Research on dynamic window algorithm of mine mobile robot based on membrane computing and particle swarm optimizatio
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摘要: 针对煤矿移动机器人采用传统动态窗口算法在复杂环境中规划路径时存在路径规划不合理、规划速度慢和实时性较差等问题,提出了一种基于膜计算和粒子群的煤矿移动机器人动态窗口算法。利用粒子群中的随机性和膜计算的分布式并行计算能力对传统动态窗口算法进行优化,将动态窗口算法中的煤矿移动机器人速度限制空间转换为坐标空间,将煤矿移动机器人的速度坐标看作粒子位置,将速度采样方式从均匀等分采样变为随机采样,并将采样粒子均匀分配到各基本膜中,利用膜间交流和膜内粒子更新机制对粒子进行评价和更新,不断迭代输出最优速度,煤矿移动机器人根据连续时间段间隔内输出的最优速度进行路径规划。仿真结果表明,该算法通过基于膜计算和粒子群算法对煤矿移动机器人的速度限制区域进行优化,提高了速度采样的随机性和规划路径的合理性;与传统动态窗口算法相比,该算法在降低规划步数和每步评价次数的同时,可缩短7%~10%的规划路径长度和9%~32%的规划时间,并可适应含U型障碍物的特殊环境。Abstract: In view of problems such as unreasonable path planning, slow planning speed and poor real -time performance when mine mobile robots use traditional dynamic window algorithm to plan path in complex environment, a dynamic window algorithm of mine mobile robot based on membrane computing and particle swarm optimization was proposed. The traditional dynamic window algorithm is optimized by using randomness of particle swarm optimization and distributed parallel computing ability of membrane computing. In the dynamic window algorithm, the velocity limit space of mine mobile robot is transformed into coordinate space, and the velocity coordinate of the mine mobile robot is regarded as particle position. The speed sampling mode is changed from uniform equal sampling to random sampling, the sample particles are evenly distributed to each basic membrane. The exchange between membranes and the renewal mechanism of particles in membrane are used to evaluate the renewal of particles. The optimal speed is output continuously. The path planning of the mine mobile robot is based on the optimal output speed in continuous time interval. The simulation results show that the algorithm optimizes the speed limit region of mine mobile robot by membrane computing and particle swarm optimization algorithm, and improves the randomness of speed sampling and the rationality of planning path. Compared with the traditional dynamic window algorithm, the proposed algorithm can not only reduce the number of planning steps and the evaluation times of each step, but also shorten the planning path length by 7% -10% and the planning time by 9% -32%, and can adapt to the special environment with U -shaped obstacles.
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