Robot path planning algorithm based on optimized ant colony algorithm

Abstract

The invention relates to a robot path planning algorithm based on an optimized ant colony algorithm. The algorithm comprises the following steps: S1, initializing an inspiring factor to construct thecomprehensive inspiring information for the movement of the robot according to a target gravitation generated by an artificial potential field and an ant colony algorithm; S2, updating pheromone in the art colony algorithm by adopting a wolves distribution rule; and S3, optimizing a planned path by utilizing a path optimization algorithm. By adopting the robot path planning algorithm, an optimum path can be rapidly and efficiently planned.

Description

technical field [0001] The invention belongs to the field of mobile robot navigation, in particular to a robot path planning algorithm based on an optimal mixed ant colony algorithm. Background technique [0002] Path planning is one of the key technologies to realize the control of mobile robots. Its purpose is to find an optimal or suboptimal safe collision-free path from the starting position to the target position under certain environmental conditions and performance index requirements. For robot path planning, domestic and foreign scholars have proposed many planning methods, including artificial potential field method, neural network adaptive planning method, genetic algorithm, ant colony algorithm, particle swarm algorithm, etc. In recent years, more and more scholars pay more attention to the combination of multiple intelligent algorithms to improve the performance of the algorithm when studying the path planning problem. For example, ImenChaari* combined the gene...

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Problems solved by technology

For example, ImenChaari* combined the genetic algorithm with the ant colony algorithm. In the early stage, the genetic algorithm is used to generate the initial pheromone distribution, and the ant colony algorithm is used to find the optimal solution in the later stage, which can effectively combine the advantages of the two algorithms and improve the search efficiency of the ant colony. , but may fall into local optimum; X Wang et al. proposed a new path planning method based on particle swarm optimization (Particle Swarm Optimization, PSO) and ant colony optimization (Ant colony optimization, ACO) algorithm. Modular method, generate the path from the starting point to the goal point, and then distribute the pheromone based on the previously generated path, finally, use the improved optimized ant colony to find the best path, this method can shorten the search time, but requires less environment High, poor adaptability; T Zhu, G Dong et al. proposed an algorithm combining the ant colony algorithm and the artificial potential field method. This algorithm uses the potential field method to initialize the overall path, optimizes the path sorting of each generation of ants, and Update the pheromone, meanwhile, with the help of the pheromone of the elite ants, use the crossover and mutation operation of the memetic algorithm on each generation path, this algorithm improves the convergence speed and stability, but the potential field method itself is easy to fall into the local Deadlock, so the algorithm is prone to fall into local optimum when initially looking for a path

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