An Extended Treatment of Uncertainty Constrained robotic Exploration: An Integrated Exploration Planner
This addresses the challenge of creating autonomous robotic systems for long-term operation in new and challenging environments, representing a novel method for a known bottleneck.
The paper tackles the problem of robotic exploration in uncertain, sensor-limited environments by developing a probabilistic framework and algorithms (G-PIE and RH-PIE) that provide probabilistic guarantees on path completion and asymptotic optimality, verified through hardware-in-the-loop experiments.
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.