A framework for unknown environment manipulator motion planning via model based realtime rehearsal

In this paper, we propose a novel framework for unknown environment planning of manipulator type robots. Unknown environment motion planning, by its nature, requires a sensor based approach. The problem domain of unknown environment planning, when compared to model based approaches, is notoriously harder (NPHARD) in terms of demanding technical depth especially for difficult cases. The framework we propose herein is a sensor based planner composed of a sequence of multiple MBPs (Model Based Planners) in the notion of cognitive planning using realtime rehearsal. That is, one can use a certain model based planner as a tactical tool to attack location specific problems in overall planning endeavor for an unknown environment. The enabling technology for the realtime rehearsal is a sensitive skin type sensor introduced in the paper.We demonstrate the feasibility of solving a difficult unknown environment problem using the introduced sensor based planning framework.