Probabilistically Informed Robot Object Search with Multiple Regions

TL;DR

This paper presents a probabilistic approach using belief MDPs with options and Monte Carlo tree search for efficient robot object search in large, complex environments, improving scalability and sensor handling.

Contribution

It introduces a novel BMDP-O formulation enabling scalable, sensor-flexible robot search with Monte Carlo tree search, and proposes a faster approximate 'lite' version.

Findings

The approach outperforms receding horizon planners in large environments.

The 'lite' formulation achieves similar success with reduced computation time.

Incorporating options improves planning efficiency and scalability.

Abstract

The increasing use of autonomous robot systems in hazardous environments underscores the need for efficient search and rescue operations. Despite significant advancements, existing literature on object search often falls short in overcoming the difficulty of long planning horizons and dealing with sensor limitations, such as noise. This study introduces a novel approach that formulates the search problem as a belief Markov decision processes with options (BMDP-O) to make Monte Carlo tree search (MCTS) a viable tool for overcoming these challenges in large scale environments. The proposed formulation incorporates sequences of actions (options) to move between regions of interest, enabling the algorithm to efficiently scale to large environments. This approach also enables the use of customizable fields of view, for use with multiple types of sensors. Experimental results demonstrate the superiority of this approach in large environments when compared to the problem without options and alternative tools such as receding horizon planners. Given compute time for the proposed formulation is relatively high, a further approximated "lite" formulation is proposed. The lite formulation finds objects in a comparable number of steps with faster computation.