Affiliations: [a] Computer Sciences and Engineering, Faculty of Engineering and Natural Sciences, Sabancı University, İstanbul, Turkey. E-mail: [email protected] | [b] Mechatronics Engineering, Faculty of Engineering and Natural Sciences, Sabancı University, İstanbul, Turkey. E-mail: [email protected] | [c] Department of Computer Engineering, Marmara University, İstanbul, Turkey. E-mail: [email protected]
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).
Keywords: Task planning, motion planning, hybrid planning, knowledge representation and reasoning, answer set programming, cognitive robotics, experimental analysis
