18,751 research outputs found
Conditional Task and Motion Planning through an Effort-based Approach
This paper proposes a preliminary work on a Conditional Task and Motion
Planning algorithm able to find a plan that minimizes robot efforts while
solving assigned tasks. Unlike most of the existing approaches that replan a
path only when it becomes unfeasible (e.g., no collision-free paths exist), the
proposed algorithm takes into consideration a replanning procedure whenever an
effort-saving is possible. The effort is here considered as the execution time,
but it is extensible to the robot energy consumption. The computed plan is both
conditional and dynamically adaptable to the unexpected environmental changes.
Based on the theoretical analysis of the algorithm, authors expect their
proposal to be complete and scalable. In progress experiments aim to prove this
investigation
Task planning using physics-based heuristics on manipulation actions
© 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
Sampling-Based Methods for Factored Task and Motion Planning
This paper presents a general-purpose formulation of a large class of
discrete-time planning problems, with hybrid state and control-spaces, as
factored transition systems. Factoring allows state transitions to be described
as the intersection of several constraints each affecting a subset of the state
and control variables. Robotic manipulation problems with many movable objects
involve constraints that only affect several variables at a time and therefore
exhibit large amounts of factoring. We develop a theoretical framework for
solving factored transition systems with sampling-based algorithms. The
framework characterizes conditions on the submanifold in which solutions lie,
leading to a characterization of robust feasibility that incorporates
dimensionality-reducing constraints. It then connects those conditions to
corresponding conditional samplers that can be composed to produce values on
this submanifold. We present two domain-independent, probabilistically complete
planning algorithms that take, as input, a set of conditional samplers. We
demonstrate the empirical efficiency of these algorithms on a set of
challenging task and motion planning problems involving picking, placing, and
pushing
Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs
In this paper, we present Batch Informed Trees (BIT*), a planning algorithm
based on unifying graph- and sampling-based planning techniques. By recognizing
that a set of samples describes an implicit random geometric graph (RGG), we
are able to combine the efficient ordered nature of graph-based techniques,
such as A*, with the anytime scalability of sampling-based algorithms, such as
Rapidly-exploring Random Trees (RRT).
BIT* uses a heuristic to efficiently search a series of increasingly dense
implicit RGGs while reusing previous information. It can be viewed as an
extension of incremental graph-search techniques, such as Lifelong Planning A*
(LPA*), to continuous problem domains as well as a generalization of existing
sampling-based optimal planners. It is shown that it is probabilistically
complete and asymptotically optimal.
We demonstrate the utility of BIT* on simulated random worlds in
and and manipulation problems on CMU's HERB, a
14-DOF two-armed robot. On these problems, BIT* finds better solutions faster
than RRT, RRT*, Informed RRT*, and Fast Marching Trees (FMT*) with faster
anytime convergence towards the optimum, especially in high dimensions.Comment: 8 Pages. 6 Figures. Video available at
http://www.youtube.com/watch?v=TQIoCC48gp
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