A systematic analysis of levels of integration between high-level task planning and low-level feasibility checks

Abstract

We provide a systematic analysis of levels of integration between discrete high-level reasoning and continuous low-level feasibility checks to address hybrid planning problems in robotic applications. We identify four distinct strategies for such an integration: (i) low-level checks are done for all possible cases in advance and the results are used during plan generation; (ii) low-level checks are done exactly when they are needed during the search for a plan; (iii) low-level checks are done after a plan is computed, and if the plan is found infeasible then a new plan is computed; (iv) similar to the previous strategy but the results of previous low-level checks are used during computation of a new plan. We analyze the usefulness of these strategies and their combinations by experiments on hybrid planning problems in different robotic application domains, in terms of computational efficiency and plan quality (relative to its feasibility).