A local path planning algorithm and device

Abstract

A local path planning algorithm and device, used for path planning of a mobile device, the mobile device is loaded with a sensor, the algorithm includes: S1, judging whether the mobile device has reached the destination, if it has reached the destination, end, otherwise, execute step S2; S2, according to Sensor data, get the candidate gap set; S3, select a nearest gap from the candidate gap set as the target gap; S4, determine the moving direction of the mobile device according to the target gap; S5, use the improved dynamic window method to control the mobile device to move along the target direction ; S6, repeating steps S1-S5 until the mobile device reaches the destination or there is no feasible gap. Gap-based and improved dynamic window methods are used to plan mobile device paths to reduce local extrema and improve obstacle avoidance performance.

Description

technical field [0001] The present invention relates to the technical field of mobile device path planning, and in particular, to a local path planning algorithm and device. Background technique [0002] The path planning algorithm is mainly divided into global path planning and local path planning. The dynamic window method is a typical local path planning algorithm. The dynamic window method considers the actual dynamic constraints and kinematic constraints of the robot, and samples the speed window in the speed space. It predicts the trajectories formed within a certain period of time, and then uses the evaluation function to evaluate these trajectories, and finally selects the velocity corresponding to the trajectory with the highest evaluation as the velocity input at the current moment. The dynamic window method mainly uses the evaluation function to evaluate each trajectory, and then selects the speed corresponding to a trajectory with the highest evaluation as the sp...

AI Technical Summary

Problems solved by technology

The dynamic window method mainly uses the evaluation function to evaluate each trajectory, and then selects the velocity corresponding to a trajectory with the highest evaluation as the velocity input. Therefore, the selection of the final velocity has a direct relationship with the The smaller the direction angle, the higher the evaluation, which makes the dynamic window method more inclined to choose the trajectory moving towards the target direction. However, when there are obstacles in front of the robot, the desired trajectory not only needs to move towards the target, but also needs to be away from the obstacle. , the emergence of this contradiction makes the robot fall into a local minimum point, which is manifested as a continuous cycle or even stagnation near this position; in addition, when the dynamic window method evaluates the trajectory, it does not directly exclude the trajectory that crosses obstacles. The corresponding speed is given a low evaluation, which may lead to the situation that the evaluation of other evaluation function items is still high, and then it is concluded that the optimal trajectory will be generated by this group of speeds, resulting in a robot with a high probability of being similar to obstacles. Collide

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