A data collection and analysis method for electric robot and electric robot

Abstract

The present invention discloses a data collection and analysis method for an electric robot and an electric robot, which are suitable for data analysis and motion control when a data collection task is executed. The data collection and analysis method comprises the following main steps: obtaining three-dimensional point cloud data of the activity area of ​​the electric robot, analyzing the three-dimensional point cloud data and obtaining obstacles with sharp edges; obtaining the relative distance value between the electric robot and the sharp edge by coordinate conversion and minimum distance search; if it is determined whether the relative distance value is less than a distance threshold, calculating the risk index according to the danger index of the obstacle and the potential damage value of the obstacle to the electric robot; if it is determined whether the risk index is greater than the risk threshold, calculating the obstacle avoidance distance; and generating an obstacle avoidance trajectory according to the obstacle avoidance distance. The present invention can perform real-time analysis and judgment through the collected data obtained when the data collection task is executed, thereby improving the safety and reliability of the electric robot in a complex environment.

Description

Technical Field

[0001] The present invention relates to the field of robot control technology, and in particular to a data collection and analysis method for an electric robot and an electric robot. Background Art

[0002] In the power system, when the power robot is performing data collection tasks, it needs to plan a reasonable motion path to avoid obstacles. However, there are a large number of obstacles with sharp edges in the power system plant environment, such as steel frames, pipelines, equipment, etc. These sharp edges may cause mechanical damage to the robot. In order to ensure that the power robot can operate safely in the complex power plant, it is necessary to identify and evaluate the risks of these obstacles with sharp edges, so as to reasonably plan the obstacle avoidance path and movement speed of the power robot. At present, most of the conventional obstacle avoidance planning methods analyze the obstacles as a whole, lack of consideration of the damage risk caused by these obstacles with sharp edges to the power robot, and cannot meet the robot control requirements in the complex plant environment of the power system. Therefore, how to accurately identify obstacles with sharp edges and quantify the risk of mechanical damage to the robot, so as to achieve reasonable obstacle avoidance trajectory design and robot control optimization, is a technical problem that needs to be solved urgently. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a data collection and analysis method for an electric robot, so as to improve the obstacle avoidance capability of the electric robot and achieve the effect of safe and stable operation of the electric robot.

[0004] In a first aspect, the present invention provides a data collection and analysis method for an electric robot, the method comprising:

[0005] Acquire three-dimensional point cloud data of the activity area of ​​the electric robot, analyze the three-dimensional point cloud data, and obtain obstacles with sharp edges in the activity area;

[0006] By coordinate transformation and minimum distance search, the relative distance value between the electric robot and the sharp edge of the obstacle is obtained;

[0007] Determine whether the relative distance value is less than a preset distance threshold, and if so, calculate the risk index of the obstacle according to the danger index of the obstacle and the potential damage value caused by the obstacle to the electric robot;

[0008] Determining whether the risk index is greater than a preset risk threshold, and if so, calculating an obstacle avoidance distance according to a difference between the risk index and the risk threshold;

[0009] An obstacle avoidance trajectory is generated according to the obstacle avoidance distance, and the motion of the electric robot is controlled according to the obstacle avoidance trajectory.

[0010] Furthermore, the step of analyzing the three-dimensional point cloud data to obtain obstacles with sharp edges in the activity area includes:

[0011] Extracting feature points from the three-dimensional point cloud data to obtain an edge feature point set, and connecting adjacent feature points of the edge feature point set using a region growing connection algorithm to obtain a contour line of the obstacle;

[0012] Resampling the contour line by using an interpolation method, and generating an edge surface according to the position coordinates of the sampling points and a triangular mesh method;

[0013] According to the edge surface, a surface equation is obtained, and according to the surface equation, the inclination angle of each point on the surface is calculated, and the sharp edge point is determined according to the change amount of the inclination angle of adjacent points;

[0014] The sharp edge points are connected to obtain the sharp edge of the obstacle.

[0015] Furthermore, the step of obtaining the relative distance value between the electric robot and the sharp edge of the obstacle through coordinate transformation and minimum distance search includes:

[0016] converting the coordinates of the obstacle into a robot coordinate system of the electric robot;

[0017] According to the converted coordinates of the obstacle, a closest point pair algorithm is used to obtain the relative distance value between the electric robot and the sharp edge of the obstacle.

[0018] Furthermore, the step of calculating the risk index of the obstacle according to the danger index of the obstacle and the potential damage value caused by the obstacle to the electric robot includes:

[0019] Obtaining feature data of the obstacle according to the three-dimensional point cloud data of the obstacle, and performing weighted summation on the feature data according to feature weights to obtain a danger index of the obstacle;

[0020] According to the characteristic data and the material parameters of each key part of the electric robot, the loss data of similar cases are extracted from the historical damage case database according to the similarity, and the loss data is used as the potential damage value caused by the obstacle to the electric robot;

[0021] The product of the hazard index and the potential damage value is used as the risk index of the obstacle.

[0022] Furthermore, the step of calculating the obstacle avoidance distance according to the difference between the risk index and the risk threshold comprises:

[0023] Calculating a difference ratio according to a difference between the risk index and the risk threshold, and taking a product of the difference ratio and a preset compensation distance as an additional obstacle avoidance distance;

[0024] The additional obstacle avoidance distance is added to the preset basic obstacle avoidance distance to obtain the obstacle avoidance distance.

[0025] Furthermore, the step of generating an obstacle avoidance trajectory according to the obstacle avoidance distance includes:

[0026] According to the obstacle avoidance distance, an obstacle avoidance path is constructed on a grid map, and at each grid node of the obstacle avoidance path, a path key point is extracted according to a preset sampling interval;

[0027] Using cubic spline interpolation to connect the key points of the path to obtain a key point path;

[0028] Calculating the path curvature of the key point path, and performing curvature smoothing on the path segment whose path curvature is greater than the path curvature threshold;

[0029] According to the smoothed path curvature, a new path segment is generated, and the key point path is updated according to the new path segment to obtain an obstacle avoidance trajectory.

[0030] Furthermore, after the step of obtaining the obstacle avoidance trajectory, the method further includes:

[0031] Calculating a steering angle of the electric robot according to a path vector of the obstacle avoidance trajectory;

[0032] Determine whether the steering angle is greater than a safety angle threshold, and if so, adjust the sampling interval and resample the path segment that exceeds the safety angle threshold;

[0033] The obstacle avoidance trajectory is updated according to the path key points obtained by resampling to obtain an updated obstacle avoidance trajectory.

[0034] Furthermore, the step of controlling the motion of the electric robot according to the obstacle avoidance trajectory includes:

[0035] Calculating the steering angle of the electric robot according to the path vector of the obstacle avoidance trajectory, and determining whether the steering angle is within an angle threshold range;

[0036] If so, the movement speed of the electric robot when passing through the obstacle avoidance trajectory corresponding to the steering angle is obtained according to the linear relationship between the steering angle and the preset angular velocity.

[0037] Furthermore, the step of controlling the motion of the electric robot according to the obstacle avoidance trajectory includes:

[0038] Obtaining the path curvature of the obstacle avoidance trajectory, and determining whether the path curvature is within a path curvature threshold range;

[0039] If so, the movement speed of the electric robot when passing through the obstacle avoidance trajectory corresponding to the path curvature is obtained according to the linear relationship between the path curvature and the preset curvature speed.

[0040] In a second aspect, the present invention provides an electric robot, which uses the above-mentioned method to perform motion control.

[0041] The present invention provides a data collection and analysis method for an electric robot and an electric robot. Through the method, the present invention can accurately identify obstacles with sharp edges by analyzing the point cloud data in the activity area of ​​the electric robot, evaluate the potential risks caused by the sharp edges of the obstacles to the electric robot, and adjust the obstacle avoidance trajectory according to the risk index. By reasonably designing the obstacle avoidance trajectory, the obstacle avoidance capability of the electric robot can be improved, and the safety of the operation of the electric robot is ensured. At the same time, the running speed of the electric robot is linearly controlled by the steering angle, and the stability of the operation of the electric robot is ensured. The present invention improves the safety and reliability of the electric robot in complex environments and provides a strong guarantee for the inspection and maintenance of electric equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flow chart of the data collection and analysis method of the electric robot in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] See also Figure 1 The first embodiment of the present invention provides a data collection and analysis method for an electric robot, which includes steps S10 to S50:

[0045] Step S10, acquiring three-dimensional point cloud data of the activity area of ​​the electric robot, analyzing the three-dimensional point cloud data, and obtaining obstacles with sharp edges in the activity area;

[0046] Step S20, obtaining the relative distance value between the electric robot and the sharp edge of the obstacle through coordinate conversion and minimum distance search;

[0047] Step S30, determining whether the relative distance value is less than a preset distance threshold, and if so, calculating the risk index of the obstacle according to the danger index of the obstacle and the potential damage value of the obstacle to the electric robot;

[0048] Step S40, determining whether the risk index is greater than a preset risk threshold, and if so, calculating an obstacle avoidance distance according to a difference between the risk index and the risk threshold;

[0049] Step S50, generating an obstacle avoidance trajectory according to the obstacle avoidance distance, and performing motion control on the electric robot according to the obstacle avoidance trajectory.

[0050] The robot data collection and analysis method provided by the present invention is mainly aimed at an electric robot that collects data in a plant environment of an electric power system. By collecting and analyzing the sharp edges of obstacles in its activity area, the electric robot is controlled to move and avoid obstacles. Since many electric equipment in the activity area have sharp edges, such as the edges of circuit breaker cabinets, multiple intersecting sharp edges at the welding point between the metal cover plate of the cable trench and the bracket, and multiple intersecting support rods at the top of the metal bracket of the lightning arrester, these sharp edges will affect the operation safety of the electric robot. Therefore, before the obstacle avoidance design, it is necessary to first identify the sharp edges in these activity areas.

[0051] In this embodiment, firstly, the environmental data of the activity area of ​​the electric robot is obtained. Here, the environmental data refers to the three-dimensional point cloud data containing the obstacle environment. Then, the position and sharp edge contour of the obstacle in the environment are determined by using the spatial coordinate information in the point cloud data. The specific steps include:

[0052] Extracting feature points from the three-dimensional point cloud data to obtain an edge feature point set, and connecting adjacent feature points of the edge feature point set using a region growing connection algorithm to obtain a contour line of the obstacle;

[0053] Resampling the contour line using a nearest neighbor interpolation method, and generating an edge surface based on the position coordinates of the sampling points and a triangular mesh method;

[0054] According to the edge surface, a surface equation is obtained, and according to the surface equation, the inclination angle of each point on the surface is calculated, and the sharp edge point is determined according to the change amount of the inclination angle of adjacent points;

[0055] The sharp edge points are connected to obtain the sharp edge of the obstacle.

[0056] In this embodiment, the three-dimensional point cloud data acquired by the lidar is first filtered and denoised, and then feature points are extracted from the point cloud data using a feature point detection algorithm (such as SUSAN, FAST, Harris, etc.). For each feature point, the K-nearest neighbor algorithm is used to determine the range of adjacent points, and the point density within the range, that is, the local point density, is calculated. Feature points whose local point density is lower than the local point density threshold are taken as edge points, thereby obtaining a set of edge feature points.

[0057] The local curvature analysis is performed on each edge feature point in the edge feature point set, and the region growing connection algorithm is used to connect the adjacent feature points whose local curvature values ​​are greater than the local curvature threshold, so as to obtain the contour line of the obstacle. Specifically, the points whose local curvature values ​​are greater than the local curvature threshold are used as seed points, and the neighborhood range of each point is determined, and the connection criterion is defined. The connection criterion is that the adjacent points need to simultaneously satisfy the local curvature value greater than the threshold and be spatially adjacent. Starting from the seed point, the points in its neighborhood are gradually checked. If the points in the neighborhood meet the connection criterion, they are added to the current area and continue to be checked as new seed points in the neighborhood. This process is repeated until no more points can be added to the current area. Finally, the adjacent feature points that meet the conditions are connected together through the region growing algorithm to form a connected area, that is, the contour line of the obstacle is obtained.

[0058] The interpolation method is used to resample the outline of the obstacle to obtain a smoother point distribution, and then the triangular mesh method is used to process the position coordinates of the resampled points. The edge surface is constructed through triangulation and mesh optimization. For the edge surface, the least squares method is used to fit the surface equation, and the inclination angle of each point on the surface is calculated according to the surface equation. The inclination angle refers to the inclination angle of the surface at this point relative to the tangent plane of the horizontal plane. Therefore, the normal vector of the surface at this point can be calculated by the surface equation, and then the angle between the normal vector and the vertical direction is calculated to obtain the inclination angle. In this embodiment, the change in the inclination angle between adjacent points is used to determine whether there is a sharp edge point. Assuming that the inclination angles of point x on the surface and its adjacent point x1 are a1 and a2 respectively, the change a=|a1-a2|, assuming that the threshold is a0, when a>a0, points x and x1 are taken as sharp edge points. For all the sharp edge points on the obtained surface, they are connected according to the adjacent areas to obtain the sharp edge of the obstacle. Through this method of the present embodiment, not only can the various obstacles in the activity area of ​​the electric robot be determined, but also the sharp edges on each obstacle can be accurately identified, thereby providing accurate data support for the next step of obstacle avoidance design.

[0059] After obtaining the sharp edge of the obstacle, the relative distance between the electric robot and the sharp edge is calculated through coordinate transformation and minimum distance search. The specific steps include:

[0060] converting the coordinates of the obstacle into a robot coordinate system of the electric robot;

[0061] According to the converted coordinates of the obstacle, a closest point pair algorithm is used to obtain the relative distance value between the electric robot and the sharp edge of the obstacle.

[0062] In this embodiment, since the coordinate system used by the robot is different from the coordinate system of the point cloud data, before calculating the distance, the coordinates of the point cloud data of the obstacle are first converted to the robot coordinate system, such as by performing coordinate conversion based on the rotation matrix and translation vector. The specific conversion steps can refer to the conventional coordinate conversion steps, and will not be repeated here.

[0063] After the coordinate transformation is completed, the relative distance value between the electric robot and the sharp edge of the obstacle is calculated through the nearest point pair algorithm. The relative distance value here includes the relative horizontal distance value and the relative height distance value.

[0064] The key parts of the electric robot include chassis, motor, arm joints and power supply. Taking into account the size parameters of the electric robot and the activity parameters of each key part, a distance threshold that can ensure the safe activity of the robot is pre-set. That is, when the relative distance value is outside the distance threshold range, the sharp edge will not affect the activity of the electric robot. When it is within the distance threshold range, the risk of obstacles with sharp edges to the robot's activity needs to be considered. The specific steps for calculating the risk index include:

[0065] Obtaining feature data of the obstacle according to the three-dimensional point cloud data of the obstacle, and performing weighted summation on the feature data according to feature weights to obtain a danger index of the obstacle;

[0066] According to the characteristic data and the material parameters of each key part of the electric robot, the loss data of similar cases are extracted from the historical damage case database according to the similarity, and the loss data is used as the potential damage value caused by the obstacle to the electric robot;

[0067] The product of the hazard index and the potential damage value is used as the risk index of the obstacle.

[0068] In this embodiment, the risk index of the obstacle is evaluated based on the characteristic data of the obstacle itself and the potential damage that the obstacle may bring to the electric robot. Specifically, the characteristic data of the obstacle is first extracted from the point cloud data of the obstacle. The characteristic data here include sharpness angle, edge length, cross-sectional area and volume parameters. For each characteristic data, a feature weight calculation method is pre-adopted to determine the feature weight of each feature data, such as the maximum entropy algorithm, etc., without too many restrictions here. The feature data is normalized and weighted summed to obtain a danger index based on the characteristics of the obstacle itself.

[0069] In this embodiment, a historical damage case database is pre-constructed, in which collision damage events of various types of electric robots with various obstacles during operation and damage data caused by the collision are stored. Based on the database, the similarity between the material parameters of each key part of the electric robot and the material parameters of the robot in the database is calculated, and the similarity between the characteristic data of the obstacle and the characteristic data of the obstacle stored in the database is calculated. According to the two similarities, similar cases are selected from the database, and the damage data corresponding to the similar cases are used as the potential damage value caused by the obstacle to the electric robot. Then, the product of the danger index of the obstacle and the damage value caused to the robot is used as the risk index of the obstacle.

[0070] The risk index of obstacles represents the degree of danger that obstacles with sharp edges bring to the electric robot. If the risk index is less than the preset risk threshold, the obstacle avoidance trajectory of the electric robot can be designed and planned according to the conventional obstacle avoidance distance. Otherwise, the obstacle avoidance distance needs to be adjusted according to the degree of danger of the obstacle. The specific adjustment steps include:

[0071] Calculating a difference ratio according to a difference between the risk index and the risk threshold, and taking a product of the difference ratio and a preset compensation distance as an additional obstacle avoidance distance;

[0072] The additional obstacle avoidance distance is added to the preset basic obstacle avoidance distance to obtain the obstacle avoidance distance.

[0073] In this embodiment, the difference between the risk index and the risk threshold is first calculated. The larger the difference, the more dangerous the obstacle is. In order to ensure the safe operation of the robot, the obstacle avoidance distance needs to be increased. The increased obstacle avoidance distance is determined based on the difference ratio of the risk index. Assuming that the risk index is A, the risk threshold is B, the preset basic obstacle avoidance distance is S1, and the preset compensation distance for avoiding obstacles above the risk threshold is S2, then the difference ratio P=(AB) / B, the additional obstacle avoidance distance for the obstacle is S2*P, and the total obstacle avoidance distance for the obstacle is S=S1+S2*P. For example, in a narrow passage of a substation, the sharp edge of a transformer radiator is identified, and its risk index is 92, which exceeds the preset threshold of 85. Therefore, the difference ratio of the risk of the transformer radiator exceeding the limit is 0.082, and the calculated additional obstacle avoidance distance is 0.025 meters. The basic obstacle avoidance distance of 0.5 meters is superimposed, so that the total obstacle avoidance distance is 0.525 meters. If there are multiple obstacles with sharp edges at the same time, the additional obstacle avoidance distances of each obstacle can be superimposed as the total additional obstacle avoidance distance. This embodiment calculates the obstacle avoidance distance by considering the degree of danger brought by the sharp edge of the obstacle to the power robot, which can improve the obstacle avoidance ability of the obstacle avoidance trajectory designed based on the obstacle avoidance distance, thereby ensuring the safety of the operation of the power robot.

[0074] After calculating the obstacle avoidance distance of the running path of the electric robot, the conventional robot path planning method can be used to design the robot's obstacle avoidance trajectory according to the obstacle avoidance distance. In this embodiment, in order to improve the curvature smoothness of the obstacle avoidance trajectory, a method for generating an obstacle avoidance trajectory based on the obstacle avoidance distance is preferably provided, and the specific steps include:

[0075] According to the obstacle avoidance distance, an obstacle avoidance path is constructed on a grid map, and at each grid node of the obstacle avoidance path, a path key point is extracted according to a preset sampling interval;

[0076] Using cubic spline interpolation to connect the key points of the path to obtain a key point path;

[0077] Calculating the path curvature of the key point path, and performing curvature smoothing on the path segment whose path curvature is greater than the path curvature threshold;

[0078] According to the smoothed path curvature, a new path segment is generated, and the key point path is updated according to the new path segment to obtain an obstacle avoidance trajectory.

[0079] In this embodiment, an obstacle avoidance path is first planned according to the obstacle avoidance distance on a grid map of a preset resolution, and key points of the path are extracted from the obstacle avoidance path according to a preset sampling interval. For example, an obstacle avoidance path is constructed using a grid map with a resolution of 0.05 meters, and key points of the path are extracted according to a sampling interval of 0.2 meters. Then, cubic spline interpolation is used to connect the key points of the path to obtain a key point path. Cubic spline interpolation is a technique for constructing a smooth curve through a series of key points. The curve is a cubic polynomial in each interval and is continuous in the entire domain of definition. At the same time, its first-order and second-order derivatives are also continuous. This embodiment can construct a key point path with a smooth transition through this interpolation method.

[0080] Then calculate the path curvature of each point on the key point path. The path curvature represents the degree of path curvature. The more severe the curvature, the greater the curvature. In the obstacle avoidance trajectory, curvature smoothing can ensure the continuity and drivability of the trajectory. Therefore, for the path segments with large path curvature, curvature smoothing operation is required. In this embodiment, the path segments with excessive path curvature are obtained by comparing the path curvature with the path curvature threshold, such as extracting the path segments with a curvature greater than 0.8 per meter, and then curvature smoothing is performed on these path segments, such as using a local weighted average algorithm for curvature smoothing, and then using the interpolation method to generate new path points based on the updated curvature values, and generate new path segments based on these new path points, and update the key point path, so as to obtain a curvature continuous and smooth obstacle avoidance trajectory. Through this obstacle avoidance trajectory generation method of this embodiment, the continuity and drivability of the obstacle avoidance trajectory can be effectively ensured.

[0081] Furthermore, for different types of electric robots, their operating parameters are also different. For example, the safe steering angles of different types of electric robots may be quite different. In order to ensure that the obstacle avoidance trajectory can adapt to different types of robots, in a preferred embodiment, the present invention also provides another obstacle avoidance trajectory generation method. The generation method is based on the above trajectory generation method, and then updates the obstacle avoidance trajectory through the following steps:

[0082] Calculating a steering angle of the electric robot according to a path vector of the obstacle avoidance trajectory;

[0083] Determine whether the steering angle is greater than a safety angle threshold, and if so, adjust the sampling interval and resample the path segment that exceeds the safety angle threshold;

[0084] The obstacle avoidance trajectory is updated according to the path key points obtained by resampling to obtain an updated obstacle avoidance trajectory.

[0085] In this embodiment, the steering angle of the electric robot when running on the generated obstacle avoidance trajectory is first calculated based on the path vector of the generated obstacle avoidance trajectory, and the safety angle threshold is set based on the dynamic constraints of different types of robots. Then, it is determined whether the calculated steering angle exceeds the safety angle threshold. If it exceeds the threshold, it is necessary to reduce the sampling interval to resample the path segment that exceeds the threshold, and then generate a new path segment using the above method based on the key points of the path obtained by resampling, and update the obstacle avoidance trajectory based on the new path segment to obtain an updated obstacle avoidance trajectory. The obstacle avoidance trajectory update method of this embodiment can further ensure the safety and stability of the operation of the electric robot.

[0086] After the obstacle avoidance trajectory is designed, the electric robot can be controlled to move and avoid obstacles based on the obstacle avoidance trajectory. In order to ensure the stability of the operation of the electric robot, in a preferred embodiment, the present invention provides a method for controlling the motion of the electric robot based on the obstacle avoidance trajectory. The specific control steps include:

[0087] Calculating the steering angle of the electric robot according to the path vector of the obstacle avoidance trajectory, and determining whether the steering angle is within an angle threshold range;

[0088] If so, the movement speed of the electric robot when passing through the obstacle avoidance trajectory corresponding to the steering angle is obtained according to the linear relationship between the steering angle and the preset angular velocity.

[0089] In this embodiment, for the motion control of the electric robot, its steering angle has a great relationship with the stability of its motion. In order to ensure that the electric robot can turn smoothly even when the steering angle is large, it is necessary to control the speed of the electric robot according to the steering angle. Specifically, the steering angle of the electric robot is first determined according to the obstacle avoidance trajectory, and then the steering angle is compared with the angle threshold range. In this embodiment, the angle threshold range is the angle range in which the electric robot needs to slow down. According to the characteristics of the electric robot, the angle threshold range is preferably set to 30 degrees to 45 degrees. Assuming that the rated operating speed of the electric robot is 0.8 meters per second, when the steering angle is less than or equal to 30 degrees, the electric robot maintains the rated speed and travels at a constant speed. When the steering angle is greater than or equal to 45 degrees, the electric robot travels at the minimum operating speed of the electric robot or stops running and issues a safety alarm. When the steering angle is between the angle threshold range, the operating speed of the electric robot is linearly adjusted according to the change of the steering angle. The speed corresponding to 30 degrees is the rated speed, and the speed corresponding to 45 degrees is the minimum speed. Then, a linear relationship between the angle and the speed is established. According to the linear relationship, the operating speed corresponding to the current steering angle can be obtained. Obviously, in this embodiment, as the steering angle increases, the running speed of the electric robot will decrease linearly, thereby achieving a smooth transition at the corner and ensuring the stability of the operation of the electric robot.

[0090] In a preferred embodiment, the present invention also provides another method for motion control of an electric robot based on an obstacle avoidance trajectory, and the specific control steps include:

[0091] Obtaining the path curvature of the obstacle avoidance trajectory, and determining whether the path curvature is within a path curvature threshold range;

[0092] If so, the movement speed of the electric robot when passing through the obstacle avoidance trajectory corresponding to the path curvature is obtained according to the linear relationship between the path curvature and the preset curvature speed.

[0093] The difference between the control steps of this embodiment and the control steps of the previous embodiment is that, in this embodiment, the running speed of the electric robot is controlled according to the path curvature of the obstacle avoidance trajectory. The path curvature represents the curvature of the path, and the curvature of the path corresponds to the turning angle of the electric robot when it passes through the road section. Therefore, by controlling the running speed of the electric robot through the path curvature, the stability of its operation can also be guaranteed. In this embodiment, if the current path curvature is less than or equal to the minimum value of the threshold range, the electric robot is controlled according to the rated speed. If it is greater than or equal to the maximum value of the threshold range, the electric robot is controlled according to the minimum speed. If it is within the threshold range, the curvature-speed linear relationship established according to the speed corresponding to the threshold endpoint is used to determine the running speed under the current roadbed curvature. It can be seen that as the roadbed curvature increases, the running speed of the electric robot will decrease linearly, thereby achieving a smooth transition at the corner and ensuring the stability of the operation of the electric robot.

[0094] This embodiment provides a data collection and analysis method for an electric robot. The present invention can accurately identify obstacles with sharp edges by analyzing the point cloud data in the activity area of ​​the electric robot, evaluate the potential risks caused by the sharp edges of the obstacles to the electric robot, and adjust the obstacle avoidance trajectory according to the risk index. By reasonably designing the obstacle avoidance trajectory, the obstacle avoidance capability of the electric robot can be improved, and the safety of the operation of the electric robot is ensured. At the same time, the operation speed of the electric robot is linearly controlled by the steering angle to ensure the stability of the operation of the electric robot. The present invention can accurately and effectively identify sharp obstacles in the activity area, accurately evaluate risks, and intelligently adjust the robot behavior, realizing real-time optimization of obstacle avoidance control, significantly improving the safety and reliability of the electric robot in complex environments, and providing a strong guarantee for the inspection and maintenance of power equipment.

[0095] Based on the same inventive concept, a second embodiment of the present invention provides an electric robot, which uses the above-mentioned method to perform motion control.

[0096] In summary, an embodiment of the present invention proposes a data collection and analysis method for an electric robot and an electric robot. The method acquires three-dimensional point cloud data of the activity area of ​​the electric robot, analyzes the three-dimensional point cloud data, and obtains obstacles with sharp edges in the activity area; obtains the relative distance value between the electric robot and the sharp edge of the obstacle through coordinate transformation and minimum distance search; determines whether the relative distance value is less than a preset distance threshold, and if so, calculates the risk index of the obstacle according to the danger index of the obstacle and the potential damage value of the obstacle to the electric robot; determines whether the risk index is greater than a preset risk threshold, and if so, calculates the obstacle avoidance distance according to the difference between the risk index and the risk threshold; generates an obstacle avoidance trajectory according to the obstacle avoidance distance, and controls the motion of the electric robot according to the obstacle avoidance trajectory. The present invention can accurately identify obstacles with sharp edges by analyzing the point cloud data in the activity area of ​​the electric robot, evaluate the potential risks caused by the sharp edges of the obstacles to the electric robot, and adjust the obstacle avoidance trajectory according to the risk index. By reasonably designing the obstacle avoidance trajectory, the obstacle avoidance ability of the electric robot can be improved, ensuring the safety of the operation of the electric robot. At the same time, the operation speed of the electric robot is linearly regulated by the steering angle to ensure the stability of the operation of the electric robot. The present invention can accurately and effectively identify sharp obstacles in the activity area, accurately evaluate risks, and intelligently adjust the robot behavior, realizing real-time optimization of obstacle avoidance control, significantly improving the safety and reliability of the electric robot in complex environments, and providing a strong guarantee for the inspection and maintenance of power equipment.

[0097] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0098] The above-mentioned embodiments only express several preferred implementation modes of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in the technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be based on the protection scope of the claims.

Claims

1. A data collection and analysis method for an electric power robot, characterized in that: include: Acquire three-dimensional point cloud data of the activity area of ​​the electric robot, analyze the three-dimensional point cloud data, and obtain obstacles with sharp edges in the activity area; By coordinate transformation and minimum distance search, the relative distance value between the electric robot and the sharp edge of the obstacle is obtained; Determine whether the relative distance value is less than a preset distance threshold, and if so, calculate the risk index of the obstacle according to the danger index of the obstacle and the potential damage value caused by the obstacle to the electric robot; Determining whether the risk index is greater than a preset risk threshold, and if so, calculating an obstacle avoidance distance according to a difference between the risk index and the risk threshold; generating an obstacle avoidance trajectory according to the obstacle avoidance distance, and controlling the motion of the electric robot according to the obstacle avoidance trajectory; The step of calculating the risk index of the obstacle according to the danger index of the obstacle and the potential damage value caused by the obstacle to the electric robot comprises: Obtaining feature data of the obstacle according to the three-dimensional point cloud data of the obstacle, and performing weighted summation on the feature data according to feature weights to obtain a danger index of the obstacle; According to the characteristic data and the material parameters of each key part of the electric robot, the loss data of similar cases are extracted from the historical damage case database according to the similarity, and the loss data is used as the potential damage value caused by the obstacle to the electric robot; The product of the hazard index and the potential damage value is used as the risk index of the obstacle; The step of calculating the obstacle avoidance distance according to the difference between the risk index and the risk threshold comprises: Calculating a difference ratio according to a difference between the risk index and the risk threshold, and taking a product of the difference ratio and a preset compensation distance as an additional obstacle avoidance distance; The additional obstacle avoidance distance is added to the preset basic obstacle avoidance distance to obtain the obstacle avoidance distance.

2. The data collection and analysis method for an electric power robot according to claim 1, characterized in that: The step of analyzing the three-dimensional point cloud data to obtain obstacles with sharp edges in the activity area comprises: Extracting feature points from the three-dimensional point cloud data to obtain an edge feature point set, and connecting adjacent feature points of the edge feature point set using a region growing connection algorithm to obtain a contour line of the obstacle; Resampling the contour line by using an interpolation method, and generating an edge surface according to the position coordinates of the sampling points and a triangular mesh method; According to the edge surface, a surface equation is obtained, and according to the surface equation, the inclination angle of each point on the surface is calculated, and the sharp edge point is determined according to the change amount of the inclination angle of adjacent points; The sharp edge points are connected to obtain the sharp edge of the obstacle.

3. The data collection and analysis method for an electric power robot according to claim 2, characterized in that: The step of obtaining the relative distance value between the electric robot and the sharp edge of the obstacle through coordinate conversion and minimum distance search comprises: converting the coordinates of the obstacle into a robot coordinate system of the electric robot; According to the converted coordinates of the obstacle, a closest point pair algorithm is used to obtain the relative distance value between the electric robot and the sharp edge of the obstacle.

4. The data collection and analysis method for an electric power robot according to claim 1, characterized in that: The step of generating an obstacle avoidance trajectory according to the obstacle avoidance distance comprises: According to the obstacle avoidance distance, an obstacle avoidance path is constructed on a grid map, and at each grid node of the obstacle avoidance path, a path key point is extracted according to a preset sampling interval; Using cubic spline interpolation to connect the key points of the path to obtain a key point path; Calculating the path curvature of the key point path, and performing curvature smoothing on the path segment whose path curvature is greater than the path curvature threshold; According to the smoothed path curvature, a new path segment is generated, and the key point path is updated according to the new path segment to obtain an obstacle avoidance trajectory.

5. The data collection and analysis method for an electric power robot according to claim 4, characterized in that: After the step of obtaining the obstacle avoidance trajectory, the method further includes: Calculating a steering angle of the electric robot according to a path vector of the obstacle avoidance trajectory; Determine whether the steering angle is greater than a safety angle threshold, and if so, adjust the sampling interval and resample the path segment that exceeds the safety angle threshold; The obstacle avoidance trajectory is updated according to the path key points obtained by resampling to obtain an updated obstacle avoidance trajectory.

6. The data collection and analysis method for an electric power robot according to claim 1, characterized in that: The step of controlling the motion of the electric robot according to the obstacle avoidance trajectory comprises: Calculating the steering angle of the electric robot according to the path vector of the obstacle avoidance trajectory, and determining whether the steering angle is within an angle threshold range; If so, the movement speed of the electric robot when passing through the obstacle avoidance trajectory corresponding to the steering angle is obtained according to the linear relationship between the steering angle and the preset angular velocity.

7. The data collection and analysis method for an electric power robot according to claim 1, characterized in that: The step of controlling the motion of the electric robot according to the obstacle avoidance trajectory comprises: Obtaining the path curvature of the obstacle avoidance trajectory, and determining whether the path curvature is within a path curvature threshold range; If so, the movement speed of the electric robot when passing through the obstacle avoidance trajectory corresponding to the path curvature is obtained according to the linear relationship between the path curvature and the preset curvature speed.

8. An electric robot, characterized in that: The electric robot performs motion control using the method described in any one of claims 1 to 7.

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