基于改进蚁群算法的移动机器人路径规划OA

Path planning for mobile robots is considered one of the fundamental research areas in robotics.It involves determining an optimal,collision-free path from a start point to a target based on the assigned task and environmental perception.To address the limitations of the standard ant colony optimization(ACO)algorithm,including slow conver-gence,redundant paths,and susceptibility to local optima,this study proposes an improved ACO algorithm.Firstly,goal-oriented Euclidean and Chebyshev distances are fused to enhance early-stage search efficiency,and a normal distribution(Gaussian distribution)is introduced into the heuristic function to improve path search precision.Secondly,a reward-penalty strategy is incorporated into the pheromone update mechanism to reinforce the influence of high-quality paths and accelerate convergence.Thirdly,an adaptive pheromone evaporation factor is applied to dynamically adjust the search behavior,thereby enhancing global exploration and reducing the risk of becoming trapped in local op-tima.Finally,a pruning strategy is employed to reduce the number of turns and shorten the overall path length.Com-parative simulation experiments with other algorithms in both two-dimensional and three-dimensional environments demonstrate that the proposed algorithm not only yields shorter paths but also exhibits higher search efficiency in terms of running time.