Abstract
Autonomous wheeled-legged robots have the potential to transform logistics systems, improving operational efficiency and adaptability in urban environments. Navigating urban environments, however, poses unique challenges for robots, necessitating innovative solutions for locomotion and navigation. These challenges include the need for adaptive locomotion across varied terrains and the ability to navigate efficiently around complex dynamic obstacles. This work introduces a fully integrated system comprising adaptive locomotion control, mobility-aware local navigation planning, and large-scale path planning within the city. Using model-free reinforcement learning (RL) techniques and privileged learning, we develop a versatile locomotion controller. This controller achieves efficient and robust locomotion over various rough terrains, facilitated by smooth transitions between walking and driving modes. It is tightly integrated with a learned navigation controller through a hierarchical RL framework, enabling effective navigation through challenging terrain and various obstacles at high speed. Our controllers are integrated into a large-scale urban navigation system and validated by autonomous, kilometer-scale navigation missions conducted in Zurich, Switzerland, and Seville, Spain. These missions demonstrate the system's robustness and adaptability, underscoring the importance of integrated control systems in achieving seamless navigation in complex environments. Our findings support the feasibility of wheeled-legged robots and hierarchical RL for autonomous navigation, with implications for last-mile delivery and beyond.
Abstract (translated)
自动驾驶轮式机器人具有潜力彻底改变物流系统，提高操作效率和适应城市环境的灵活性。然而，在导航城市环境中还存在独特的挑战，对机器人的运动和导航提出了创新解决方案。这些挑战包括在各种地形上进行自适应运动以及高效地围绕复杂动态障碍物进行导航。本文介绍了一种集成系统，包括自适应运动控制、面向移动性的局部路径规划和城市规模路径规划。我们使用基于模型无关强化学习（RL）技术和优先学习方法开发了一个多功能的运动控制器。该控制器在各种崎岖不平的地面上实现高效的稳健运动，得益于平滑的步行和驾驶模式之间的转换。它与通过分层的RL框架集成的学习导航控制器紧密集成，使机器人能够有效通过具有挑战性的地形和各种障碍物的高速导航。我们的控制器被集成到大型城市导航系统中，并通过瑞士苏黎世和西班牙塞维利亚等地进行的自主、公里级导航任务进行了验证。这些任务突显了系统的稳健性和适应性，进一步强调了集成控制系统在复杂环境中实现无缝导航的重要性。我们的研究结果支持轮式机器人的可行性和层次式RL在自主导航方面的应用，这对末端交付和更广阔的应用领域都有重要的意义。
URL
https://arxiv.org/abs/2405.01792
