基于多目标蝗虫优化算法的移动机器人路径规划

在静态多障碍物环境下的移动机器人路径规划问题中,粒子群算法存在容易产生早熟收敛和局部寻优能力较差等缺点,导致机器人路径规划精度低。为此,提出一种多目标蝗虫优化算法(MOGOA)来解决这一问题。根据移动机器人路径规划要求将路径长度、平滑度和安全性作为路径优化的目标,建立相应的多目标优化问题的数学模型。在种群的搜索过程中,引入曲线自适应策略以提高算法收敛速度,并使用Pareto最优准则来解决三个目标之间的共存问题。实验结果表明:所提出的算法在解决上述问题中寻找到的路径更短,表现出更好的收敛性。该算法与多目标粒子群(MOPSO)算法相比路径长度减少了约2.01%,搜索到最小路径的迭代次数减少了约19.34%。

Path planning of mobile robot based on multi-objective grasshopper optimization algorithm

In the mobile robot path planning problem in static multi-obstacle environment, Particle Swarm Optimization (PSO) algorithm has the disadvantages of easy premature convergence and poor local optimization ability, resulting in low accuracy of robot path planning. To solve the problem, a Multi-Objective Grasshopper Optimization Algorithm (MOGOA) was proposed. The path length, smoothness and security were taken as path optimization targets according to the mobile robot path planning requirements, and the corresponding mathematical model of multi-objective optimization problem was established. In the process of population search, the curve adaptive strategy was introduced to speed up the convergence of the algorithm, and the Pareto optimal criterion was used to solve the coexistence problem of the above three targets. Experimental results show that the proposed algorithm finds shorter paths and shows better convergence while solving the above problems. Compared with the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, the proposed algorithm has the path length reduced by about 2.01 percentage, and the number of iterations reduced by about 19.34 percentage.