Auxiliary Heuristics for Frontier Based Planners

TL;DR

This paper introduces learned heuristics via imitation of clairvoyant oracles and a filter-based heuristic to improve the efficiency of frontier-based exploration planners in unknown environments.

Contribution

It presents a novel approach of using learned heuristics and a filter-based heuristic to enhance frontier-based exploration without relying on search trees.

Findings

Learned heuristics improve exploration efficiency.

Filter-based heuristic enhances coverage planning performance.

Approach reduces computational load compared to traditional methods.

Abstract

Autonomous exploration of unknown environments is a vital function for robots and has applications in a wide variety of scenarios. Our focus primarily lies in its application for the task of efficient coverage of unknown environments. Various methods have been proposed for this task and frontier based methods are an efficient category in this class of methods. Efficiency is of utmost importance in exploration and heuristics play a critical role in guiding our search. In this work we demonstrate the ability of heuristics that are learnt by imitating clairvoyant oracles. These learnt heuristics can be used to predict the expected future return from selected states without building search trees, which are inefficient and limited by on-board compute. We also propose an additional filter-based heuristic which results in an enhancement in the performance of the frontier-based planner with respect to certain tasks such as coverage planning.