Robot path planning incorporating improved A*and ant colony optimization
According to the problems of the ant colony optimization(ACO)algorithm,which suffers from poor guidance and easily falls into local optimums and unsmooth paths,a path planning algorithm is proposed by combining A*and an improved ant colony algorithm.Firstly,in order to improve the initial convergence speed of the ant colony,the A*algorithm is used to differentiate the pheromone concentration on the map.Secondly,by incorporating the three influencing factors-path cost,transition angle,and obstacle concentration,into the traditional heuristic function,integrating the distance offset function into the transition probability,and dynamically adjusting the weight of the heuristic factor,the attractiveness of the target point to the ant is enhanced in many ways.Furthermore,a reward and punishment mechanism is used to update the pheromone levels and address the issue of the local optimality.Finally,the cubic B-spline is introduced to optimize the path corners and improve the path smoothness.The simulation results show that this algorithm has good global planning ability and robustness in complex environments.
path planningant colony algorithmreward and punishment mechanismglobal planning
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