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

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