CN113985884A - Power inspection robot path planning method and system and robot - Google Patents
Power inspection robot path planning method and system and robot Download PDFInfo
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Abstract
The invention provides a power inspection robot path planning method, a power inspection robot path planning system and a power inspection robot, wherein the method comprises the steps of receiving a target position inspection instruction; calculating the distance between the initial position and the target position by taking the current position as the initial position; calculating an optimal path between the initial position and the target position by adopting a path planning algorithm, so that the inspection robot adopts the optimal path from the initial position to the target position; selecting a sample node with the minimum cost as a transfer point of the inspection robot in a single-movement state by a path optimization algorithm; the minimum cost sample node is the node with the minimum sum of Euclidean distances from the initial position to the target position. Based on the method, a power inspection robot path planning system and a robot are also provided. The invention provides an optimal patrol route method for a patrol robot in the process of patrolling the position of a target interval, and the patrol robot can safely avoid obstacles in a complex environment and reach a specified target interval to complete a patrol task.
Description
Technical Field
The invention belongs to the technical field of intelligent inspection of electric power systems, and particularly relates to a method and a system for planning paths of an electric power inspection robot and the robot.
Background
With the actual requirement of intelligent power inspection, the path planning of the inspection robot is more and more challenging and is also a valuable research direction. The path planning is to find a feasible path from a starting point to a target point of the robot on the premise of avoiding collision with an obstacle. In recent years, many path planning algorithms have been proposed, and currently, common navigation algorithms are: the a-star Algorithm [, Genetic Algorithm (Genetic Algorithm), Artificial Potential Field Method (APFM), ant colony Algorithm, Probabilistic Roadmapping (PRM), and fast-search random tree (RRT). Compared with other algorithms, the RRT algorithm has the obvious advantages of high expansion speed and suitability for high-dimensional environment, so that the RRT algorithm is rapidly developed and applied, but asymptotic optimization cannot be guaranteed.
In order to improve the efficiency of the RRT algorithm, more and more scholars improve it, and j.j.kuffner et al propose a bidirectional random tree (RRT-Connect) to further shorten the search time of the random tree, but other advantages are not obvious. Karaman et al propose the RRT algorithm, which optimizes the random tree continuously as the number of samples increases in the exploration. Although the RRT algorithm finds the initial path faster than the RRT algorithm, it has a slow convergence speed. Nasir J et al propose RRT-smart, add intelligent sampling and path optimization methods to RRT algorithm, which converges to the optimum faster than RRT, but RRT-smart explores the entire configuration space, still requiring thousands of iterations to converge to the optimum path. Therefore, the existing RRT algorithm also has the technical problems of low convergence speed, poor target guidance performance and more path inflection points in the robot inspection process.
Disclosure of Invention
In order to solve the technical problems, the invention provides a power inspection robot path planning method, a power inspection robot path planning system and a power inspection robot, so that the inspection robot can safely avoid obstacles in a complex environment, reach a specified target interval and complete an inspection task.
In order to achieve the purpose, the invention adopts the following technical scheme:
a power inspection robot path planning method comprises the following steps:
receiving a target position patrol instruction;
calculating the distance between the initial position and the target position by taking the current position as the initial position;
calculating an optimal path between an initial position and a target position by adopting a path planning algorithm, so that the inspection robot adopts the optimal path from the initial position to the target position; the path optimization algorithm selects a sample node with the minimum cost between an initial position and a target position as a transfer point of the inspection robot in a single-movement state; the minimum cost sample node is the node with the minimum sum of Euclidean distances from the initial position to the target position.
Further, after the instruction of the target position inspection is received, the inspection robot enters a path planning working mode.
Further, the method for calculating the distance between the initial position and the target position includes acquiring the distance between the initial position and the target position by a laser sensor and an odometer provided on the inspection robot.
Further, the method for selecting the sample node with the minimum cost between the initial position and the target position as the transfer point of the inspection robot in the single-movement state comprises the following steps:
selecting n sample nodes, and calculating the cost of each sample node;
selecting a sample node with the minimum cost as a final sampling point qrand(ii) a Wherein F (q)n)＝R(qn)+G(qn) (ii) a Wherein F (q)n) Is a sample node qnThe cost of (d); r (q)n) Representing random sample nodes qnEuclidean distance to the initial position; g (q)n) Representing random sample nodes qnEuclidean distance to the target location.
Further, the method for optimizing the path includes:
if no barrier is detected between two nonadjacent nodes, the electric power inspection robot moves linearly;
and if the obstacle is detected between two non-adjacent nodes, the electric power inspection robot adopts optimized sampling, and selects a sample node with the minimum cost as a transfer point of the inspection robot in a single-movement state.
Further, the method further comprises: and judging whether the stay time of the inspection robot in a certain area exceeds a time threshold, if so, returning the original path to the previous normal moving point position, and then replanning the path to the target position.
The invention also provides a power inspection robot path planning system, which comprises a receiving module, a calculating module and an optimizing module;
the receiving module is used for receiving a target position patrol instruction;
the calculation module is used for calculating the distance between the initial position and the target position by taking the current position as the initial position;
the optimization module is used for calculating an optimal path from an initial position to a target position by adopting a path planning algorithm, so that the inspection robot adopts the optimal path from the initial position to the target position; the path optimization algorithm selects a sample node with the minimum cost between an initial position and a target position as a transfer point of the inspection robot in a single-movement state; the minimum cost sample node is the node with the minimum sum of Euclidean distances from the initial position to the target position.
Further, the system also comprises a judging module;
the judging module is used for judging whether the staying time of the inspection robot in a certain area exceeds a time threshold, if the staying time exceeds the time threshold, the original path returns to the upper normal moving point position, and then the path is planned again to the target position.
Further, the path optimization module executes the following process: if no barrier is detected between two nonadjacent nodes, the electric power inspection robot moves linearly;
and if the obstacle is detected between two non-adjacent nodes, the electric power inspection robot adopts optimized sampling, and selects a sample node with the minimum cost as a transfer point of the inspection robot in a single-movement state.
The invention also provides an electric power inspection robot, which is a robot for inspection by adopting the electric power inspection robot path planning method.
The effect provided in the summary of the invention is only the effect of the embodiment, not all the effects of the invention, and one of the above technical solutions has the following advantages or beneficial effects:
the invention provides a power inspection robot path planning method, a power inspection robot path planning system and a power inspection robot, wherein the method comprises the steps of receiving a target position inspection instruction; calculating the distance between the initial position and the target position by taking the current position as the initial position; calculating an optimal path between the initial position and the target position by adopting a path planning algorithm, so that the inspection robot adopts the optimal path from the initial position to the target position; selecting a sample node with the minimum cost between the initial position and the target position as a transfer point of the inspection robot in a single-movement state by a path optimization algorithm; the minimum cost sample node is the node with the minimum sum of Euclidean distances from the initial position to the target position. Based on the power inspection robot path planning method, the power inspection robot path planning system and the robot are also provided. The invention provides a path planning algorithm, namely an improved RRT algorithm, and aims to solve the problems of low convergence speed, poor target guidance performance and more path inflection points in the RRT algorithm path planning in the prior art. The invention provides an optimal patrol route method for the patrol robot in the process of patrolling the target interval positions, so that the patrol robot can safely avoid obstacles in a complex environment and reach the specified target interval to complete the patrol task.
Drawings
Fig. 1 is a first flowchart of a path planning method for a power inspection robot according to embodiment 1 of the present invention;
fig. 2 is a second flowchart of a path planning method for a power inspection robot according to embodiment 1 of the present invention;
FIG. 3 is a schematic diagram comparing the path optimization strategies in embodiment 1 of the present invention;
fig. 4 is a schematic diagram illustrating a simulation of a path planning result in environment 1 according to embodiment 1 of the present invention;
fig. 5 is a schematic diagram illustrating a simulation of a path planning result in an environment 2 according to embodiment 1 of the present invention;
fig. 6 is a schematic diagram illustrating simulation of a path planning result in an environment 3 according to embodiment 1 of the present invention;
fig. 7 is a schematic diagram illustrating a simulation of a path planning result in an environment 4 according to embodiment 1 of the present invention;
fig. 8 is a schematic diagram of a power inspection robot path planning system according to embodiment 2 of the present invention.
Detailed Description
In order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
Example 1
The embodiment 1 of the invention provides a path planning method for an electric power inspection robot, which is used for providing an optimal inspection path for the inspection robot in the process of inspecting the position of a target interval, so that the inspection robot can safely avoid obstacles in a complex environment and reach a specified target interval to complete an inspection task. Fig. 1 shows a first flowchart of a path planning method for a power inspection robot according to embodiment 1 of the present invention;
the transformer substation operating personnel sends an instruction for inspecting at least one interval to the inspection robot through the wireless local area network, and the inspection robot enters a path planning mode for reaching the target interval position after receiving the target interval position information; then, accurately positioning the pose of the inspection robot by combining laser data acquired by a laser sensor and a milemeter which are loaded on the inspection robot with an observation model; the inspection robot continuously explores the peripheral space according to the accurate positioning of the current position, and calculates the optimal path reaching the target interval position by adopting an improved RRT algorithm; after the inspection robot reaches the destination, judging whether the position is a target interval position; if the position is not the target interval position, the current position is taken as the initial position, and an optimal path from the current position to the target interval position is re-planned.
Fig. 2 is a second flowchart of a path planning method for a power inspection robot according to embodiment 1 of the present invention; in the first flow chart, in the process that the inspection robot calculates the optimal path reaching the target interval position and moves from the current position of the inspection robot to the target interval position, the method also comprises the step that if the inspection robot continuously moves in one area and exceeds the preset time, the inspection robot is judged to enter the trap area; and if the inspection robot is judged to enter the trap area, the inspection robot is abandoned to move to the target interval position, returns to the initial position, and restarts to plan a new moving route of the inspection robot by improving the RRT algorithm.
The improved RRT algorithm in the present invention tends to solve the problems of sampling randomness and waypoint redundancy. The optimized sampling and path are realized by an Imsample function and a Pathoptimization function, and the specific implementation process is as follows:
in the invention, n sample nodes are selected, and the cost of each sample node is calculated; selecting the sample node with the minimum cost as the mostFinal sampling point qrand(ii) a Wherein F (q)n)＝R(qn)+G(qn) (ii) a Wherein F (q)n) Is a sample node qnThe cost of (d); r (q)n) Representing random sample nodes qnEuclidean distance to the initial position; g (q)n) Representing random sample nodes qnEuclidean distance to the target location. In practical application, a sampling mode from a point A (initial point) to a point B (target point) of the inspection robot is optimized, a plurality of random points are selected to calculate node costs, the sum of Euclidean distances from the node costs to the point A and the point B is compared, a minimum cost sampling point is selected, and the sampling point at the moment is a turning point of the inspection robot in a single-movement state based on the improved RRT algorithm.
FIG. 3 is a schematic diagram comparing the path optimization strategies in embodiment 1 of the present invention; in order to solve the problems of more inflection points and high path cost of the initial path, a path optimization strategy is provided. The path optimization process starts from a target point and ends from a starting point, and whether the two non-adjacent nodes are in the obstacle area or not is sequentially checked. If the route is not in the obstacle area, the route is directly connected, otherwise, the optimized route is obtained according to the optimization strategy shown in fig. 3, namely, the strategy based on the optimized sampling mentioned in the above paragraph, where the edges a, b and c, d in fig. 3(a) are the initial route and the optimized route, respectively, where the initial route a → b is the travel route implemented based on the original RRT algorithm, and the optimized route c → d is the travel route implemented based on the improved RRT algorithm. In fig. 3, (b) represents the pre-optimization path and (c) represents the post-optimization path, and the results show that the post-optimization path is reduced by 24.4%. In practical application, compared with the original RRT algorithm, the inspection robot based on the improved RRT algorithm can directly move in a straight line if no barrier is detected between two nonadjacent nodes in advance, so that more inflection points can be reduced; if the obstacle is detected in advance, a better sampling point is selected based on optimized sampling, the Euclidean distance between the advancing points is reduced, the path cost is reduced, and more optimized movement of the inspection robot is guaranteed.
The improved RRT algorithm is superior to the RRT algorithm in the aspects of planning time, path length and path node number, and simulation proves as follows.
The proposed modified RRT algorithm was simulation verified using MATLAB software, and the following simulation verifications were all performed in a static environment. The simulation process adopts 200-200 environment maps, and the positions of a starting point and a target point are marked in the maps. In the figure, black represents a static obstacle, red lines represent a path of the mobile robot, and blue lines represent a random tree generated during a path search. Comparing the RRT algorithm and the improved RRT algorithm in four different environments respectively, wherein the iteration times of the environments 1, 2 and 3 are 1000, the iteration time in the environment 4 is 3000, each comparison experiment is simulated for 50 times, and the average values are taken for comparison. Fig. 4 is a schematic diagram illustrating a simulation of a path planning result in environment 1 according to embodiment 1 of the present invention; fig. 5 is a schematic diagram illustrating a simulation of a path planning result in an environment 2 according to embodiment 1 of the present invention; fig. 6 is a schematic diagram illustrating simulation of a path planning result in an environment 3 according to embodiment 1 of the present invention; fig. 7 is a schematic diagram illustrating a simulation of a path planning result in an environment 4 according to embodiment 1 of the present invention; selecting three aspects of planning time, path length and path node number for comparison, wherein simulation experiment data obtained by comparison is shown in the following table:
the improved RRT algorithm is superior to the RRT algorithm in the aspects of planning time, path length and path node number. The results show that the node number of the improved RRT algorithm in the environments 1, 2, 3 and 4 is obviously reduced, the planning time is shortened by 80.4%, 84.2%, 84.4% and 90.9% compared with the RRT algorithm, and the path length is shortened by 7.6%, 17.5%, 1.9% and 7.2%. Therefore, the improved RRT algorithm provided by the invention is suitable for various different environments, can obtain paths with fewer search nodes and higher convergence rate, and has greater practical value.
The embodiment 1 of the invention provides an optimal patrol route method for a patrol robot in the process of patrolling the position of a target interval, so that the patrol robot can safely avoid obstacles in a complex environment, reach a specified target interval and complete a patrol task.
Example 2
Based on the power inspection robot path planning method provided by the embodiment 1 of the invention, the embodiment 2 of the invention provides a power inspection robot path planning system. Fig. 8 is a schematic diagram of a power inspection robot path planning system according to embodiment 2 of the present invention, where the system includes a receiving module, a calculating module, and an optimizing module;
the receiving module is used for receiving a target position patrol instruction;
the calculation module is used for calculating the distance between the initial position and the target position by taking the current position as the initial position;
the optimization module is used for calculating an optimal path from the initial position to the target position by adopting a path planning algorithm, so that the inspection robot adopts the optimal path from the initial position to the target position; selecting a sample node with the minimum cost between the initial position and the target position as a transfer point of the inspection robot in a single-movement state by a path optimization algorithm; the minimum cost sample node is the node with the minimum sum of Euclidean distances from the initial position to the target position.
The system also comprises a judging module;
the judging module is used for judging whether the staying time of the inspection robot in a certain area exceeds a time threshold, if the staying time exceeds the time threshold, the original path returns to the previous normal moving point position, and then the path is planned again to the target position.
The process of the path optimization algorithm in the invention is as follows: if no barrier is detected between two nonadjacent nodes, the electric power inspection robot moves linearly;
if the obstacle is detected between two non-adjacent nodes, the power inspection robot adopts optimized sampling, and the sample node with the minimum cost is selected as a turning point of the inspection robot in a single-movement state
The method for selecting the sample node with the minimum cost between the initial position and the target position as the transfer point of the inspection robot in the single-movement state comprises the following steps:
selecting n sample nodes, and calculating the cost of each sample node;
selecting a sample node with the minimum cost as a final sampling point qrand(ii) a Wherein F (q)n)＝R(qn)+G(qn) (ii) a Wherein F (q)n) Is a sample node qnThe cost of (d); r (q)n) Representing random sample nodes qnEuclidean distance to the initial position; g (q)n) Representing random sample nodes qnEuclidean distance to the target location.
Embodiment 2 of the invention provides a method for providing an optimal patrol route for a patrol robot in the process of patrolling the position of a target interval, so that the patrol robot can safely avoid obstacles and reach a specified target interval to complete a patrol task in a complex environment
Example 3
The embodiment 3 of the invention also provides the inspection robot, and the inspection robot adopts the power inspection robot path planning method in the embodiment 1 to perform inspection.
In the process of inspecting the position of the target interval, the method for providing the optimal inspection path for the inspection robot provided by the embodiment 3 of the invention enables the inspection robot to safely avoid the obstacle and reach the specified target interval in a complex environment, thereby completing the inspection task.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include elements inherent in the list. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element. In addition, parts of the above technical solutions provided in the embodiments of the present application, which are consistent with the implementation principles of corresponding technical solutions in the prior art, are not described in detail so as to avoid redundant description.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, the scope of the present invention is not limited thereto. Various modifications and alterations will occur to those skilled in the art based on the foregoing description. And are neither required nor exhaustive of all embodiments. On the basis of the technical scheme of the invention, various modifications or changes which can be made by a person skilled in the art without creative efforts are still within the protection scope of the invention.
Claims (10)
1. A power inspection robot path planning method is characterized by comprising the following steps:
receiving a target position patrol instruction;
calculating the distance between the initial position and the target position by taking the current position as the initial position;
calculating an optimal path between an initial position and a target position by adopting a path planning algorithm, so that the inspection robot adopts the optimal path from the initial position to the target position; the path optimization algorithm selects a sample node with the minimum cost between an initial position and a target position as a transfer point of the inspection robot in a single-movement state; the minimum cost sample node is the node with the minimum sum of Euclidean distances from the initial position to the target position.
2. The method for planning the path of the power inspection robot according to claim 1, wherein the inspection robot enters a path planning mode after receiving the command of the target position inspection.
3. The power inspection robot path planning method according to claim 1, wherein the distance between the initial position and the target position is calculated by acquiring the distance between the initial position and the target position through a laser sensor and an odometer provided on the inspection robot.
4. The method for planning the path of the power inspection robot according to claim 1, wherein the method for selecting the sample node with the minimum cost between the initial position and the target position as the transition point of the inspection robot in the single-movement state comprises the following steps:
selecting n sample nodes, and calculating the cost of each sample node;
selecting a sample node with the minimum cost as a final sampling point qrand(ii) a Wherein F (q)n)＝R(qn)+G(qn) (ii) a Wherein F (q)n) Is a sample node qnThe cost of (d); r (q)n) Representing random sample nodes qnEuclidean distance to the initial position; g (q)n) Representing random sample nodes qnEuclidean distance to the target location.
5. The power inspection robot path planning method according to claim 4, wherein the path optimization method comprises the following steps:
if no barrier is detected between two nonadjacent nodes, the electric power inspection robot moves linearly;
and if the obstacle is detected between two non-adjacent nodes, the electric power inspection robot adopts optimized sampling, and selects a sample node with the minimum cost as a transfer point of the inspection robot in a single-movement state.
6. The power inspection robot path planning method according to claim 1, further comprising: and judging whether the stay time of the inspection robot in a certain area exceeds a time threshold, if so, returning the original path to the previous normal moving point position, and then replanning the path to the target position.
7. A power inspection robot path planning system is characterized by comprising a receiving module, a calculating module and an optimizing module;
the receiving module is used for receiving a target position patrol instruction;
the calculation module is used for calculating the distance between the initial position and the target position by taking the current position as the initial position;
the optimization module is used for calculating an optimal path from an initial position to a target position by adopting a path planning algorithm, so that the inspection robot adopts the optimal path from the initial position to the target position; the path optimization algorithm selects a sample node with the minimum cost between an initial position and a target position as a transfer point of the inspection robot in a single-movement state; the minimum cost sample node is the node with the minimum sum of Euclidean distances from the initial position to the target position.
8. The power inspection robot path planning system according to claim 7, further comprising a determination module;
the judging module is used for judging whether the staying time of the inspection robot in a certain area exceeds a time threshold, if the staying time exceeds the time threshold, the original path returns to the upper normal moving point position, and then the path is planned again to the target position.
9. The power inspection robot path planning method according to claim 1, wherein the path optimization module executes the following process: if no barrier is detected between two nonadjacent nodes, the electric power inspection robot moves linearly;
and if the obstacle is detected between two non-adjacent nodes, the electric power inspection robot adopts optimized sampling, and selects a sample node with the minimum cost as a transfer point of the inspection robot in a single-movement state.
10. An electric power inspection robot, characterized in that the robot for inspection by the electric power inspection robot path planning method of claims 1 to 6 is adopted.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108803627A (en) * | 2018-08-20 | 2018-11-13 | 国网福建省电力有限公司 | A kind of crusing robot paths planning method suitable for substation's cubicle switch room |
| WO2021042519A1 (en) * | 2019-09-02 | 2021-03-11 | 平安科技（深圳）有限公司 | Assembly path planning method and related device |
-
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108803627A (en) * | 2018-08-20 | 2018-11-13 | 国网福建省电力有限公司 | A kind of crusing robot paths planning method suitable for substation's cubicle switch room |
| WO2021042519A1 (en) * | 2019-09-02 | 2021-03-11 | 平安科技（深圳）有限公司 | Assembly path planning method and related device |
Non-Patent Citations (1)
| Title |
|---|
| WEI WANG 等: "An Improved RRT* Path Planning Algorithm for Service Robot", 《2020 1EEE 4TH INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC2020)》, pages 1824 - 1828 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117435850A (en) * | 2023-12-20 | 2024-01-23 | 中交通力建设股份有限公司 | Road inspection method, system, equipment and medium based on improved greedy algorithm |
| CN117435850B (en) * | 2023-12-20 | 2024-03-19 | 中交通力建设股份有限公司 | Road inspection method, system, equipment and medium based on improved greedy algorithm |
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