Control method of operation type intelligent foot robot applied to electric power inspection

Abstract

The invention discloses a control method of an operation type intelligent foot-type robot applied to electric power inspection, and relates to the technical field of electric power inspection robots, and the method comprises the following steps: carrying out the omnibearing scanning and detection of a surrounding environment based on a multi-mode sensor carried by the foot-type robot, so as to collect environment data, and carrying out the fusion of the collected environment data, wherein the multi-mode sensor comprises a front stereoscopic vision sensor, a rear stereoscopic vision sensor and a three-dimensional solid laser radar. Through the carried multi-mode sensor, all-directional scanning and detection of electric power facilities and the autonomous navigation capability of the robot are realized, and the SLAM technology is combined, so that the robot can carry out effective path planning and autonomous inspection in an unknown environment, collision between the robot and an obstacle is avoided, the damage risk is reduced, and the safety of the robot is improved. And the dependence on manpower is reduced, the inspection range is expanded, and the continuity and integrity of inspection are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power inspection robots, and in particular to a control method for an operational intelligent foot-type robot used for electric power inspection. Background Art

[0002] With the increase in the scale of power grids and the improvement in voltage levels, the requirements for power supply safety and reliability are becoming increasingly stringent. The normal operation of key facilities such as substations, transmission lines, distribution rooms, power tunnels and wind farms has become an important link in ensuring the stable power supply of power systems. These facilities are often distributed over a wide area, and the environment is complex and changeable. Traditional manual inspection methods can no longer meet the needs of efficient, comprehensive and accurate inspections, especially under severe weather conditions. Inspectors not only face physical hazards, but also find it difficult to ensure the comprehensiveness and accuracy of inspections. Intelligent inspection robots, especially operational intelligent foot-type robots, are gradually being used in the field of power inspections with their unique advantages.

[0003] For example, the Chinese patent publication number: CN116787457A discloses an operational intelligent legged robot and control method for power inspection. Its structure includes a quadruped bionic robot and a five-degree-of-freedom serial robot arm. The five-degree-of-freedom serial robot arm is installed on the quadruped bionic robot, and can achieve high-precision environmental perception through the sensors carried. At the same time, the five-degree-of-freedom serial robot arm is combined with the quadruped robot to perform special tasks such as target collection and object grasping in complex power inspection scenarios; through the belt-driven legs, different leg configurations can be selected according to actual conditions, and a robot bionic control method is proposed, which can achieve unified control of the five-degree-of-freedom serial robot arm and the quadruped robot.

[0004] In the prior art, although the quadruped robot is equipped with a five-degree-of-freedom tandem robotic arm, it can perform special tasks such as target acquisition and object grasping in complex scenes, and can perform multi-posture conversion in complex scenes, but the various sensors it carries will have a certain impact on the sensor's perception performance in a complex environment, thereby affecting the robot's stability and maneuverability. Therefore, how to judge the complexity of the robot's inspection environment in order to plan the inspection path is a problem we need to solve. To this end, a control method for an operational intelligent legged robot used in power inspection is proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a control method for an operational intelligent footed robot used for power inspection, so as to solve the problems raised in the above-mentioned background technology.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A control method for an operational intelligent foot-type robot applied to power inspection comprises the following steps:

[0008] Step 1: Performing all-round scanning and detection of the surrounding environment based on the multimodal sensor carried by the legged robot to collect environmental data and fuse the collected environmental data, wherein the multimodal sensor includes a front stereo vision sensor, a rear stereo vision sensor and a three-dimensional solid-state laser radar;

[0009] Step 2: Evaluate the complexity of the environment based on the fused environmental data, determine whether there are extreme terrain or obstacles, and plan the optimal inspection path based on the environmental complexity evaluation results and environmental mapping results to ensure that the legged robot can complete the inspection task efficiently and safely;

[0010] Step 3: According to the inspection path planning results and inspection task requirements, the posture of the manipulator and the legged robot is adjusted, and the SLAM technology is used for real-time positioning and environmental mapping to adapt to the current terrain and environmental conditions, so that the legged robot can autonomously navigate in an unknown environment. The SLAM technology is based on the simultaneous positioning and mapping system, which is the SLAM system.

[0011] Step 4, performing power inspection tasks of the legged robot and the robotic arm, and making dynamic adjustments according to the real-time operating status and performance parameters of the legged robot and the robotic arm, wherein the status of the legged robot and the robotic arm includes position, posture, and energy consumption;

[0012] Step 5: Establish a remote monitoring system to monitor the energy consumption of the legged robot in real time, and dynamically adjust the operation mode of the legged robot according to the energy consumption monitoring results.

[0013] A further improvement of the technical solution of the present invention is that in step 1, the process of fusing the collected environmental data is as follows:

[0014] Step 101, start the multimodal sensor carried by the legged robot to collect raw environmental data, wherein the multimodal sensor includes a front stereo vision sensor, a rear stereo vision sensor and a three-dimensional solid-state laser radar, the front stereo vision sensor is used to collect image data of the front environment of the legged robot, including color, shape and texture information, the rear stereo vision sensor is used to collect image data of the rear environment for rear perception and obstacle avoidance of the legged robot, and the three-dimensional solid-state laser radar is used to perform 360-degree omnidirectional scanning to collect distance and shape information of the surrounding environment;

[0015] Step 102, performing pre-processing operations such as denoising and filtering on the raw environmental data collected by the multimodal sensor to improve data quality and reduce noise interference;

[0016] Step 103, time synchronization and spatial registration are performed on the preprocessed environmental data, so that all sensor data are synchronized in time to reflect the environmental state at the same time, and the data of different sensors are converted into the same coordinate system for spatial registration;

[0017] Step 104, using data fusion technology to fuse the data of the front stereo vision sensor, the rear stereo vision sensor and the three-dimensional solid-state laser radar, and extracting edge, corner point and texture features from the fused data, performing feature matching, and identifying objects and obstacles in the environment;

[0018] Step 105, using the extracted features and pre-processed environmental data, construct a three-dimensional model of the surrounding environment to provide a basis for the navigation, obstacle avoidance and path planning of the legged robot, and based on the fused data and the constructed three-dimensional model of the surrounding environment, enable the robot to identify traversable areas, obstacles and terrain changes.

[0019] A further improvement of the technical solution of the present invention is that in step 2, the process of planning the optimal inspection path is:

[0020] Step 201, using the three-dimensional model of the surrounding environment to analyze the terrain features in the environment, identify obstacles in the environment, and record the number, size, shape and position of the obstacles, wherein the terrain features include slope and unevenness, and the obstacles include rocks, ditches, vegetation, etc.;

[0021] Step 202, based on the terrain characteristics and the distribution of obstacles, comprehensively analyze the terrain undulation, the density of obstacles, the diversity of terrain types and the relative distance between the legged robot and the obstacles, and calculate the environmental complexity evaluation index, wherein the terrain undulation is obtained by calculating the standard deviation of the terrain height, the density of obstacles is obtained by calculating the ratio of the obstacle data to the area covered by the travel path, and the diversity of terrain types is obtained by calculating the cumulative frequency of each obstacle type in the inspection area and calculating its Gini coefficient;

[0022] Step 203, using the environment complexity assessment index and the preset risk threshold to assess the environment complexity, so as to determine whether there is an extreme terrain or obstacle risk in the environment. If the preset risk threshold is exceeded, it is determined that there is an extreme terrain or obstacle risk in the environment, and based on the assessment result of the environment complexity, further analyze the factors leading to high environment complexity, and identify specific terrain features or obstacle types;

[0023] Step 204, determining the starting position and the end position of the legged robot according to the inspection task requirements, and generating multiple candidate paths from the starting point to the end point using a path planning algorithm based on the analysis results of terrain features or obstacle types;

[0024] Step 205, optimize and evaluate the generated candidate paths, comprehensively analyze the path length, complexity and risk, avoid high-risk areas and reduce path complexity, reduce the passage through areas with large terrain fluctuations and dense obstacles, and plan the optimal inspection path.

[0025] A further improvement of the technical solution of the present invention is that the calculation formula of the environmental complexity evaluation index is:

[0026]

[0027] V = 1-Gi(tp);

[0028]

[0029] Among them, ECI is the environmental complexity assessment index, Q is the terrain undulation, Q = std(h), h is the terrain height dataset, N is the number of obstacles, that is, the total number of obstacles encountered on the path of the legged robot, and D is the obstacle distribution density, which indicates the number of obstacles per unit area. A is the area covered by the path of the legged robot, V is the diversity of terrain types, Gi(tp) is the Gini coefficient, and A i is the cumulative frequency of the ith obstacle type, n is the total number of obstacle types, R is the inverse of the relative distance between the legged robot and the nearest obstacle, r is the distance between the legged robot and each obstacle, and w1, w2, w3, w4, w5, and w6 are weight coefficients.

[0030] A further improvement of the technical solution of the present invention is that in step 3, the process of autonomous navigation of the legged robot in an unknown environment is as follows:

[0031] Step 301, analyzing the operations that the robot arm needs to perform in the inspection task, planning the initial posture of the robot arm and the position of the end effector according to the task requirements, ensuring that it can accurately reach the target position and perform the predetermined operation, and using the dynamic model of the robot arm to calculate the joint angle and torque required to achieve the specified posture, ensuring that the robot arm moves smoothly and meets the mechanical requirements;

[0032] Step 302, based on the surrounding environment data collected by the multimodal sensor, after preprocessing the surrounding environment data, matching feature points of the current sensor data with the historical map, and initializing the simultaneous localization and mapping (SLAM) system, calibrating the sensor and the initial position setting, and associating the matching feature points with the features on the historical map, and using the position information provided by the SLAM system to estimate the state of the legged robot, and determine the position and direction of the legged robot in the environment;

[0033] Step 303, updating the two-dimensional grid map of the surrounding environment according to the multimodal sensor data and the positioning result, and receiving new multimodal sensor data during the movement of the legged robot to update the positioning and map information in real time;

[0034] Step 304, based on the updated two-dimensional grid map of the surrounding environment and terrain features, track the preset optimal inspection path, combine the actual terrain and environmental changes, dynamically adjust the path and the posture of the legged robot to adapt to the current terrain and environmental conditions, and enable the legged robot to autonomously navigate in an unknown environment.

[0035] A further improvement of the technical solution of the present invention is that the process of updating the two-dimensional grid map of the surrounding environment is:

[0036] Step 3031, extracting obstacle edges and terrain undulation features in the environment from the multimodal sensor data;

[0037] Step 3032, creating an initial two-dimensional array to represent a two-dimensional grid map of the environment, and each cell records the edge of the obstacle and the terrain undulation characteristics;

[0038] Step 3033, combining the extracted obstacle edges and terrain undulation features with the determined position and orientation results of the legged robot in the environment, and updating the two-dimensional grid map of the surrounding environment;

[0039] Step 3034, for each grid unit of the two-dimensional grid map, the grid occupancy probability is updated, and when the legged robot moves, the multimodal sensor data is continuously collected, and SLAM processing is performed in real time to determine the current position of the legged robot in the environment, and the positioning of the legged robot is updated by combining the historical map information and the current multimodal sensor data;

[0040] Step 3035, based on the output of the real-time SLAM system and the new multimodal sensor data, the grid map is continuously updated to reflect the latest state of the environment.

[0041] A further improvement of the technical solution of the present invention is that the expression for updating the grid occupancy probability is:

[0042]

[0043] p(0|z t , x t , m k )=1-p(1|z t , x t , m k );

[0044] Among them, p(m k |z 1∶t , x 1∶t) is given all sensor data z 1∶t and all state estimates x 1∶t Under the condition of , the probability that the kth grid is occupied, p occ is the prior probability that the grid is occupied, p(0|z t , x t , m k ) is the given sensor data z t , state estimate x t and grid m k Under the condition of t , x t , m k ) is the given sensor data z t , state estimate x t and grid m k Under the condition of k |z 1∶t-1 , x 1∶t-1 ) represents the probability that the kth grid was occupied at the last moment, p(z t |1,x t , m k ) indicates that sensor data z is obtained when the grid is occupied t The probability, p(z t |0,x t , m k ) indicates that sensor data z is obtained when the grid is idle t probability.

[0045] A further improvement of the technical solution of the present invention is that in step 4, the dynamic adjustment process is:

[0046] Step 401, according to the inspection lines, inspection points, and inspection standard requirements of the power inspection, the inspection paths and task sequences of the legged robot and the robotic arm are planned, and the inspection tasks are assigned to the legged robot and the robotic arm, and their respective task areas, inspection contents, and time requirements are clarified;

[0047] Step 402, during the task execution, the position and posture changes of the legged robot and the manipulator are monitored in real time through GPS, IMU and visual odometer sensors, and the battery power consumption is monitored in real time to predict the remaining battery life, and the joint angle, speed, and strength performance parameters of the manipulator, as well as the gait stability and ground adaptability of the legged robot are monitored to ensure that they are in good working condition;

[0048] Step 403, using synchronous positioning and mapping technology and grid maps to plan the path, and dynamically adjusting the path according to environmental changes and the state of the legged robot, and dynamically adjusting the inspection tasks and priorities of the robot arm according to the inspection progress, importance and urgency, to ensure that key inspection points are inspected in a timely manner;

[0049] Step 404, records key data during the inspection process, including position, posture, test results, and energy consumption, and processes and analyzes the inspection data to evaluate the completion of the inspection task, equipment performance, and inspection results.

[0050] A further improvement of the technical solution of the present invention is that in step 5, the process of monitoring the energy consumption of the legged robot is:

[0051] Step 501, deploying a remote monitoring system, and using a monitoring center of the remote monitoring system to collect energy consumption data from an energy management system of the legged robot, wherein the energy consumption data is divided into historical energy consumption data and real-time energy consumption data;

[0052] Step 502, analyzing and processing the historical energy consumption data of the legged robot, analyzing the actual energy consumption and the benchmark energy consumption of each energy-consuming component of the legged robot, calculating the energy consumption evaluation index, and evaluating the energy consumption of the legged robot;

[0053] Step 503, classifying the energy consumption levels according to the energy consumption evaluation index into poor energy consumption level, general energy consumption level, good energy consumption level and excellent energy consumption level, and assigning corresponding evaluation thresholds to different energy consumption levels;

[0054] Step 504, compare the real-time energy consumption data of the legged robot with the classification standard, evaluate its energy consumption level, and generate an energy consumption report based on the energy consumption evaluation result, feedback the energy consumption status of the legged robot to relevant personnel, and remotely and dynamically adjust the operation mode of the legged robot.

[0055] A further improvement of the technical solution of the present invention is that the calculation formula of the energy consumption evaluation index is:

[0056]

[0057] Among them, E is the energy consumption evaluation index, which is used to quantitatively evaluate the energy efficiency of the legged robot, T is the length of the evaluation period, M is the number of energy-consuming components of the legged robot involved in the evaluation, and E l (t) is the actual energy consumption of the energy-consuming component of the l-th legged robot at time t, E s , l is the standard energy consumption value of the energy-consuming component of the l-th legged robot, which is used as a benchmark for energy consumption comparison. and Respectively represent the maximum and minimum values ​​among all energy-consuming components, It is the average deviation of the energy consumption of all energy-consuming components from their standard values ​​at each time point t;

[0058] A plurality of the energy consumption levels correspond to a plurality of the evaluation thresholds, wherein the evaluation thresholds include an upper threshold and a lower threshold;

[0059] The multiple energy consumption levels and the multiple evaluation thresholds satisfy the following relationship:

[0060] Excellent energy consumption level E≤E ln ; It means that the energy efficiency of the legged robot is very high, and the actual energy consumption is very close to the standard value;

[0061] Good energy consumption class E ln <E≤E yn ; It means that the energy efficiency of legged robots is relatively high, but there is still room for optimization;

[0062] General energy consumption level E yn <E≤E cn ; It indicates that the energy efficiency of legged robots is at a medium level, which requires attention and possible adoption of some energy-saving measures;

[0063] Poor energy consumption level E>E cn ; It indicates that the energy efficiency of legged robots is low, and the actual energy consumption is much higher than the standard value, and immediate measures need to be taken to optimize it;

[0064] Among them, E is the energy consumption evaluation index, E ln is the lower threshold corresponding to the good energy consumption level and the upper threshold corresponding to the excellent energy consumption level, E yn is the lower threshold corresponding to the general energy consumption level and the upper threshold corresponding to the good energy consumption level, E cn It is the lower threshold corresponding to the poor energy consumption level and the upper threshold corresponding to the general energy consumption level.

[0065] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0066] 1. The present invention provides a control method for an operational intelligent legged robot used for power inspection. Through the multimodal sensor on board, all-round scanning and detection of power facilities are realized. The robot's autonomous navigation capability, combined with SLAM technology, enables it to perform effective path planning and autonomous inspection in unknown environments, which not only avoids collision between the robot and obstacles and reduces the risk of damage, but also reduces dependence on manual labor, expands the scope of inspection, and ensures the continuity and integrity of inspection.

[0067] 2. The present invention provides a control method for an operational intelligent legged robot used for power inspection. By monitoring the energy consumption of the robot and combining the energy consumption evaluation index, the operation mode is dynamically adjusted according to the current energy consumption level and task requirements to reduce energy consumption and extend the battery life. The method can reasonably allocate and efficiently utilize energy according to the actual energy consumption situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0069] Figure 1 is a flow chart of the method of the present invention;

[0070] Figure 2 A flow chart for planning the optimal inspection path for the present invention;

[0071] Figure 3 This is a flow chart of monitoring the energy consumption of a legged robot according to the present invention. DETAILED DESCRIPTION

[0072] 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.

[0073] Embodiment 1, as Figure 1 , Figure 2 As shown, the present invention provides a control method for an operational intelligent foot-type robot applied to power inspection, comprising the following steps:

[0074] Step 1, based on the multimodal sensor carried by the legged robot, perform all-round scanning and detection of the surrounding environment to collect environmental data, and fuse the collected environmental data, wherein the multimodal sensor includes a front stereo vision sensor, a rear stereo vision sensor and a three-dimensional solid-state laser radar, start the multimodal sensor carried by the legged robot to collect original environmental data, wherein the multimodal sensor includes a front stereo vision sensor, a rear stereo vision sensor and a three-dimensional solid-state laser radar, the front stereo vision sensor is used to collect image data of the environment in front of the legged robot, including color, shape and texture information, the rear stereo vision sensor is used to collect image data of the rear environment for rear perception and obstacle avoidance of the legged robot, the three-dimensional solid-state laser radar is used to perform a 360-degree all-round scan, collect distance and shape information of the surrounding environment, and fuse the multimodal sensor The robot collects raw environmental data for denoising and filtering preprocessing operations, performs time synchronization and spatial registration on the preprocessed environmental data, so that all sensor data are synchronized in time to reflect the environmental status at the same time, and converts the data of different sensors into the same coordinate system for spatial registration. It applies data fusion technology to fuse the data of the front stereo vision sensor, the rear stereo vision sensor and the three-dimensional solid-state laser radar, and extracts edge, corner and texture features from the fused data, performs feature matching, identifies objects and obstacles in the environment, and uses the extracted features and preprocessed environmental data to construct a three-dimensional model of the surrounding environment, which provides a basis for the navigation, obstacle avoidance and path planning of the legged robot. Based on the fused data and the constructed three-dimensional model of the surrounding environment, the robot can identify the traversable area, obstacles and terrain changes.

[0075] Step 2: Evaluate the complexity of the environment based on the fused environmental data, determine whether there are extreme terrain or obstacles, and plan the optimal inspection path based on the environmental complexity evaluation results and environmental mapping results to ensure that the legged robot can complete the inspection task efficiently and safely. Use the three-dimensional model of the surrounding environment to analyze the terrain characteristics in the environment, identify obstacles in the environment, and record the number, size, shape and position of obstacles. Among them, terrain characteristics include slope and unevenness, and obstacles include rocks, ditches, vegetation, etc. According to the terrain characteristics and obstacle distribution, comprehensively analyze the terrain undulation, obstacle density, terrain type diversity and the relative distance between the legged robot and the obstacle, and calculate the environmental complexity evaluation index. Among them, the terrain undulation is obtained by calculating the standard deviation of the terrain height, the obstacle density is obtained by calculating the ratio of the obstacle data to the area covered by the travel path, and the terrain type diversity is obtained by calculating the ratio of the obstacle data to the area covered by the travel path. The accuracy is obtained by calculating the cumulative frequency of each obstacle type in the inspection area and calculating its Gini coefficient. The environmental complexity assessment index is used to preset the risk threshold to assess the environmental complexity, so as to determine whether there is extreme terrain or obstacle risk in the environment. If it exceeds the preset risk threshold, it is determined that there is extreme terrain or obstacle risk in the environment. Based on the evaluation results of the environmental complexity, the factors leading to high environmental complexity are further analyzed, and the specific terrain features or obstacle types are identified. According to the inspection task requirements, the starting and ending positions of the legged robot are determined. Based on the analysis results of the terrain features or obstacle types, a path planning algorithm is used to generate multiple candidate paths from the starting point to the end point, and the generated candidate paths are optimized and evaluated. The path length, complexity and risk are comprehensively analyzed to avoid high-risk areas and reduce path complexity, reduce the passage through areas with large terrain fluctuations and dense obstacles, and plan the optimal inspection path.

[0076] Furthermore, the calculation formula of the environmental complexity evaluation index is:

[0077]

[0078] V = 1-Gi(tp);

[0079]

[0080] Among them, ECI is the environmental complexity assessment index, Q is the terrain undulation, Q = std(h), h is the terrain height dataset, N is the number of obstacles, that is, the total number of obstacles encountered on the path of the legged robot, and D is the obstacle distribution density, which indicates the number of obstacles per unit area. A is the area covered by the path of the legged robot, V is the diversity of terrain types, Gi(tp) is the Gini coefficient, and A iis the cumulative frequency of the ith obstacle type, n is the total number of obstacle types, R is the inverse of the relative distance between the legged robot and the nearest obstacle, r is the distance between the legged robot and each obstacle, w1, w2, w3, w4, w5, w6 are weight coefficients. It should be noted that when the ECI value is high, it means that the environment complexity is high and the legged robot has great difficulty in performing the task. When the ECI value is low, it means that the environment complexity is low and the legged robot has little difficulty in performing the task. ECI increases with the increase of the number of obstacles N and the density of obstacle distribution D. ECI increases with the increase of terrain undulation Q. ECI decreases with the increase of terrain diversity V. ECI increases with the decrease of the relative distance R between the robot and the obstacle. The closer the distance, the higher the urgency of obstacle avoidance.

[0081] Step 3. According to the inspection path planning results and inspection task requirements, the posture of the manipulator and the legged robot is adjusted, and the SLAM technology is used for real-time positioning and environmental mapping to adapt to the current terrain and environmental conditions, so that the legged robot can navigate autonomously in an unknown environment. Among them, the SLAM technology is based on the synchronous positioning and mapping system, that is, the SLAM system, which analyzes the operations that the manipulator needs to perform in the inspection task, and plans the initial posture of the manipulator and the position of the end effector according to the task requirements to ensure that it can accurately reach the target position and perform the predetermined operation. The dynamic model of the manipulator is used to calculate the joint angle and torque required to achieve the specified posture to ensure that the manipulator moves smoothly and meets the mechanical requirements. Based on the surrounding environment data collected by the multimodal sensor, the surrounding environment data is preprocessed and matched with the current sensor data and the historical terrain. The method comprises the following steps: matching feature points of the image, initializing the simultaneous localization and mapping (SLAM) system, calibrating the sensor and the initial position setting, and associating the matching feature points with the features on the historical map; using the position information provided by the SLAM system to estimate the state of the legged robot, determining the position and orientation of the legged robot in the environment, updating the two-dimensional grid map of the surrounding environment according to the multimodal sensor data and the positioning result, and receiving new multimodal sensor data during the movement of the legged robot, updating the positioning and map information in real time, tracking the preset optimal inspection path based on the updated two-dimensional grid map of the surrounding environment and the terrain features, and dynamically adjusting the path and the posture of the legged robot in combination with the actual terrain and environmental changes to adapt to the current terrain and environmental conditions, so that the legged robot can autonomously navigate in an unknown environment to ensure stable walking and crossing obstacles;

[0082] Furthermore, the process of updating the two-dimensional grid map of the surrounding environment is:

[0083] Obstacle edges and terrain undulation features in the environment are extracted from multimodal sensor data, and an initial two-dimensional array is created to represent a two-dimensional grid map of the environment, and each cell records the obstacle edges and terrain undulation features. The extracted obstacle edges and terrain undulation features are combined with the determined position and orientation results of the legged robot in the environment to update the two-dimensional grid map of the surrounding environment. For each grid cell of the two-dimensional grid map, the grid occupancy probability is updated, and when the legged robot moves, the multimodal sensor data is continuously collected, and SLAM processing is performed in real time to determine the current position of the legged robot in the environment. The positioning of the legged robot is updated by combining historical map information and current multimodal sensor data. According to the output of the real-time SLAM system and the new multimodal sensor data, the grid map is continuously updated to reflect the latest status of the environment.

[0084] Furthermore, the expression for updating the grid occupancy probability is:

[0085]

[0086] p(0|z t , x t , m k )=1-p(1|z t , x t , m k );

[0087] Among them, p(m k |z 1∶t , x 1∶t ) is given all sensor data z 1∶t and all state estimates x 1∶t Under the condition of , the probability that the kth grid is occupied, p occ is the prior probability that the grid is occupied, p(0|z t , x t , m k ) is the given sensor data z t , state estimate x t and grid m k Under the condition of t , x t , m k ) is the given sensor data z t , state estimate x t and grid m k Under the condition of k |z 1∶t-1 , x 1∶t-1 ) represents the probability that the kth grid was occupied at the last moment, p(z t |1,x t , mk ) indicates that sensor data z is obtained when the grid is occupied t The probability, p(z t |0,x t , m k ) indicates that sensor data z is obtained when the grid is idle t probability;

[0088] Step 4, performing power inspection tasks of the legged robot and the robotic arm, and making dynamic adjustments according to the real-time operating status and performance parameters of the legged robot and the robotic arm, wherein the status of the legged robot and the robotic arm includes position, posture, and energy consumption;

[0089] Step 5: Establish a remote monitoring system to monitor the energy consumption of the legged robot in real time, and dynamically adjust the operation mode of the legged robot according to the energy consumption monitoring results.

[0090] Embodiment 2, as Figure 3 As shown, based on Example 1, the present invention provides a technical solution: Preferably, in step 4, the dynamic adjustment process is:

[0091] According to the inspection lines, inspection points, and inspection standard requirements of power inspection, the inspection paths and task sequences of the legged robots and robotic arms are planned, and the inspection tasks are assigned to the legged robots and robotic arms, and their respective task areas, inspection contents, and time requirements are clarified. During the execution of the tasks, the position and posture changes of the legged robots and robotic arms are monitored in real time through GPS, IMU, and visual odometer sensors, and the battery power consumption is monitored in real time to predict the remaining battery life. The joint angle, speed, and strength performance parameters of the robotic arms, as well as the gait stability and ground adaptability of the legged robots are monitored to ensure that they are in good working condition. The synchronous positioning and mapping technology and grid maps are used for path planning, and the paths are dynamically adjusted according to environmental changes and the status of the legged robots. According to the inspection progress, importance, and urgency, the inspection tasks and priorities of the robotic arms are dynamically adjusted to ensure that key inspection points are inspected in a timely manner. The key data in the inspection process, including position, posture, inspection results, and energy consumption, are recorded, and the inspection data is processed and analyzed to evaluate the completion of the inspection tasks, equipment performance, and inspection results.

[0092] In step 5, the process of monitoring the energy consumption of the legged robot is as follows:

[0093] Deploy a remote monitoring system, and use the monitoring center of the remote monitoring system to collect energy consumption data from the energy management system of the legged robot, where the energy consumption data is divided into historical energy consumption data and real-time energy consumption data. Analyze and process the historical energy consumption data of the legged robot, analyze the actual energy consumption and benchmark energy consumption of each energy-consuming component of the legged robot, calculate the energy consumption evaluation index, evaluate the energy consumption of the legged robot, divide the energy consumption level according to the energy consumption evaluation index, and divide it into poor energy consumption level, general energy consumption level, good energy consumption level and excellent energy consumption level, and divide the corresponding evaluation thresholds for different energy consumption levels, compare the real-time energy consumption data of the legged robot with the classification standard, evaluate its energy consumption level, and generate an energy consumption report based on the energy consumption evaluation results, feedback the energy consumption status of the legged robot to relevant personnel, and remotely and dynamically adjust the operation mode of the legged robot;

[0094] Furthermore, the calculation formula of the energy consumption assessment index is:

[0095]

[0096] Among them, E is the energy consumption evaluation index, which is used to quantitatively evaluate the energy efficiency of the legged robot, T is the length of the evaluation period, M is the number of energy-consuming components of the legged robot involved in the evaluation, and E l (t) is the actual energy consumption of the energy-consuming component of the l-th legged robot at time t, E s , l is the standard energy consumption value of the energy-consuming component of the l-th legged robot, which is used as a benchmark for energy consumption comparison. and Respectively represent the maximum and minimum values ​​among all energy-consuming components, It is the average deviation of the energy consumption of all energy-consuming components at each time point t from their standard values. It should be noted that when the actual energy consumption of all energy-consuming components of the legged robot is very close to its standard value, E approaches 0. The smaller the value of E, the higher the energy efficiency of the legged robot, that is, the closer the actual energy consumption is to the standard value; conversely, the larger the value of E, the lower the energy efficiency, and the greater the deviation between the actual energy consumption and the standard value.

[0097] Furthermore, the multiple energy consumption levels correspond to multiple evaluation thresholds, wherein the evaluation thresholds include an upper threshold and a lower threshold;

[0098] Multiple energy consumption levels and multiple evaluation thresholds satisfy the following relationship:

[0099] Excellent energy consumption level E≤E ln ; It means that the energy efficiency of the legged robot is very high, and the actual energy consumption is very close to the standard value;

[0100] Good energy consumption class E ln <E≤E yn; It means that the energy efficiency of legged robots is relatively high, but there is still room for optimization;

[0101] General energy consumption level E yn <E≤E cn ; It indicates that the energy efficiency of legged robots is at a medium level, which requires attention and possible adoption of some energy-saving measures;

[0102] Poor energy consumption level E>E cn ; It indicates that the energy efficiency of legged robots is low, and the actual energy consumption is much higher than the standard value, and immediate measures need to be taken to optimize it;

[0103] Among them, E is the energy consumption evaluation index, E ln is the lower threshold corresponding to the good energy consumption level and the upper threshold corresponding to the excellent energy consumption level, E yn is the lower threshold corresponding to the general energy consumption level and the upper threshold corresponding to the good energy consumption level, E cn Suggestions for dynamically adjusting the operation mode based on the lower threshold corresponding to the poor energy consumption level and the upper threshold corresponding to the normal energy consumption level:

[0104] When E is in "excellent energy consumption level" or "good energy consumption level", the current operation mode is maintained, but the energy consumption situation continues to be monitored to prevent abnormal fluctuations;

[0105] When E is at the "general energy consumption level", analyze which time periods or energy-consuming components have high energy consumption, and optimize them in a targeted manner. Adjust the operation mode, such as reducing speed, reducing load, optimizing paths, etc., to reduce energy consumption. Regularly inspect and maintain the equipment to ensure that it operates in the best condition.

[0106] When E is in the "poor energy consumption level", take emergency measures immediately, such as suspending non-critical tasks, adjusting task priorities, etc., to reduce energy, and conduct in-depth analysis of the reasons for the high energy consumption, which may be equipment failure, improper operation, environmental factors, etc. Based on the analysis results, formulate a detailed energy-saving plan, including short-term and long-term improvement measures.

[0107] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the 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 control method for an operational intelligent foot-type robot applied to power inspection, characterized in that: The following steps are involved: Step 1: Performing all-round scanning and detection of the surrounding environment based on the multimodal sensor carried by the legged robot to collect environmental data and fuse the collected environmental data, wherein the multimodal sensor includes a front stereo vision sensor, a rear stereo vision sensor and a three-dimensional solid-state laser radar; Step 2: Evaluate the complexity of the environment based on the fused environmental data, determine whether there are extreme terrain or obstacles, and plan the optimal inspection path based on the environmental complexity evaluation results and environmental mapping results; Step 3: According to the inspection path planning results and inspection task requirements, the posture of the manipulator and the legged robot is adjusted, and the SLAM technology is used for real-time positioning and environmental mapping to adapt to the current terrain and environmental conditions, so that the legged robot can autonomously navigate in an unknown environment. The SLAM technology is based on the simultaneous positioning and mapping system, which is the SLAM system. Step 4, performing power inspection tasks of the legged robot and the robotic arm, and making dynamic adjustments according to the real-time operating status and performance parameters of the legged robot and the robotic arm; Step 5: Establish a remote monitoring system to monitor the energy consumption of the legged robot in real time, and dynamically adjust the operation mode of the legged robot according to the energy consumption monitoring results.

2. The control method of the operating intelligent foot-type robot for power inspection according to claim 1 is characterized in that: In step 1, the process of fusing the collected environmental data is as follows: Step 101, start the multimodal sensor carried by the legged robot to collect raw environmental data, wherein the multimodal sensor includes a front stereo vision sensor, a rear stereo vision sensor and a three-dimensional solid-state laser radar, the front stereo vision sensor is used to collect image data of the front environment of the legged robot, including color, shape and texture information, the rear stereo vision sensor is used to collect image data of the rear environment for rear perception and obstacle avoidance of the legged robot, and the three-dimensional solid-state laser radar is used to perform 360-degree omnidirectional scanning to collect distance and shape information of the surrounding environment; Step 102, performing pre-processing operations of denoising and filtering on the raw environmental data collected by the multimodal sensor; Step 103, time synchronization and spatial registration are performed on the preprocessed environmental data, so that all sensor data are synchronized in time to reflect the environmental state at the same time, and the data of different sensors are converted into the same coordinate system for spatial registration; Step 104, using data fusion technology to fuse the data of the front stereo vision sensor, the rear stereo vision sensor and the three-dimensional solid-state laser radar, and extracting edge, corner point and texture features from the fused data, performing feature matching, and identifying objects and obstacles in the environment; Step 105, using the extracted features and the pre-processed environmental data, constructing a three-dimensional model of the surrounding environment, and based on the fused data and the constructed three-dimensional model of the surrounding environment, enabling the robot to identify traversable areas, obstacles, and terrain changes.

3. The control method of the operating intelligent foot-type robot for power inspection according to claim 2 is characterized in that: In step 2, the process of planning the optimal inspection path is as follows: Step 201, using the three-dimensional model of the surrounding environment to analyze the terrain features in the environment, identify obstacles in the environment, and record the number, size, shape and location of the obstacles; Step 202, based on the terrain characteristics and the distribution of obstacles, comprehensively analyze the terrain undulation, the density of obstacles, the diversity of terrain types and the relative distance between the legged robot and the obstacles, and calculate the environmental complexity evaluation index, wherein the terrain undulation is obtained by calculating the standard deviation of the terrain height, the density of obstacles is obtained by calculating the ratio of the obstacle data to the area covered by the travel path, and the diversity of terrain types is obtained by calculating the cumulative frequency of each obstacle type in the inspection area and calculating its Gini coefficient; Step 203, using the environment complexity assessment index and the preset risk threshold to assess the environment complexity, so as to determine whether there is an extreme terrain or obstacle risk in the environment. If the preset risk threshold is exceeded, it is determined that there is an extreme terrain or obstacle risk in the environment, and based on the assessment result of the environment complexity, further analyze the factors leading to high environment complexity, and identify specific terrain features or obstacle types; Step 204, determining the starting position and the end position of the legged robot according to the inspection task requirements, and generating multiple candidate paths from the starting point to the end point using a path planning algorithm based on the analysis results of terrain features or obstacle types; Step 205, optimize and evaluate the generated candidate paths, comprehensively analyze the path length, complexity and risk, avoid high-risk areas and reduce path complexity, reduce the passage through areas with large terrain fluctuations and dense obstacles, and plan the optimal inspection path.

4. The control method of the operating intelligent foot-type robot for power inspection according to claim 3 is characterized in that: The calculation formula of the environmental complexity evaluation index is: V = 1-Gi(tp); Among them, ECI is the environmental complexity assessment index, Q is the terrain undulation, Q = std(h), h is the terrain height dataset, N is the number of obstacles, that is, the total number of obstacles encountered on the path of the legged robot, and D is the obstacle distribution density, which indicates the number of obstacles per unit area. A is the area covered by the path of the legged robot, V is the diversity of terrain types, Gi(tp) is the Gini coefficient, and A i is the cumulative frequency of the ith obstacle type, n is the total number of obstacle types, R is the inverse of the relative distance between the legged robot and the nearest obstacle, r is the distance between the legged robot and each obstacle, and w1, w2, w3, w4, w5, and w6 are weight coefficients.

5. The control method of the operating intelligent foot-type robot for power inspection according to claim 4 is characterized in that: In step 3, the process of autonomous navigation of the legged robot in an unknown environment is as follows: Step 301, analyzing the operations that the robot arm needs to perform in the inspection task, planning the initial posture of the robot arm and the position of the end effector according to the task requirements, and calculating the joint angle and torque required to achieve the specified posture; Step 302, based on the surrounding environment data collected by the multimodal sensor, after preprocessing the surrounding environment data, matching feature points of the current sensor data with the historical map, and initializing the synchronous positioning and mapping system, calibrating the sensor and the initial position setting, and associating the matching feature points with the features on the historical map, and using the position information provided by the SLAM system to estimate the state of the legged robot, and determine the position and direction of the legged robot in the environment; Step 303, updating the two-dimensional grid map of the surrounding environment according to the multimodal sensor data and the positioning result, and receiving new multimodal sensor data during the movement of the legged robot to update the positioning and map information in real time; Step 304, based on the updated two-dimensional grid map of the surrounding environment and terrain features, track the preset optimal inspection path, combine the actual terrain and environmental changes, dynamically adjust the path and the posture of the legged robot to adapt to the current terrain and environmental conditions, and enable the legged robot to autonomously navigate in an unknown environment.

6. The control method of the operating intelligent foot-type robot for power inspection according to claim 5 is characterized in that: The process of updating the two-dimensional grid map of the surrounding environment is as follows: Step 3031, extracting obstacle edges and terrain undulation features in the environment from the multimodal sensor data; Step 3032, creating an initial two-dimensional array to represent a two-dimensional grid map of the environment, and each cell records the edge of the obstacle and the terrain undulation characteristics; Step 3033, combining the extracted obstacle edges and terrain undulation features with the determined position and orientation results of the legged robot in the environment, and updating the two-dimensional grid map of the surrounding environment; Step 3034, for each grid unit of the two-dimensional grid map, the grid occupancy probability is updated, and when the legged robot moves, the multimodal sensor data is continuously collected, and SLAM processing is performed in real time to determine the current position of the legged robot in the environment, and the positioning of the legged robot is updated by combining the historical map information and the current multimodal sensor data; Step 3035, based on the output of the real-time SLAM system and the new multimodal sensor data, the grid map is continuously updated to reflect the latest state of the environment.

7. The control method of the operating intelligent foot-type robot for power inspection according to claim 6 is characterized in that: The expression for updating the grid occupancy probability is: p(0|z t ,x t ,m k )=1-p(1|z t ,x t ,m k ); Among them, p(m k |z 1∶t , x 1∶t ) is given all sensor data z 1∶t and all state estimates x 1∶t Under the condition of , the probability that the kth grid is occupied, p occ is the prior probability that the grid is occupied, p(0|z t , x t , m k ) is the given sensor data z t , state estimate x t and grid m k Under the condition of t , x t , m k ) is the given sensor data z t , state estimate x t and grid m k Under the condition of k |z 1∶t-1 , x 1∶t-1 ) represents the probability that the kth grid was occupied at the last moment, p(z t |1,x t , m k ) indicates that sensor data z is obtained when the grid is occupied t The probability, p(z t |0,x t , m k ) indicates that sensor data z is obtained when the grid is idle t probability.

8. The control method of the operating intelligent foot-type robot for power inspection according to claim 7 is characterized in that: In step 4, the dynamic adjustment process is as follows: Step 401, according to the inspection lines, inspection points, and inspection standard requirements of the power inspection, the inspection paths and task sequences of the legged robot and the robotic arm are planned, and the inspection tasks are assigned to the legged robot and the robotic arm, and their respective task areas, inspection contents, and time requirements are clarified; Step 402, during the task execution, the position and posture changes of the legged robot and the manipulator are monitored in real time through GPS, IMU and visual odometer sensors, and the battery power consumption is monitored in real time to predict the remaining battery life, and the joint angle, speed, and strength performance parameters of the manipulator are monitored, as well as the gait stability and ground adaptability of the legged robot; Step 403, using synchronous positioning and mapping technology and grid map to plan the path, and dynamically adjust the path according to environmental changes and the state of the legged robot, and dynamically adjust the inspection tasks and priorities of the robot arm according to the inspection progress, importance and urgency; Step 404, records key data during the inspection process, including position, posture, test results, and energy consumption, and processes and analyzes the inspection data to evaluate the completion of the inspection task, equipment performance, and inspection results.

9. The control method of the operating intelligent foot-type robot for power inspection according to claim 8, characterized in that: In step 5, the process of monitoring the energy consumption of the legged robot is as follows: Step 501, deploying a remote monitoring system, and using a monitoring center of the remote monitoring system to collect energy consumption data from an energy management system of the legged robot, wherein the energy consumption data is divided into historical energy consumption data and real-time energy consumption data; Step 502, analyzing and processing the historical energy consumption data of the legged robot, analyzing the actual energy consumption and the benchmark energy consumption of each energy-consuming component of the legged robot, calculating the energy consumption evaluation index, and evaluating the energy consumption of the legged robot; Step 503, classifying the energy consumption levels according to the energy consumption evaluation index into poor energy consumption level, general energy consumption level, good energy consumption level and excellent energy consumption level, and assigning corresponding evaluation thresholds to different energy consumption levels; Step 504, compare the real-time energy consumption data of the legged robot with the classification standard, evaluate its energy consumption level, and generate an energy consumption report based on the energy consumption evaluation result, feedback the energy consumption status of the legged robot to relevant personnel, and remotely and dynamically adjust the operation mode of the legged robot.

10. The control method of the operating intelligent foot-type robot for power inspection according to claim 9, characterized in that: The calculation formula of the energy consumption assessment index is: Where E is the energy consumption evaluation index, T is the length of the evaluation period, M is the number of energy-consuming components of the legged robot involved in the evaluation, and E l (t) is the actual energy consumption of the energy-consuming component of the l-th legged robot at time t, E s , l is the standard energy consumption value of the energy-consuming components of the l-th legged robot, and Respectively represent the maximum and minimum values ​​among all energy-consuming components, It is the average deviation of the energy consumption of all energy-consuming components relative to their standard values ​​at each time point t.

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