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首页> 外文期刊>Journal of computational and theoretical nanoscience >Robot Path Planning Based on Hybrid Improved D* with Particle Swarm Optimization Algorithms in Dynamic Environment
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Robot Path Planning Based on Hybrid Improved D* with Particle Swarm Optimization Algorithms in Dynamic Environment

机译：基于混合改进D *的机器人路径规划在动态环境中粒子群优化算法

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摘要

The paper presents a novel approach to apply a robot path planning in a dynamic environment based on particle swarm optimization and improved D* algorithm. The environment from the goal node is analyzed and the cost for each nodes are also computed by the improved D* algorithm. The improvement is represented by moving the robot from the start node in a dynamic environment by using the Ibest PSO algorithm to find and display the optimal path in an environment which includes dynamic obstacles. Unlike improved D* algorithm, the new approach has another improvement represented by the ability to manipulate the gate raise state (the position where a robot is unable to pass that node) in analysis stage. Some experimental results are simulation in different dynamic environments. The results indicate that the proposed approach enables the robot to get to the target without collision and to find the optimal path with the minimum total arc cost, minimum iteration, and minimum time occupy and when gate raise states are in the path.
机译：本文提出了一种基于粒子群优化和改进的D *算法在动态环境中应用机器人路径规划的新方法。分析来自目标节点的环境，并且还通过改进的D *算法计算每个节点的成本。通过使用IBEST PSO算法在动态环境中从起始节点移动机器人来查找和显示包括动态障碍物的环境的最佳路径来表示改进。与改进的D *算法不同，新方法具有通过操纵栅极提升状态的能力（机器人无法通过该节点的位置）表示的另一个改进。一些实验结果是在不同动态环境中的模拟。结果表明，该方法使机器人能够在没有碰撞的情况下进入目标，并找到具有最小总电弧成本，最小迭代和最短时间的最佳路径，并且当栅极升高状态在路径中时。

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