Improved RRT<*> obstacle avoidance motion planning method based on multi-degree-of-freedom mechanical arm

Abstract

The invention discloses an improved RRT<*> obstacle avoidance motion planning method based on a multi-degree-of-freedom mechanical arm, and belongs to the field of mechanical arm motion planning. A six-degree-of-freedom mechanical arm model with seven connecting rods and six rotary joints is built; parameters in a to-be-searched space are determined; if the distance is shorter than the distance of a path with lowest cost, the distances between a near node in a set to an initial point and the distance between the node to a random point are temporarily determined as the minimum path; a newly generated sigma is subjected to collision detection, and the node and the path are added if the newly generated path does not collide an obstacle interval; the steps are repeated until the optimal path is found; and the generated path is added into a path planning device. Compared with the prior art, the method has the following advantages that the random search characteristic is changed in a mode of adding normal distribution, the algorithm convergence rate can be increased through the heuristic search, the RRT<*> algorithm has the evolutionary optimization path, and a large number of calculations is not needed; and after Gaussian distribution of an inspiration point near a target point is added, the convergence rate is increased, and the search time is shortened.

Description

technical field [0001] The invention relates to a motion planning method in the obstacle avoidance link of a mechanical arm, specifically an improved fast random search method applied to the global space, belonging to the field of mechanical arm motion planning Background technique [0002] The problem of robot motion planning was proposed as early as the 1960s. The early motion planning only involved path planning. The robot was regarded as a point, and motion planning was regarded as finding a path in the pose space from the initial pose point to the For the continuous path of the target pose point, the path planning goes through a search process. According to the modeling and search methods of the world environment, it is mainly divided into planning of free space geometric structure, intelligent method planning and planning method based on random sampling. However, the early path planning cannot adapt to the current environment with high degrees of freedom of the manipul...

AI Technical Summary

Problems solved by technology

However, the early path planning cannot adapt to the high degree of freedom of the current manipulator and the complex obstacle space environment. The early planning method can only increase a large amount of calculation and reduce the efficiency of the search.

[0003] The obstacle avoidance planning goal of the manipulator is to plan an optimal path that satisfies various indicators. For this problem, the C space method, A* search method, artificial test field method, and genetic algorithm that have been adopted have certain limitations. The calculation time of the C space method is longer than the response time of the mechanical arm, which limits its application in obstacle avoidance. The ant colony algorithm is an essentially parallel algorithm. It starts multi-line independent solution search at multiple points in the problem space at the same time. However, the search time of the modified algorithm is long, and it is easy to stagnate

[0004] Considering the problems of inefficiency in the current search algorithm, the present invention aims to propose an improved RRT* (rapidly exploring random tree) algorithm for multi-degree-of-freedom mechanical arm obstacle avoidance motion planning method, which solves the problem of time spent on searching. The time is long, the number of iterations is large, it is easy to be limited to local infinitesimal problems, and the convergence rate is low, so that the manipulator can quickly and effectively avoid obstacles

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