Uncertainty Constrained Robotic Exploration: An Integrated Exploration Planner

摘要

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 that 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 probabilistic information explorer (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.