Energy-Constrained Active Exploration Under Incremental-Resolution Symbolic Perception

TL;DR

This paper introduces a novel autonomous exploration framework that optimizes target search under energy constraints using incremental symbolic perception and MILP-based decision-making, with demonstrated efficiency and empirical validation.

Contribution

It presents a new decision-making approach combining automata and MILP techniques for energy-constrained exploration with incremental perception.

Findings

Effective target search within energy limits demonstrated

Online planning is computationally feasible for moderate environments

Empirical evaluation shows reduced expected regret

Abstract

In this work, we consider the problem of autonomous exploration in search of targets while respecting a fixed energy budget. The robot is equipped with an incremental-resolution symbolic perception module wherein the perception of targets in the environment improves as the robot's distance from targets decreases. We assume no prior information about the total number of targets, their locations as well as their possible distribution within the environment. This work proposes a novel decision-making framework for the resulting constrained sequential decision-making problem by first converting it into a reward maximization problem on a product graph computed offline. It is then solved online as a Mixed-Integer Linear Program (MILP) where the knowledge about the environment is updated at each step, combining automata-based and MILP-based techniques. We demonstrate the efficacy of our approach with the help of a case study and present empirical evaluation in terms of expected regret. Furthermore, the runtime performance shows that online planning can be efficiently performed for moderately-sized grid environments.