Robot path planning method based on final distance index

Abstract

The invention discloses a robot path planning method based on a final distance index. The robot path planning method comprises the following steps of: S1, creating a robot environment map by adoptinga grid method; S2, searching the shortest path of the environment by adopting an ant colony algorithm. The ant colony algorithm comprises the following steps that: S21, parameters of the ant colony algorithm are initialized; S22, m ants are placed at a starting point to start searching; S23, a probability selection formula is used to select a next moving grid; S24, whether all the ants reach a target point or not is judged, if so, the step S25 is executed, and if not, the algorithm returns to the step S23; S25, a final distance index is updated according to all feasible paths obtained in the present generation; S26, whether the maximum number of iterations is reached or not is judged , and if so, the algorithm is terminated; if not, 1 is added to the number of iterations, and the algorithmreturns to the step S22. The robot path planning method further comprises the step S3, namely, taking the shortest path obtained in the step S2 as a planned optimal path; The method not only improvesthe global optimal solution, but also improves the convergence rate.

Description

technical field [0001] The invention relates to the technical field of robot path planning, in particular to a robot path planning method based on terminal distance index. Background technique [0002] Path planning technology is an important part of the research field of mobile robots. The main purpose is to find a path from the starting position according to a given goal (such as the shortest path, the least position inflection point, the shortest time, etc.) in an environment with obstacles. Optimal or sub-optimal safe collision-free paths between nodes to target location nodes. [0003] The development of path planning technology marks the level of robot intelligence to a certain extent, and the pros and cons of path planning methods directly affect the path planning effect. [0004] At present, many experts and scholars at home and abroad are working on the research of path planning algorithms. The commonly used optimization algorithms mainly include artificial potenti...

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Problems solved by technology

[0005] Among them, the ant colony algorithm is a population-based probability selection algorithm. Compared with other heuristic algorithms, the algorithm has strong robustness and better solution search ability, and is easy to compare with other heuristic algorithms. Algorithms are combined to improve the performance of the algorithm, so the ant colony algorithm has been widely used in path planning and other fields, but the ant colony algorithm also has some shortcomings, such as slow convergence speed, easy to fall into local optimal solution, etc.

[0006] In response to these shortcomings, many scholars at home and abroad have tried to improve the traditional ant colony algorithm. Although a large number of simulation results have shown that some improvement strategies to the basic ant colony algorithm are feasible in some aspects, there are still some defects. It needs to be compensated. For example, the selection strategy of ants is affected by the concentration of pheromone, and the concentration is determined by the path. Therefore, there are redundant paths, and the pheromone concentration produced by them will be disturbed by redundant paths, which will affect the correct choice of ants. Rate

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