Mobile robot path planning method based on extended view adaptive ant colony algorithm

Abstract

The invention discloses a mobile robot path planning method based on an extended view adaptive ant colony algorithm, and the method expands the current view of an ant from four directions, so that the extended view of the ant is wider, and the moving speed of the ant can be accelerated. Meanwhile, when the state transition probability is calculated, the two factors of the rotation angle and the safety distance are considered, and the state transition probability in the algorithm is jointly controlled by multiple factors. In a roulette selection strategy, a parameter adaptive pseudo-random transfer strategy is adopted, and the convergence speed of the algorithm can be improved in the early stage and the global search capability can be improved in the later stage by changing values of probability parameters in different stages in the iteration process. The self-adaptive pheromone volatilization coefficient can accelerate the global search range of the algorithm when the early-stage value is small, the concentration difference on the path can be increased as the searched path becomes shorter and shorter, the pheromone volatilization coefficient becomes larger, the guide function of the ant colony is enhanced, the search speed of the ant colony is increased, and the algorithm is enabled to converge quickly.

Description

technical field [0001] The invention belongs to the technical field of robots, and in particular relates to a path planning method for a mobile robot. Background technique [0002] Mobile robots are an important driving force for the realization of intelligent manufacturing, and have become the focus of attention in both academia and business circles. Among them, path planning is the core component of the mobile robot, and its goal is to find the optimal or near-optimal path from the starting point to the target point without collision. A fast, efficient and safe path planning algorithm is the key to finding the optimal path, and it is also the theoretical basis for the successful application of mobile robots. [0003] The literature "Miao C, Chen G, Yan C. Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm [J]. Computers & Industrial Engineering, 2021: 107230." provides an improved adaptive ant colony algorithm to overcome the existing...

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Problems solved by technology

[0003] The literature "Miao C, Chen G, Yan C. Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm [J]. Computers & Industrial Engineering, 2021: 107230." provides an improved adaptive ant colony algorithm to overcome the existing indoor mobile robot Some shortcomings of the mobile robot path planning algorithm, such as long path planning time, slow convergence of the optimal path, and easy to fall into local optimum

However, because the search range of this method is limited to eight candidate grids around the current position, and the late change of adaptive pheromone is not obvious, there are problems of ant short-sightedness and weak adjustment of pheromone volatilization coefficient in the later stage, which leads to the decrease of algorithm search efficiency and convergence slow

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