Navigation strategies that intentionally incorporate contact with humans (i.e. "contact-based" social navigation) in crowded environments are largely unexplored even though collision-free social navigation is a well studied problem. Traditional social navigation frameworks require the robot to stop suddenly or "freeze" whenever a collision is imminent. This paradigm poses two problems: 1) freezing while navigating a crowd may cause people to trip and fall over the robot, resulting in more harm than the collision itself, and 2) in very dense social environments where collisions are unavoidable, such a control scheme would render the robot unable to move and preclude the opportunity to study how humans incorporate robots into these environments. However, if robots are to be meaningfully included in crowded social spaces, such as busy streets, subways, stores, or other densely populated locales, there may not exist trajectories that can guarantee zero collisions. Thus, adoption of robots in these environments requires the development of minimally disruptive navigation plans that can safely plan for and respond to contacts. We propose a learning-based motion planner and control scheme to navigate dense social environments using safe contacts for an omnidirectional mobile robot. The planner is evaluated in simulation over 360 trials with crowd densities varying between 0.0 and 1.6 people per square meter. Our navigation scheme is able to use contact to safely navigate in crowds of higher density than has been previously reported, to our knowledge.
翻译:在拥挤环境中,故意与人类接触(即“以接触为基础的”社会导航)的导航战略在拥挤环境中基本上没有被探索,尽管没有碰撞的社会导航是一个研究周密的问题。传统的社会导航框架要求机器人在碰撞即将来临时突然停止或“冻住”。这一范式提出了两个问题:(1)在人群航行时,冷冻可能导致人们绊倒和跌倒在机器人身上,造成比碰撞本身更大的伤害;(2)在碰撞不可避免的非常密集的社会环境中,这种控制方案将使机器人无法移动,排除研究人类如何将机器人纳入这些环境中的机会。然而,如果机器人被有意义地纳入拥挤的社会空间,例如繁忙的街道、地铁、商店或其他人口稠密的地方,则可能不存在能够保证零碰撞的轨迹。因此,在这种环境中采用机器人需要制定最起码的破坏性导航计划,能够安全地规划和应对接触。我们提议了一个基于学习的移动规划和控制计划,以便利用更安全的接触来安全地将机器人纳入这些环境中。如果机器人纳入拥挤的社会空间,例如繁忙的街道、地铁、地铁、商店或其他人口密集的移动轨道,则要用一个固定的机器人来评估。</s>