avoidance index and the initial obstacle avoidance distance is as follows: Among them, AD 实际 Indicated as the actual obstacle avoidance distance, AD 初始 It is represented as the initial obstacle avoidance distance, PA is represented as the priority avoidance index, and PA0 is represented as the priority avoidance index threshold.

10. A robot arm obstacle avoidance system based on state feedback, characterized in that: The system comprises: The obstacle data collection module is used to collect obstacle information and transmit the obstacle information to the obstacle selection module; The obstacle selection module is used to receive the obstacle information transmitted by the obstacle data acquisition module, analyze the obstacle information, select the obstacles to be avoided first according to the analysis results, and transmit the obstacles to be avoided first to the obstacle avoidance distance adjustment module; The obstacle avoidance distance adjustment module is used to receive the obstacles to be avoided first transmitted by the obstacle selection module, and adjust the obstacle avoidance distance according to the information of the obstacles to be avoided first, so as to obtain the actual obstacle avoidance distance and transmit it to the obstacle avoidance module; The obstacle avoidance module is used to receive the actual obstacle avoidance distance transmitted by the obstacle avoidance distance adjustment module and perform obstacle avoidance processing according to the actual obstacle avoidance distance.

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