"基于强化学习的粒子群优化,利用二维激光雷达点云进行自主地面飞行器轨迹规划"

IF 4.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Ambuj, Harsh Nagar, Ayan Paul, Rajendra Machavaram, Peeyush Soni
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引用次数: 0

摘要

自主移动机器人的出现促进了对高效轨迹规划方法的研究,尤其是在存在各种障碍物的动态环境中。本研究的重点是利用新颖的强化学习粒子群优化(RLPSO)算法优化自主地面车辆(AGV)的轨迹规划。通过在机器人操作系统(ROS)平台中使用 Hector-SLAM 算法,引入了实时移动机器人定位和地图生成功能,从而创建了一个二元占位网格。本研究深入探讨了 RLPSO 算法的性能,并在四种不同的物理环境中将其与五种成熟的粒子群优化(PSO)变体进行对比。实验设计旨在模拟真实世界的场景,包括静态和动态障碍物带来的一系列挑战。配备了激光雷达传感器的 AGV 在不同几何形状的障碍物构成的各种环境中航行。RLPSO 算法可根据反馈动态调整策略,从而在有效避开障碍物的同时,实现适应性轨迹规划。大量实验得出的数值结果凸显了该算法的功效。利用二维激光雷达测绘点数据,在 MATLAB 二维虚拟环境中对导航模型进行了验证。在使用 AGV 进行物理实验时,RLPSO 继续表现出卓越的性能,展示了其在实际自主导航应用中的潜力。在不同的场景中,与性能最佳的 PSO 变体相比,RLPSO 的路径距离和穿越时间平均缩短了 10-15%。RLPSO 的自适应特性,以及来自环境的反馈信息,使其成为在动态环境中进行自主导航的一种有前途的解决方案,并对在现实世界中的实际应用产生了影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
“Reinforcement learning particle swarm optimization based trajectory planning of autonomous ground vehicle using 2D LiDAR point cloud”

The advent of autonomous mobile robots has spurred research into efficient trajectory planning methods, particularly in dynamic environments with varied obstacles. This study focuses on optimizing trajectory planning for an Autonomous Ground Vehicle (AGV) using a novel Reinforcement Learning Particle Swarm Optimization (RLPSO) algorithm. Real-time mobile robot localization and map generation are introduced through the utilization of the Hector-SLAM algorithm within the Robot Operating System (ROS) platform, resulting in the creation of a binary occupancy grid. The present research thoroughly investigates the performance of the RLPSO algorithm, juxtaposed against five established Particle Swarm Optimization (PSO) variants, within the context of four distinct physical environments. The experimental design is tailored to emulate real-world scenarios, encompassing a spectrum of challenges posed by static and dynamic obstacles. The AGV, equipped with LiDAR sensors, navigates through diverse environments characterized by obstacles of different geometries. The RLPSO algorithm dynamically adapts its strategies based on feedback, enabling adaptable trajectory planning while effectively avoiding obstacles. Numerical results obtained from extensive experimentation highlight the algorithm's efficacy. The navigational model's validation is achieved within a MATLAB 2D virtual environment, employing 2D Lidar mapping point data. Transitioning to physical experiments with an AGV, RLPSO continues to demonstrate superior performance, showcasing its potential for real-world applications in autonomous navigation. On average, RLPSO achieves a 10–15 % reduction in path distances and traversal time compared to the following best-performing PSO variant across diverse scenarios. The adaptive nature of RLPSO, informed by feedback from the environment, distinguishes it as a promising solution for autonomous navigation in dynamic settings, with implications for practical implementation in real-world scenarios.

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来源期刊
Robotics and Autonomous Systems
Robotics and Autonomous Systems 工程技术-机器人学
CiteScore
9.00
自引率
7.00%
发文量
164
审稿时长
4.5 months
期刊介绍: Robotics and Autonomous Systems will carry articles describing fundamental developments in the field of robotics, with special emphasis on autonomous systems. An important goal of this journal is to extend the state of the art in both symbolic and sensory based robot control and learning in the context of autonomous systems. Robotics and Autonomous Systems will carry articles on the theoretical, computational and experimental aspects of autonomous systems, or modules of such systems.
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