arXiv 2409.149722024-09-23cs.ROcs.AI

基于深度强化学习的仓库环境中机器人移动避障

Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments

AMA University（AMA大学）；New York University（纽约大学）；University of California, Berkeley（加州大学伯克利分校）；Carnegie Mellon University（卡内基梅隆大学）；Northeastern University（东北大学）；Cornell University（康奈尔大学）；University of California, Irvine（加州大学欧文分校）；Northern Arizona University（北亚利桑那大学）

Keqin Li, Jiajing Chen, Denzhi Yu, Tao Dajun, Xinyu Qiu, Lian Jieting, Sun Baiwei, Zhang Shengyuan, Zhenyu Wan, Ran Ji, Bo Hong, Fanghao Ni

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

针对仓库环境中传统移动机器人避障策略反馈不佳的问题，提出一种基于深度强化学习的避障算法，通过改进价值函数网络并设计基于行人空间行为的奖励函数，实现了高效舒适的避障，仿真实验验证了其有效性。

摘要：

目前，在大多数仓库环境中，货物堆积情况复杂，管理人员在控制货物的同时还需与仓库移动机器人的轨迹进行交互。传统移动机器人无法很好地针对货物和行人反馈正确的避障策略。为了控制移动机器人在仓库环境中高效且友好地完成避障任务，本文提出了一种基于深度强化学习的仓库环境移动机器人避障算法。首先，针对深度强化学习算法中价值函数网络学习能力不足的问题，基于行人交互对价值函数网络进行了改进，通过行人角度网格提取行人间的交互信息，并通过注意力机制提取个体行人的时间特征，从而学习获取当前状态与历史轨迹状态的相对重要性及其对机器人避障策略的联合影响，为后续多层感知机的学习提供了机会。其次，基于行人的空间行为设计了强化学习的奖励函数，对角度变化过大的状态对机器人进行惩罚，以达到舒适避障的要求。最后，通过仿真实验验证了基于深度强化学习的移动机器人避障算法在仓库复杂环境中的可行性与有效性。

英文摘要：

At present, in most warehouse environments, the accumulation of goods is complex, and the management personnel in the control of goods at the same time with the warehouse mobile robot trajectory interaction, the traditional mobile robot can not be very good on the goods and pedestrians to feed back the correct obstacle avoidance strategy, in order to control the mobile robot in the warehouse environment efficiently and friendly to complete the obstacle avoidance task, this paper proposes a deep reinforcement learning based on the warehouse environment, the mobile robot obstacle avoidance Algorithm. Firstly, for the insufficient learning ability of the value function network in the deep reinforcement learning algorithm, the value function network is improved based on the pedestrian interaction, the interaction information between pedestrians is extracted through the pedestrian angle grid, and the temporal features of individual pedestrians are extracted through the attention mechanism, so that we can learn to obtain the relative importance of the current state and the historical trajectory state as well as the joint impact on the robot's obstacle avoidance strategy, which provides an opportunity for the learning of multi-layer perceptual machines afterwards. Secondly, the reward function of reinforcement learning is designed based on the spatial behaviour of pedestrians, and the robot is punished for the state where the angle changes too much, so as to achieve the requirement of comfortable obstacle avoidance; Finally, the feasibility and effectiveness of the deep reinforcement learning-based mobile robot obstacle avoidance algorithm in the warehouse environment in the complex environment of the warehouse are verified through simulation experiments.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。