Path planning with far-away obstacles detection under uncertainty.

Abstract

Nowadays faster terrestrial and space exploration robots are being investigated, in response to the growing demand for faster, more efficient, and cost-effective exploration and research capabilities. For these rapid mobile platforms, the identification and avoidance of far obstacles are critical, since their high speed implies the need to take into account as many near and far obstacles as possible for the global path computation, avoiding any possible accident due to their speed and the computation time of the replanning algorithms. Due to their distance, the far obstacles are not included within the local maps, which are limited by the range of the depth cameras. For these reasons, this paper proposes the use of Artificial Intelligence techniques to detect them from images and estimate their sizes and positions with a certain degree of uncertainty. The detected obstacles will be later included in the global maps, correcting the global path in case it collides with them.