In an environment where robots coexist with humans, mobile robots should be human-aware and comply with humans' behavioural norms so as to not disturb humans' personal space and activities. In this work, we propose an inverse reinforcement learning-based time-dependent A* planner for human-aware robot navigation with local vision. In this method, the planning process of time-dependent A* is regarded as a Markov decision process and the cost function of the time-dependent A* is learned using the inverse reinforcement learning via capturing humans' demonstration trajectories. With this method, a robot can plan a path that complies with humans' behaviour patterns and the robot's kinematics. When constructing feature vectors of the cost function, considering the local vision characteristics, we propose a visual coverage feature for enabling robots to learn from how humans move in a limited visual field. The effectiveness of the proposed method has been validated by experiments in real-world scenarios: using this approach robots can effectively mimic human motion patterns when avoiding pedestrians; furthermore, in a limited visual field, robots can learn to choose a path that enables them to have the larger visual coverage which shows a better navigation performance. GRAPHICAL ABSTRACT

中文翻译：

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基于逆强化学习的时间相关 A* 规划器，用于具有局部视觉的人类感知机器人导航

在机器人与人类共存的环境中，移动机器人应该具有人类意识，遵守人类的行为规范，不干扰人类的个人空间和活动。在这项工作中，我们提出了一种基于逆强化学习的时间相关 A* 规划器，用于具有局部视觉的人类感知机器人导航。在该方法中，时间相关A*的规划过程被视为马尔可夫决策过程，时间相关A*的成本函数是通过捕捉人类的示范轨迹使用逆强化学习来学习的。通过这种方法，机器人可以规划出符合人类行为模式和机器人运动学的路径。在构建代价函数的特征向量时，考虑局部视觉特征，我们提出了一种视觉覆盖功能，使机器人能够从人类在有限视野中的移动方式中学习。所提方法的有效性已通过实际场景中的实验验证：使用这种方法机器人可以在避开行人时有效地模仿人类的运动模式；此外，在有限的视野中，机器人可以学会选择一条路径，使它们具有更大的视觉覆盖范围，从而显示出更好的导航性能。图形概要 机器人可以学习选择一条路径，使它们具有更大的视觉覆盖范围，从而显示出更好的导航性能。图形概要 机器人可以学习选择一条路径，使它们具有更大的视觉覆盖范围，从而显示出更好的导航性能。图形概要