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摘要:
为更好地解决移动机器人路径规划问题, 改进蝙蝠算法的寻优性能, 拓展其应用领域, 提出了一种具有反向学习和正切随机探索机制的蝙蝠算法. 在全局搜索阶段的位置更新中引入动态扰动系数, 提高算法全局搜索能力; 在局部搜索阶段, 融入正切随机探索机制, 增强算法局部寻优的策略性, 避免算法陷入局部极值. 同时, 加入反向学习选择策略, 进一步平衡蝙蝠种群多样性和算法局部开采能力, 提高算法的收敛精度. 然后, 把改进算法与三次样条插值方法相结合去求解机器人全局路径规划问题, 定义了基于路径结点的编码方式, 构造了绕避障碍求解最短路径的方法和适应度函数. 最后, 在简单和复杂障碍环境下分别对单机器人和多机器人系统进行了路径规划对比实验. 实验结果表明, 改进后算法无论在最优解还是平均解方面都要优于其他几种对比算法, 对于求解机器人全局路径规划问题具有较好的可行性和有效性.
Abstract:In order to better solve the problem of mobile robot path planning, improve the optimization performance of bat algorithm and expand its application field, a bat algorithm with reverse learning and tangent random exploration mechanism is proposed. The dynamic perturbation coefficient is introduced into the position update in the global search stage to improve the global search ability of the algorithm. In the local search phase, the tangent random exploration mechanism is incorporated to enhance the strategy of local optimization and avoid the algorithm falling into local extremum. At the same time, the reverse learning selection strategy is added to further balance the diversity of bat population and local mining ability of the algorithm, and improve the convergence accuracy of the algorithm. Then, the improved algorithm and cubic spline interpolation method are combined to solve the robot global path planning problem, the coding method based on path node is defined, the method and the fitness function for solving the shortest path without collision with obstacles are constructed. Finally, the path planning experiments of single robot and multi-robot system are carried out in simple and complex obstacle environment. The experimental results show that the improved algorithm is superior to other comparison algorithms in terms of the optimal solution and the average solution, which is feasible and effective for solving the robot global path planning problem.
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图 1 简单环境下4种算法路径规划对比图
Fig. 1 Comparison of four algorithm path planning in a simple environment
图 2 简单环境下4种算法迭代曲线对比图
Fig. 2 Comparison of four algorithm iteration curves in a simple environment
图 3 复杂环境下4种算法路径规划对比图
Fig. 3 Comparison of four algorithm path planning in a complex environment
图 4 复杂环境下4种算法迭代曲线对比图
Fig. 4 Comparison of four algorithm iteration curves in a complex environment
图 11 复杂环境下UGBA算法所规划路径
Fig. 11 Planning path of UGBA algorithm in a complex environment
图 12 复杂环境下PTRBA算法所规划路径
Fig. 12 Planning path of PTRBA algorithm in a complex environment
表 1 简单环境下4种算法所求路径长度比较
Table 1 Comparison of path lengths obtained by four algorithms in a simple environment
算法 最优解 最差解 平均解 PSO 9.0853 9.8596 9.4564 BA 10.9109 13.4622 11.8820 UGBA 9.0346 11.8220 10.0908 PTRBA 8.9638 9.0151 8.9821 
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表 2 复杂环境下4种算法所求路径长度比较
Table 2 Comparison of path length obtained by four algorithms in a complex environment
算法 最优解 最差解 平均解 PSO 9.4117 11.0475 10.1180 BA 10.5201 15.3563 12.3124 UGBA 9.4695 13.9027 11.1290 PTRBA 8.8452 9.8183 9.1892
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表 3 简单环境下4种算法所求路径比较
Table 3 Comparison of path lengths obtained by four algorithms in a simple environment
算法 机器人1平均解 机器人2平均解 机器人3平均解 总路径最优解 总路径最差解 总路径平均解 PSO 10.1045 10.4892 11.3857 31.2132 32.6915 31.9794 BA 11.3361 12.4762 12.7646 34.6364 39.3087 36.5770 UGBA 10.9146 11.2058 11.8302 32.6335 35.1046 33.9507 PTRBA 8.9105 9.7747 10.8018 28.4527 29.8305 29.4870
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表 4 复杂环境下4种算法所求路径比较
Table 4 Comparison of path lengths obtained by four algorithms in a complex environment
算法 机器人1平均解 机器人2平均解 机器人3平均解 总路径最优解 总路径最差解 总路径平均解 PSO 10.1527 10.7141 11.3416 30.8667 33.3565 32.2085 BA 11.6525 11.6937 13.2311 35.3250 37.7116 36.5773 UGBA 9.7856 11.8275 12.0380 32.7774 35.0416 33.6512 PTRBA 9.2125 9.6017 10.5695 29.2493 29.7060 29.3837
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