Multi Robot Path Planning Parameter Analysis Based on Particle Swarm Optimization (PSO) in an Intricate Unknown Environments

Shubham Shukla, Nk Shukla, V. Sachan
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引用次数: 4

Abstract

Through Particle Swarm Optimization (PSO) path planning in an intricate environment turns out to be a novel approach for robot’s multi path planning. Automation and detection capabilities of robots are the major challenges, to overcome these problems optimized path needs to be established. Robot path planning is one of the main problem that deals with the computation of collision free path for the given robot (agent) with the map, which helps it to operate. When the environment is known and the target location is estimated then only the path establishment is possible. The work we have presented on our paper totally focusses on the path planning problem. We have taken only one case into consideration, according to it the robot (agent) tracks the coordinated targets and reach towards the unknown environment through obstacle avoidance technique when the location of the target is unknown. Important parameters that we have taken to asses these algorithms are: (a) Number of visited node we consider as (Move). (b) Area explored considered as (Coverage). (c) Distance travelled considered as (Energy) and time elapsed as (Time).
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复杂未知环境下基于粒子群算法的多机器人路径规划参数分析
基于粒子群算法的复杂环境下的路径规划是机器人多路径规划的一种新方法。机器人的自动化和检测能力是主要的挑战,为了克服这些问题,需要建立优化路径。机器人路径规划是机器人的主要问题之一,它处理给定机器人(智能体)的无碰撞路径的计算,从而帮助机器人(智能体)在地图上运行。当环境是已知的,目标位置是估计的,那么只有路径建立是可能的。我们在论文中提出的工作完全集中在路径规划问题上。我们只考虑了一种情况,根据这种情况,机器人(agent)在目标位置未知的情况下,跟踪协调的目标并通过避障技术向未知环境移动。我们用来评估这些算法的重要参数是:(a)我们认为是(移动)的访问节点的数量。(b)被视为(覆盖范围)的探索地区。(c)行进距离(能量),经过时间(时间)。
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