基于改进乌燕鸥算法的移动音乐机器人路径规划

Path Planning of Mobile Music Robot Based on Enhanced Sooty Tern Algorithm

移动音乐机器人最短路径规划是一个带有复杂约束和高维的非线性规划问题,标准的乌燕鸥算法求解此问题时,存在局部停滞不足导致其获得的最短路径精度较低。为此,设计一种改进的乌燕鸥算法。引入一种新颖的改进Logistic混沌映射种群初始化策略,增强算法迭代初期的寻优能力;采用非线性自适应Lévy飞行攻击位置更新方式,协调算法的全局搜索和局部开发能力之间的动态平衡;采用Lévy飞行和黄金正弦混合扰动策略,提高算法的全局寻优能力。仿真结果表明:与其他著名算法相比,该算法路径规划结果更优,寻优能力和搜索效率更强,可高效地处理复杂的移动音乐机器人路径规划问题,具有较强的适用性。

The shortest path planning of mobile music robot is a nonlinear programming problem with complex constraints and high dimensions.Solving the problem by standard black tern algorithm will generate insufficient local stagnation that leads to poor accuracy of the shortest path.Therefore,an enhanced sooty tern algorithm is proposed.A novel improved Logistic chaotic mapping population initialization strategy is introduced to enhance the optimization ability of the algorithm at the initial iteration stage.The nonlinear adaptive Levy flight attack position update method is adopted to coordinate the dynamic balance between the global search and local development capabilities of the algorithm.The hybrid Lévy flight and golden sine mechanism is used to improve the global convergence ability of the algorithm.The simulation results show that in comparison with other algorithms,the proposed algorithm has better path planning results,better search ability and search efficiency,and can efficiently deal with mobile robot path planning problems under complex environment with strong applicability.