15 research outputs found

    Task planning using physics-based heuristics on manipulation actions

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    © 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 ReviewedPostprint (author's final draft

    Path Planning with Modified a Star Algorithm for a Mobile Robot

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    AbstractThis article deals with path planning of a mobile robot based on a grid map. Essential assumption for path planning is a mobile robot with functional and reliable reactive navigation and SLAM. Therefore, such issues are not addressed in this article. The main body of the article introduces several modifications (Basic Theta*, Phi*) and improvements (RSR, JPS) of A star algorithm. These modifications are focused primarily on computational time and the path optimality. Individual modifications were evaluated in several scenarios, which varied in the complexity of environment. On the basis of these evaluations, it is possible to choose path planning method suitable for individual scenario

    Autonomous Motion Planning for Avatar Limbs

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    In this work, a new algorithm for autonomous avatar motion is presented. The new algorithm is based in the Rapidly-exploring Random Tree (RRT) and an appropriate ontology. It uses a novel approach for calculating the motion sequence planning for the different avatar limbs: legs or arms. First, the algorithm uses the information stored in the ontology concerning the avatar structure and the Degrees Of Freedom (DOFs) to obtain the basic actions for motion planning. Second, this information is used to perform the growth process in the RRT algorithm. Then, all this information is used to produce planning. The plans are generated by a random search for possible motions that respect the structural restrictions of the avatar on kinesiology studies. To avoid a big configuration space search, exploration, exploitation, and hill climbing are used in order to obtain motion plans
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