Interactive search for action and motion planning with dynamics. Issue 5 (2nd September 2016)

• Record Type: Journal Article
• Authors: Plaku, Erion; Le, Duong
• Is Part Of: Journal of experimental & theoretical artificial intelligence. Volume 28:Issue 5(2016)
• Page Start: 849
• Page End: 869
• Publication Date: 2016-09-02
• Subjects: Sampling-based motion planning -- AI planning -- PDDL -- robot dynamics.; Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/0952813X.2016.1146348
• Languages: English
• ISSNs: 0952-813X

Abstract

Abstract : This paper proposes an interactive search approach, termed INTERACT, which couples sampling-based motion planning with action planning in order to effectively solve the combined task and motion planning problem. INTERACT is geared towards scenarios involving a mobile robot operating in a fully known environment consisting of static and movable objects. INTERACT makes it possible to specify a task in the planning domain definition language (PDDL) and automatically computes a collision-free and dynamically feasible trajectory that enables the robot to accomplish the task. The coupling of sampling-based motion planning with action planning is made possible by expanding a tree of feasible motions and partitioning it into equivalence classes based on the task predicates. Action plans provide guidance as to which a equivalence class should be further expanded. Information gathered during the motion tree expansion is used to adjust the action costs in order to effectively guide the expansion towards the goal. This interactive process of selecting an equivalence class, expanding the motion tree to implement its action plan and updating the action costs and plans to reflect the progress made is repeated until a solution is found. Experimental validation is provided in simulation using a robotic vehicle to accomplish sophisticated pick-and-place tasks. Comparisons to previous work show significant improvements.