Towards Safe Motion Planning in Human Workspaces: A Robust Multi-agent Approach
Towards Safe Motion Planning in Human Workspaces: A Robust Multi-agent Approach
复制标题
实现人类工作空间中的安全运动规划：稳健的多智能体方法
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
A. Thomaz
中科院分区：
文献类型：
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作者：
Shih;B. Fernández;P. Stone;A. Thomaz
机器人与人类共享工作空间变得越来越可行。然而，为了在保持敏捷性能的同时安全地做到这一点，他们需要能够平稳地处理由人类运动引起的动态和不确定性。马尔可夫决策过程（MDP）作为一个共同的框架，制定机器人规划问题。然而，由于它的单智能体配方，这样的计划不能考虑人类的反应时，评估机器人的行动。因此，机器人可能会遭受不安全的运动，并以附近人类难以理解的方式移动。为了解决这个问题，我们，而不是模型机器人规划在人类大脑中作为一个随机游戏，并贡献了一个强大的规划算法，它使机器人占其预测误差在人类的反应，以防止碰撞，同时不失去灵活性，而不是传统的最大最小优化技术，通过应用最大最小操作只在“临界状态”。我们验证的行人行为的部分知识下的方法，并表明，我们的方法遇到零碰撞，尽管不完美的预测，同时提高路径效率，相比基线。
It is becoming increasingly feasible for robots to share a workspace with humans. However, for them to do so safely while maintaining agile performance, they need the ability to smoothly handle the dynamics and uncertainty caused by human motions. Markov Decision Processes (MDPs) serve as a common framework to formulate robot planning problems. However, because of its single-agent formulation, such planner cannot account for human reaction when evaluating robot actions. The robot can thus suffer from unsafe motions and move in ways that are hard for nearby humans to understand. To resolve this, we instead model robot planning in human workspaces as a Stochastic Game, and contribute a robust planning algorithm, which enables the robot to account for its prediction errors in human responses to prevent collision, while not losing agility, opposed to traditional maximin optimization techniques, by applying maximin operation only at "critical states". We validate the approach under partial knowledge of pedestrian behaviors, and show that our approach encounters zero collision despite imperfect prediction, while improving path efficiency, compared to baselines.