Social Momentum: A Framework for Legible Navigation in Dynamic Multi-Agent Environments
Social Momentum: A Framework for Legible Navigation in Dynamic Multi-Agent Environments
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社交动力：动态多代理环境中清晰导航的框架
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Ross A. Knepper
中科院分区：
文献类型：
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作者：
Christoforos Mavrogiannis;Wil B. Thomason;Ross A. Knepper
意图表达的机器人运动已被证明会导致提高效率和减少规划工作的人类。现有的用于生成意图表达机器人行为的框架通常集中在静态或结构化环境中的应用。在这样的设置下，重点放在将机器人的预期最终配置传达给其他代理上。然而，在动态的，非结构化的和多智能体域，如行人环境，机器人的最终配置的知识是不够的信息，因为它完全忽略了代理之间的交互的复杂动态。为了解决这个问题，我们设计了一个规划框架，旨在生成运动，清楚地传达代理的预期防撞策略，而不是其目的地。我们的框架估计最有可能的意图避免协议的其他人根据他们过去的行为，叠加它们，并生成一个富有表现力和社会兼容的机器人动作，加强对这些避免协议的期望。这一行动促进了每个人的推理和决策，如代理轨迹的简化拓扑模式所示。广泛的模拟表明，我们的框架始终实现显着降低拓扑复杂性，与常见的基准方法相比，在多智能体碰撞避免。这一结果的意义真实的世界的应用程序证明了用户的研究，揭示了统计证据表明，多智能体的轨迹较低的拓扑复杂性往往有利于观察员的推理。
Intent-expressive robot motion has been shown to result in increased efficiency and reduced planning efforts for copresent humans. Existing frameworks for generating intent-expressive robot behaviors have typically focused on applications in static or structured environments. Under such settings, emphasis is placed towards communicating the robot’s intended final configuration to other agents. However, in dynamic, unstructured and multi-agent domains, such as pedestrian environments, knowledge of the robot’s final configuration is not sufficiently informative as it completely ignores the complex dynamics of interaction among agents. To address this problem, we design a planning framework that aims at generating motion that clearly communicates an agent’s intended collision avoidance strategy rather than its destination. Our framework estimates the most likely intended avoidance protocols of others based on their past behaviors, superimposes them, and generates an expressive and socially compliant robot action that reinforces the expectations of others regarding these avoidance protocols. This action facilitates inference and decision making for everyone, as illustrated in the simplified topological pattern of agents’ trajectories. Extensive simulations demonstrate that our framework consistently achieves significantly lower topological complexity, compared against common benchmark approaches in multi-agent collision avoidance. The significance of this result for real world applications is demonstrated by a user study that reveals statistical evidence suggesting that multi-agent trajectories of lower topological complexity tend to facilitate inference for observers.