Abstract
Reliable localization is crucial for autonomous robots to navigate efficiently and safely. Some navigation methods can plan paths with high localizability (which describes the capability of acquiring reliable localization). By following these paths, the robot can access the sensor streams that facilitate more accurate location estimation results by the localization algorithms. However, most of these methods require prior knowledge and struggle to adapt to unseen scenarios or dynamic changes. To overcome these limitations, we propose a novel approach for localizability-enhanced navigation via deep reinforcement learning in dynamic human environments. Our proposed planner automatically extracts geometric features from 2D laser data that are helpful for localization. The planner learns to assign different importance to the geometric features and encourages the robot to navigate through areas that are helpful for laser localization. To facilitate the learning of the planner, we suggest two techniques: (1) an augmented state representation that considers the dynamic changes and the confidence of the localization results, which provides more information and allows the robot to make better decisions, (2) a reward metric that is capable to offer both sparse and dense feedback on behaviors that affect localization accuracy. Our method exhibits significant improvements in lost rate and arrival rate when tested in previously unseen environments.
Abstract (translated)
可靠的定位对于自主机器人高效、安全地导航至关重要。一些导航方法可以规划具有高定位可靠性的路径(这描述了获取可靠定位的能力)。通过遵循这些路径,机器人可以访问有助于定位算法更准确地定位传感器流,从而更轻松地实现定位算法。然而,这些方法的大部分都需要先前的知识,并且很难适应未曾遇到的情况和动态变化。为了克服这些限制,我们提出了一种基于深度强化学习的新颖定位增强方法,通过动态人类环境。我们提议的规划者自动从2D激光数据中提取几何特征,这些特征对于定位有用。规划者学习将不同的几何特征赋予不同的重要性,并鼓励机器人穿越有助于激光定位的区域。为了促进规划者的学习,我们建议两种方法:(1)一个扩展的状态表示法,考虑动态变化和定位结果的可信度,提供了更多的信息,使机器人能够做出更好的决策,(2)一个奖励指标,能够提供稀疏和稠密的反馈,影响定位准确性的行为。我们在之前未曾测试过的环境中测试时,该方法表现出显著的减少丢失率和到达率的提高。
URL
https://arxiv.org/abs/2303.12354
