Local path planning algorithm and device

Abstract

The invention provides a local path planning algorithm and device, which are used for path planning of mobile equipment that is provided with a sensor. The algorithm comprises the following steps: S1,judging whether the mobile equipment reaches a destination or not, and if so, ending a process, or if not, executing step S2; S2, acquiring a candidate gap set according to sensor data; S3, selectinga nearest gap from the candidate gap set as a target gap; S4, determining the moving direction of the mobile equipment according to the target gap; S5, controlling the mobile equipment to move alongthe target direction by adopting an improved dynamic window method; and S6, repeating the steps S1 to S5 until the mobile equipment reaches the destination or no feasible gap exists. The path of the mobile equipment is planned based on gaps and an improved dynamic window method so as to reduce the condition of local extreme value and improve obstacle avoidance performance.

Description

technical field [0001] The invention relates to the technical field of path planning for mobile devices, 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 takes into account the actual dynamic constraints and kinematic constraints of the robot, and samples the velocity window in the velocity space. Multiple sets of velocities within the model, and predict the trajectories formed within a certain period of time, and then use the evaluation function to evaluate these trajectories, and finally select the velocity corresponding to the highest evaluated trajectory 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 velocity corresponding to a traj...

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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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