A hybrid inductive learning-based and deductive reasoning-based 3-D path planning method in complex environments

Abstract Traditional path planning methods, such as sampling-based and iterative approaches, allow for optimal path’s computation in complex environments. Nonetheless, environment exploration is subject to rules which can be obtained by domain experts could used improving the search. The present work aims at integrating inductive techniques that generate candidates with deductive choose preferred ones. In particular, an learning model trained expert demonstrations translated into a reward function, while logic programming starting point according some expert’s suggestions. We discuss, use case, 3-D neurosurgical steerable needles. Results show proposed method computes paths terms of obstacle clearance kinematic constraints compliance, able outperform state-of-the-art approaches safety distance-from-obstacles respect, smoothness, computational time.