Mobile robot path planning method for optimizing turning angle

Abstract

The invention discloses a mobile robot path planning method for optimizing a turning angle. The method comprises the steps of establishing a grid map and a right-hand rectangular coordinate system based on the environment where a mobile robot is located; calculating the movable angle of each node in the grid map and the difference between the angle of a newly added node and the angle of a previous path, and calculating the turning cost of the mobile robot in real time; and adding the cost function of the turning cost into a total cost function of an A* algorithm to obtain an optimized A* algorithm cost function, and performing path planning on an environment with an obstacle by using the A* algorithm cost function. According to the invention, path planning is carried out on an environment with obstacles, a smooth path with fewer unnecessary turns and a smaller turning angle can be obtained, and the time complexity of an algorithm is greatly reduced.

Description

technical field [0001] The invention relates to the technical fields of path planning, signal processing, and the Internet of Things, in particular to a path planning method for a mobile robot that optimizes turning angles. Background technique [0002] We believe that the cost of the robot during operation should be considered from the following aspects: the movement distance of the robot, we can know from common sense that in the case of basically the same movement environment, the movement energy consumption of the robot is positively correlated with the movement distance, that is, The farther the movement distance is, the greater the energy it consumes. For mobile robot systems, we hope to reduce the movement distance of the robot as much as possible under the premise of completing the robot movement goal. This can be done by using the classic algorithm of path planning. To; the turning angle of the robot, frequent or large-angle turning not only affects the work efficie...

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Problems solved by technology

[0002] We believe that the cost of the robot during operation should be considered from the following aspects: the movement distance of the robot, we can know from common sense that in the case of basically the same movement environment, the movement energy consumption of the robot is positively correlated with the movement distance, that is, The farther the movement distance is, the greater the energy it consumes. For mobile robot systems, we hope to reduce the movement distance of the robot as much as possible under the premise of completing the robot movement goal. This can be done by using the classic algorithm of path planning. To; the turning angle of the robot, frequent or large-angle turning not only affects the work efficiency of the robot when moving, but also consumes more energy under the condition of the same movement distance, although with the rechargeable battery technology such as lithium batteries in recent years With the rapid development and maturity of the future, the battery life of the robot is getting longer and longer, but the shortcomings such as long charging time still limit the cycle efficiency of the robot

[0003] For the practical application of mobile robots, the A* algorithm guarantees the shortest distance but cannot guarantee that the searched path is the least expensive for the robot. If a grid map is used, since the movement direction is limited to eight adjacent nodes, For some paths with specific angles, the classic A* algorithm may give the path with the shortest distance but more tortuous paths. In addition, compared with the Dijkstra algorithm, although the A* algorithm adds a heuristic function to make the path search more efficient A very large improvement, but for more complex or large-scale motion environments, the search efficiency of the A* algorithm in practical applications is still unsatisfactory; this is because as mentioned above, the heuristics in the classic A* algorithm The function is an estimate of the distance between the current node and the target node. If the estimate is completely accurate under ideal barrier-free conditions, the A* algorithm can be used to search completely according to the optimal path. Under this condition, the search The efficiency is the highest, but in the actual environment with obstacles or more complicated, the heuristic function often cannot accurately estimate the distance. If the estimated distance value of the heuristic function is larger than the actual distance value, the path given by the algorithm may not be the shortest path; Conversely, if the estimated distance value of the heuristic function is smaller than the actual distance value, the algorithm can give the best solution, but the smaller the heuristic function is, the more nodes the algorithm needs to search, and the lower the overall time efficiency

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