在资源有限下，让机器人应对各种不确定性并可靠完成任务。
Resource-Constrained Robotic Planning in the face of Mixed Uncertainty
- 用消费型马尔可夫决策过程建模机器人行动与资源消耗。
- 在不耗尽资源前提下，最大化满足任务逻辑目标的概率。
- 适合需要高可靠性与资源管理的工业机器人场景。
机器人在运行中面临显著不确定性，包括可量化的噪声和不可量化的未知因素，同时必须遵守严格的资源约束。本文提出一种合成鲁棒策略的方法，指导机器人完成指定任务，且确保系统不会耗尽资源。首先，将机器人系统建模为带有集合转移的消费型马尔可夫决策过程（CMDPST），统一刻画非确定性动作、可量化与不可量化不确定性及资源消耗。随后，将任务规范表示为有限轨迹上的线性时序逻辑（LTLf）公式，并解决资源受限下的最优鲁棒策略合成问题：在不耗尽资源的前提下，最大化满足LTLf目标的概率。提出的解决方案包含两种方法：一种基于直接展开的通用方法，另一种通过状态空间剪枝优化的高效方法。在仓库运输网络上的实验验证了所提方法的有效性。
原文摘要 · Abstract (English)
Robots operate under significant uncertainty, from quantifiable noise to unquantifiable unknowns, and must account for strict operational constraints, such as limited resources. In this paper, we consider the problem of synthesizing robust strategies to guide a robot's actions in fulfilling a given task, while ensuring the system never exhausts its resources. To solve this problem, we first model the robotic system as a Consumption Markov Decision Process with Set-valued Transitions(CMDPST), a unified framework modelling nondeterministic actions, quantifiable and unquantifiable uncertainty, and resource consumption. Then, we combine the CMDPST with the task specification, expressed as a Linear Temporal Logic over finite traces (LTLf ) formula. Lastly, we address the resource constrained optimal robust strategy synthesis problem, which aims to synthesize a strategy that maximizes the probability of satisfying the LTLf objective without resource exhaustion. Our solution involves two techniques: a direct unrolling-based method and a more efficient, optimized approach that leverages state-space pruning for better performance. Experiments on a warehouse transportation network show the effectiveness of the proposed solutions.
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