提出在线安全规划新方法，实时保证机器人决策安全且持续优化
Anytime Probabilistically Constrained Provably Convergent Online Belief Space Planning
- 基于蒙特卡洛树搜索，在连续空间中实现随时可中断的安全规划
- 少量搜索次数下比基线更安全，且目标性能持续更优
- 动态修正树中统计值，剔除剪枝动作影响，提升决策可靠性
考虑未来风险对自主机器人在线决策至关重要，以确保不仅找到最优且安全的动作。本文基于最近提出的概率信念依赖约束形式化，提出一种基于蒙特卡洛树搜索（MCTS）的任意时间方法，适用于连续状态空间。与以往方法不同，本方法在当前展开的搜索树上始终保证安全性，无需等待搜索收敛。我们证明了算法版本在概率意义下的指数级收敛性，并通过大量仿真验证了所提技术。即使仅进行极少数树查询，本文方法找到的最佳动作也远比基线更安全；此外，在目标性能方面，本方法始终优于基线，因其会更新并修正搜索树中的价值与统计量，移除被剪枝动作的贡献。
原文摘要 · Abstract (English)
Taking into account future risk is essential for an autonomously operating robot to find online not only the best but also a safe action to execute. In this paper, we build upon the recently introduced formulation of probabilistic belief-dependent constraints. We present an anytime approach employing the Monte Carlo Tree Search (MCTS) method in continuous domains. Unlike previous approaches, our method assures safety anytime with respect to the currently expanded search tree without relying on the convergence of the search. We prove convergence in probability with an exponential rate of a version of our algorithms and study proposed techniques via extensive simulations. Even with a tiny number of tree queries, the best action found by our approach is much safer than the baseline. Moreover, our approach constantly finds better than the baseline action in terms of objective. This is because we revise the values and statistics maintained in the search tree and remove from them the contribution of the pruned actions.
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