强化学习
计算机科学
机器人
避障
人工智能
一般化
控制器(灌溉)
容器(类型理论)
移动机器人导航
移动机器人
机器人控制
工程类
机械工程
数学分析
数学
农学
生物
作者
Yan Wang,Jian Sun,Zhuo Li,Gang Wang
标识
DOI:10.23919/ccc58697.2023.10240139
摘要
Multi-robot navigation in complex scenarios such as container terminalss is a challenging problem where each robot can only perceive a subset of the states and intentions of other robots. In this paper, we propose a multi-robot navigation framework based on option-based hierarchical deep reinforcement learning (DRL) for rapid and safe navigation. The framework comprises two control models: a low-level model that generates actions using sub-policies, and a high-level model that learns a stable and reliable behavior selection policy automatically. Additionally, we design a PID-based target drive controller and an emergency braking controller to enhance obstacle avoidance efficiency and generalization ability in hazardous scenarios. We evaluate the proposed method against existing DRL-based navigation methods in various simulated scenarios with thorough performance evaluations. Our results indicate that the proposed framework significantly improves multi-robot navigation performance in complex scenarios and exhibits excellent generalization ability to new scenarios.
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