基于文化萤火虫算法的足球机器人动态路径规划

提出一种基于文化算法框架的萤火虫优化算法,结合动态避障和滑模控制求解足球机器人动态路径规划问题,并利用数学定理证明算法的收敛性.根据足球机器人在比赛中承担任务的分工不同,分别对进攻和防守两种角色进行分析讨论,进攻时结合动态避碰的方法平滑和修正规划的路径;防守时通过滑模控制跟踪足球或对手机器人的轨迹,利用CFA算法进行整定优化滑模控制的参数,计算出机器人的运行速度和角速度.以足球机器人比赛实例进行测试,实验结果证实所提出算法无论对无碰撞危险还是有多个障碍物机器人碰撞危险等不同情况,都具备有效性和高效性.考察路径采样点数、种群数量和进化迭代次数等参数变化对收敛性能的影响,并将所提出算法与PSO和ACO等进化计算算法进行性能比较,验证了算法更容易搜索到全局最优解,有更好的收敛性能.

Cultural firefly algorithm for dynamic path planning of soccer robot

In this paper, combined with dynamic collision preventation and sliding mode control, a firefly algorithm based on the framework of a cultural algorithm is proposed to solve the dynamic path planning for the soccer robot problem. The convergence of the algorithm is proved by the mathematical theorem. According to the different tasks of the robot soccer in the competition, two roles of attack and defense are analyzed and discussed respectively. When attacking, combined with the method of dynamic collision avoidance, the path is smoothed and corrected. When defending, the trajectory of the ball or opponent robot is tracked based on sliding mode control, and the parameters of sliding mode control are optimized using the CFA algorithm. Thus the speed and angular velocity of the robot are computed. The robot soccer games are used to test the new algorithm. The experimental results confirms that the CFA has effectiveness and efficiency, regardless of without risk of collision, or with risk of collision when encountering different obstacle robots. The effects of different parameters on the convergence performance are tested, such as path sampling points, population scale and evolutionary iteration number. Compared with the PSO, ACO and other famous evolutionary algorithms, it is verified that the proposed algorithm is easier to search the global optimal solution and has better convergence performance.