| 1. |
- Bidot, Julien, 1977-, et al.
(författare)
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Geometric backtracking for combined task and motion planning in robotic systems
- 2017
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Ingår i: Artificial Intelligence. - : Elsevier. - 0004-3702 .- 1872-7921. ; 247, s. 229-265
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Tidskriftsartikel (refereegranskat)abstract
- Planners for real robotic systems should not only reason about abstract actions, but also about aspects related to physical execution such as kinematics and geometry. We present an approach to hybrid task and motion planning, in which state-based forward-chaining task planning is tightly coupled with motion planning and other forms of geometric reasoning. Our approach is centered around the problem of geometric backtracking that arises in hybrid task and motion planning: in order to satisfy the geometric preconditions of the current action, a planner may need to reconsider geometric choices, such as grasps and poses, that were made for previous actions. Geometric backtracking is a necessary condition for completeness, but it may lead to a dramatic computational explosion due to the large size of the space of geometric states. We explore two avenues to deal with this issue: the use of heuristics based on different geometric conditions to guide the search, and the use of geometric constraints to prune the search space. We empirically evaluate these different approaches, and demonstrate that they improve the performance of hybrid task and motion planning. We demonstrate our hybrid planning approach in two domains: a real, humanoid robotic platform, the DLR Justin robot, performing object manipulation tasks; and a simulated autonomous forklift operating in a warehouse.
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| 2. |
- Lagriffoul, Fabien, 1977-, et al.
(författare)
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Combining Task and Motion Planning is Not Always a Good Idea
- 2013
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Konferensbidrag (refereegranskat)abstract
- Combining task and motion planning requires tointerleave causal and geometric reasoning, in order to guaranteethe plan to be executable in the real world. The resulting searchspace, which is the cross product of the symbolic search spaceand the geometric search space, is huge. Systematically calling ageometric reasoner while evaluating symbolic actions is costly. Onthe other hand, geometric reasoning can prune out large parts ofthis search space if geometrically infeasible actions are detectedearly. Hence, we hypothesized the existence of a search depthlevel, until which geometric reasoning can be interleaved withsymbolic reasoning with tractable combinatorial explosion, whilekeeping the benefits of this pruning. In this paper, we propose asimple model that proves the existence of such search depth level,and validate it empirically through experiments in simulation
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| 3. |
- Lagriffoul, Fabien, 1977-, et al.
(författare)
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Efficiently combining task and motion planning using geometric constraints
- 2014
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Ingår i: The international journal of robotics research. - : SAGE Publications. - 0278-3649 .- 1741-3176. ; 33:14, s. 1726-1747
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Tidskriftsartikel (refereegranskat)abstract
- We propose a constraint-based approach to address a class of problems encountered in combined task and motion planning (CTAMP), which we call kinematically constrained problems. CTAMP is a hybrid planning process in which task planning and geometric reasoning are interleaved. During this process, symbolic action sequences generated by a task planner are geometrically evaluated. This geometric evaluation is a search problem per se, which we refer to as geometric backtrack search. In kinematically constrained problems, a significant computational effort is spent on geometric backtrack search, which impairs search at the task level. At the basis of our approach to address this problem, is the introduction of an intermediate layer between task planning and geometric reasoning. A set of constraints is automatically generated from the symbolic action sequences to evaluate, and combined with a set of constraints derived from the kinematic model of the robot. The resulting constraint network is then used to prune the search space during geometric backtrack search. We present experimental evidence that our approach significantly reduces the complexity of geometric backtrack search on various types of problem.
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