Robot path planning based on PSO and D∗ algorithmsin dynamic environment

A. Sadiq, Ali Hadi Hasan
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引用次数: 14

Abstract

This paper proposes two new approaches of robot path planning in dynamic environments based on D∗ algorithm and particle swarm optimization. Generally speaking, the grid method is used to decompose two-dimensional space to build class node which contains the information of the space environment. D∗ algorithm analyze the environment from the goal node and computes the cost for each node to the start node. In the first approach, Lbest PSO algorithm is used to move the robot from the start node through dynamic environment which contains dynamic obstacle moving in free space by finding and displaying the optimal path. In the second approach a method is developed to manipulate the gate raise state where the robot cannot pass this node unlike D∗ algorithm. Some experimental results are simulation in different dynamic environments, show that in second approach the robot reaches its target without colliding obstacles and finds the optimal path with minimum iterations, minimum total arc cost and minimum time occupy.
动态环境下基于粒子群算法和D *算法的机器人路径规划
提出了基于D *算法和粒子群算法的动态环境下机器人路径规划的两种新方法。一般采用网格法对二维空间进行分解,建立包含空间环境信息的类节点。D *算法分析从目标节点开始的环境,并计算每个节点到起始节点的代价。第一种方法采用Lbest粒子群算法,通过寻找并显示最优路径,使机器人从起始节点移动到包含动态障碍物的动态环境中。在第二种方法中,开发了一种方法来操纵门抬高状态,使机器人不能通过该节点,这与D *算法不同。对不同动态环境下的实验结果进行了仿真,结果表明,在二次逼近中,机器人在没有碰撞障碍物的情况下到达了目标,并以最小的迭代次数、最小的总弧度和最小的占用时间找到了最优路径。
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