Resource-Constrained Robotic Planning in the face of Mixed Uncertainty

TL;DR

This paper introduces a unified framework for robotic planning under uncertainty and resource constraints, combining a novel model with strategy synthesis methods to maximize task success probability.

Contribution

It proposes the Consumption Markov Decision Process with Set-valued Transitions (CMDPST) and integrates it with LTLf specifications for resource-aware strategy synthesis.

Findings

The unrolling-based method effectively synthesizes strategies under uncertainty.

State-space pruning improves efficiency of the strategy synthesis.

Experiments demonstrate successful application in warehouse transportation.

Abstract

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.