We focus on decentralized navigation among multiple non-communicating agents in continuous domains without explicit traffic rules, such as sidewalks, hallways, or squares. Following collision-free motion in such domains requires effective mechanisms of multiagent behavior prediction. Although this prediction problem can be shown to be NP-hard, humans are often capable of solving it efficiently by leveraging sophisticated mechanisms of implicit coordination. Inspired by the human paradigm, we propose a novel topological formalism that explicitly models multiagent coordination. Our formalism features both geometric and algebraic descriptions enabling the use of standard gradient-based optimization techniques for trajectory generation but also symbolic inference over coordination strategies. In this article, we contribute (a) HCP (Hamiltonian Coordination Primitives), a novel multiagent trajectory-generation pipeline that accommodates spatiotemporal constraints formulated as symbolic topological specifications corresponding to a desired coordination strategy; (b) HCPnav, an online planning framework for decentralized collision avoidance that generates motion by following multiagent trajectory primitives corresponding to high-likelihood, low-cost coordination strategies. Through a series of challenging trajectory-generation experiments, we show that HCP outperforms a trajectory-optimization baseline in generating trajectories of desired topological specifications in terms of success rate and computational efficiency. Finally, through a variety of navigation experiments, we illustrate the efficacy of HCPnav in handling challenging multiagent navigation scenarios under homogeneous or heterogeneous agents across a series of environments of different geometry.

我们专注于在没有明确交通规则的连续域中多个非通信代理之间的分散导航，例如人行道、走廊或广场。在这些域中遵循无碰撞运动需要有效的多智能体行为预测机制。虽然这个预测问题可以被证明是 NP-hard，但人类通常能够通过利用复杂的隐式协调机制来有效地解决它。受人类范式的启发，我们提出了一种新颖的拓扑形式主义，它明确地对多智能体协调进行建模。我们的形式主义具有几何和代数描述，能够使用标准的基于梯度的优化技术来生成轨迹，而且还可以对协调策略进行符号推理。在本文中，我们贡献了 (a) HCP（哈密尔顿协调原语），一种新的多智能体轨迹生成管道，它适应制定为对应于所需协调策略的符号拓扑规范的时空约束；(b) HCPnav，一种用于分散碰撞避免的在线规划框架，它通过遵循与高似然、低成本协调策略相对应的多智能体轨迹原语来生成运动。通过一系列具有挑战性的轨迹生成实验，我们表明 HCP 在生成所需拓扑规范的轨迹方面在成功率和计算效率方面优于轨迹优化基线。最后，通过各种导航实验，