An Integrated Approach to Goal Selection in Mobile Robot Exploration

TL;DR

This paper presents an integrated method for autonomous mobile robot exploration that combines goal generation and path optimization using an evolutionary algorithm, outperforming existing methods in certain environments.

Contribution

It introduces a novel evolutionary algorithm with indirect representation for solving the d-Watchman Route Problem in robot exploration.

Findings

Outperforms state-of-the-art methods by up to 12.5% in low obstacle density environments.

Slightly less effective by 4.5% in office-like environments.

Validated on both simulated maps and real robot hardware.

Abstract

This paper deals with the problem of autonomous navigation of a mobile robot in an unknown 2D environment to fully explore the environment as efficiently as possible. We assume a terrestrial mobile robot equipped with a ranging sensor with a limited range and 360 degrees field of view. The key part of the exploration process is formulated as the d-Watchman Route Problem which consists of two coupled tasks - candidate goals generation and finding an optimal path through a subset of goals - which are solved in each exploration step. The latter has been defined as a constrained variant of the Generalized Traveling Salesman Problem and solved using an evolutionary algorithm. An evolutionary algorithm that uses an indirect representation and the nearest neighbor based constructive procedure was proposed to solve this problem. Individuals evolved in this evolutionary algorithm do not directly code the solutions to the problem. Instead, they represent sequences of instructions to construct a feasible solution. The problems with efficiently generating feasible solutions typically arising when applying traditional evolutionary algorithms to constrained optimization problems are eliminated this way. The proposed exploration framework was evaluated in a simulated environment on three maps and the time needed to explore the whole environment was compared to state-of-the-art exploration methods. Experimental results show that our method outperforms the compared ones in environments with a low density of obstacles by up to 12.5%, while it is slightly worse in office-like environments by 4.5% at maximum. The framework has also been deployed on a real robot to demonstrate the applicability of the proposed solution with real hardware.