© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ; In order to solve mobile manipulation problems, the efficient combination of task and motion planning is usually required. Moreover, the incorporation of physics-based information has recently been taken into account in order to plan the tasks in a more realistic way. In the present paper, a task and motion planning framework is proposed based on a modified version of the Fast-Forward task planner that is guided by physics-based knowledge. The proposal uses manipulation knowledge for reasoning on symbolic literals (both in offline and online modes) taking into account geometric information in order to evaluate the applicability as well as feasibility of actions while evaluating the heuristic cost. It results in an efficient search of the state space and in the obtention of low-cost physically-feasible plans. The proposal has been implemented and is illustrated with a manipulation problem consisting of a mobile robot and some fixed and manipulatable objects. ; Peer Reviewed ; Postprint (author's final draft)
Task planning using physics-based heuristics on manipulation actions
2016-01-01
Miscellaneous
Electronic Resource
English
symbolic literals , Mobile robots--Automatic control , fixed objects , physics-based knowledge , mobile robots , geomètric information , motion planning , physics-based information , low-cost physically-feasible plans , fastforward task planner , manipulatable objects , path planning , Robots mòbils -- Control automàtic , Àrees temàtiques de la UPC::Informàtica::Automàtica i control , mobile robot , manipulators , state space , mobile manipulation , task planning , manipulation knowledge , state-space methods
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