针对对手的随机多目标运动动力学规划
Stochastic Multi-Objective Kinodynamic Planning Against Adversaries
- University of Michigan（密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究在含随机混合对手环境下的多目标运动动力学规划，核心方法是将规划空间转移到闭环策略序列，通过蒙特卡洛粒子展开评估风险，引入SMO-RRT和SMO-SST算法，推导有限样本界，实现概率安全规划。
AI中文摘要：
本文研究在存在随机混合对手的环境中的多目标运动动力学规划，对手会根据自身状态概率性地转变为对抗模式。目标是构建在执行成本和违反安全约束概率（风险）之间权衡的路径帕累托前沿。现有的机会约束规划器在开环轨迹上评估风险，导致过于保守的解决方案。为此，我们将规划空间转移到闭环策略序列，并通过蒙特卡洛粒子展开将基于样本的风险评估直接集成到树构建中。我们首先引入随机多目标快速扩展随机树（SMO-RRT）并证明其概率完备性，接着引入随机多目标稳定稀疏快速扩展随机树（SMO-SST），它利用选择性剪枝以牺牲完备性为代价提高数值性能。对于这两种算法，我们推导了具有非高斯、状态依赖不确定性系统违反机会约束概率的有限样本界，实现了在适用于多智能体系统、社会导航和自动驾驶的广泛环境中的概率安全规划。
英文摘要：
This paper addresses multi-objective kinodynamic planning in environments with stochastic hybrid adversaries that probabilistically transition to adversarial modes based on the ego state. The goal is to construct the Pareto-front of paths that trade off execution cost and the probability of safety constraint violation (risk). Existing chance-constrained planners evaluate risk over open-loop trajectories, yielding overly conservative solutions that fail to account for ego-agent reactivity. To address this limitation, we shift the planning space to sequences of closed-loop policies, and integrate sample-based risk evaluation directly into tree construction via Monte-Carlo particle rollouts. We first introduce Stochastic Multi-Objective RRT (SMO-RRT), for which we prove probabilistic completeness, followed by Stochastic Multi-Objective Stable Sparse RRT (SMO-SST), which leverages selective pruning to improve numerical performance at the cost of completeness. For both algorithms, we derive a finite-sample bound on the probability of chance constraint violation for systems with non-Gaussian, state-dependent uncertainty, enabling probabilistically safe planning in a broad class of environments applicable to multi-agent systems, social navigation, and autonomous driving.
