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

机译:复杂未知环境中基于粒子群优化(PSO)的多机器人路径规划参数分析

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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).
机译:通过粒子群优化(PSO),在复杂环境中进行路径规划是机器人多路径规划的一种新颖方法。机器人的自动化和检测能力是主要挑战,要克服这些问题,需要建立优化路径。机器人路径规划是处理带有地图的给定机器人(代理)的无碰撞路径计算的主要问题之一,这有助于机器人进行操作。当已知环境并且估计了目标位置时,则仅可能建立路径。我们在论文中介绍的工作完全集中在路径规划问题上。我们仅考虑了一种情况,据此,机器人(代理)会跟踪协调的目标并在目标位置未知时通过避障技术来到达未知环境。我们用来评估这些算法的重要参数是:(a)我们认为访问的节点数(移动)。 (b)探索的区域被视为(覆盖)。 (c)行驶距离视为(能量),经过时间视为(时间)。

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