A robot path planning method based on terminal distance index

Abstract

The invention discloses a robot path planning method based on terminal distance index, which includes the following steps: S1, using grid method to create a robot environment map; S2, using an ant colony algorithm to find the shortest path in the environment, and the ant colony algorithm includes the following steps: S21, Initialize the parameters of the ant colony algorithm; S22, place m ants on the starting point to start searching; S23, use the probability selection formula to select the next step to move the grid; S24, judge whether all ants have reached the target point, if so, proceed to step S25, If no, return to step S23; S25, update the terminal distance index according to all feasible paths obtained in the current generation; S26, judge whether the maximum number of iterations is reached, if yes, end; if not, increase the number of iterations by 1 and return to step S22; S3. Taking the shortest path obtained in step S2 as the optimal path for planning; the present invention not only improves the global optimal solution but also improves the convergence speed.

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...

AI Technical Summary

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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