An Extended Treatment of Uncertainty Constrained robotic Exploration: An Integrated Exploration Planner

TL;DR

This paper introduces a probabilistic exploration framework for autonomous robots in uncertain environments, featuring a novel planner that guarantees success probabilities and is validated through hardware experiments.

Contribution

It develops G-PIE, a probabilistic exploration algorithm with guarantees, and RH-PIE, a receding horizon version addressing complexity, both validated in real-world tests.

Findings

G-PIE provides probabilistic guarantees of path completion.

RH-PIE reduces computational complexity for practical deployment.

Hardware-in-the-loop experiments confirm the effectiveness of the proposed planners.

Abstract

Efficient robotic exploration of unknown, sensor limited, global-information-deficient environments poses unique challenges to path planning algorithms. In these difficult environments, no deterministic guarantees on path completion and mission success can be made in general. Integrated Exploration (IE), which strives to combine localization and exploration, must be solved in order to create an autonomous robotic system capable of long term operation in new and challenging environments. This paper formulates a probabilistic framework which allows the creation of exploration algorithms providing probabilistic guarantees of success. A novel connection is made between the Hamiltonian Path Problem and exploration. The Guaranteed Probabilistic Information Explorer (G-PIE) is developed for the IE problem, providing a probabilistic guarantee on path completion, and asymptotic optimality of exploration. A receding horizon formulation, dubbed RH-PIE, is presented which addresses the exponential complexity present in G-PIE. Finally, RH-PIE planner is verified via autonomous, hardware-in-the-loop experiments.