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Trajectory planning of an autonomous mobile robot

机译:自主移动机器人的轨迹规划

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The latest moves in trajectory planning for autonomous mobile robots are directed towards a popular investigation work. This paper introduces modified particle swarm optimisation technique called as adaptive particle swarm optimisation (APSO) and particle swarm optimisation (PSO) for trajectory length optimisation. For estimating the trajectory length of the robot, nine numbers of obstacles is selected between start and goal point in a static environment. Lastly a comparison is established between these two approaches, to identify the approach that affords shortest trajectory length in a least computation time and shortest possible travel time. Simulation result shows that APSO contributes towards curtail trajectory length at a lesser computational and travel time as compared to PSO.
机译:自主移动机器人的轨迹规划的最新动向是针对流行的调查工作的。本文介绍了改进的粒子群优化技术,称为自适应粒子群优化(APSO)和粒子群优化(PSO),用于轨迹长度的优化。为了估计机器人的轨迹长度,在静态环境中在起点和目标点之间选择了九个障碍物。最后,在这两种方法之间建立比较,以识别在最少的计算时间和最短的可能行驶时间中提供最短轨迹长度的方法。仿真结果表明,与PSO相比,APSO在缩短的计算和行进时间上有助于缩短轨迹长度。

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