A heuristic and complete planner for the classical mover's problem

Hwang, Yong Hee; Chen, P.C.

doi:10.1109/robot.1995.525370

Cited by 29 publications

(21 citation statements)

References 22 publications

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“…Other planners based upon the A * search have been developed [14], [19]. These are not always suitable for local planning strategies because of how expensive they are to evaluate and compute in both space and time complexity.…”

Section: B Local Plannersmentioning

confidence: 99%

The Toggle Local Planner for sampling-based motion planning

Denny

Amato

2012

2012 IEEE International Conference on Robotics and Automation

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Abstract-Sampling-based solutions to the motion planning problem, such as the probabilistic roadmap method (PRM), have become commonplace in robotics applications. These solutions are the norm as the dimensionality of the planning space grows, i.e., d > 5. An important primitive of these methods is the local planner, which is used for validation of simple paths between two configurations. The most common is the straight-line local planner which interpolates along the straight line between the two configurations. In this paper, we introduce a new local planner, Toggle Local Planner (Toggle LP), which extends local planning to a two-dimensional subspace of the overall planning space. If no path exists between the two configurations in the subspace, then Toggle LP is guaranteed to correctly return f alse. Intuitively, more connections could be found by Toggle LP than by the straight-line planner, resulting in better connected roadmaps. As shown in our results, this is the case, and additionally, the extra cost, in terms of time or storage, for Toggle LP is minimal. Additionally, our experimental analysis of the planner shows the benefit for a wide array of robots, with DOF as high as 70.

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Section: B Local Plannersmentioning

confidence: 99%

The Toggle Local Planner for sampling-based motion planning

Denny

Amato

2012

2012 IEEE International Conference on Robotics and Automation

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“…In turn, these theoretical results suggest improvements of the planner. Other work investigating similar or related randomized planning approaches include 1,5,15,16,35,36]. Formal attempts to predict the behavior of speci c random planners are proposed in very few papers 9, 2 8 ].…”

Section: Introductionmentioning

confidence: 99%

A Random Sampling Scheme for Path Planning

Barraquand¹,

Kavraki²,

Latombe³

et al. 1996

Robotics Research

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Several randomized p ath planners have been proposed during the last few years. Their attractiveness stems from their applicability to virtually any type o f r obots, and their empirically observed s u c cess. In this paper we attempt to present a unifying view of these planners and to theoretically explain their success. First, we introduce a general planning scheme that consists of randomly sampling the robot's con guration space. We then describe t w o p r eviously developed planners as instances of planners based on this scheme, but applying very di erent sampling strategies. These planners are p r obabilistically complete: if a path exists, they will nd one with high probability, if we let them run long enough. Next, for one of the planners, we analyze the relation between the probability of failure and the running time. Under assumptions characterizing the \goodness" of the robot's free s p ace, we show that the running time only grows as the absolute value of the logarithm of the probability of failure t h a t w e a r e willing to tolerate. We also show that it increases at a reasonable ra t e a s t h e s p ace g o odness degrades. In the last section we suggest directions for future r esearch.

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“…The main feature of our algorithm is to find a solution path in a practical amount of time while maintaining the resolution completeness, i.e., guaranteeing a solution if it exists. This is exactly the motivation for the development of the Sandros search strategy, whose typical running time is in the range of a few minutes both for a manipulator [3] and for a rigid body [9]. In this paper we describe the extension of the Sandros methodology to the motion planning of multiple rigid bodies.…”

Section: Algorithmmentioning

confidence: 98%

“…1 exhaustive search is performed at a user-specified resolution, making our planner resolution complete. This planner was initially developed for motion planning of a single manipulator [3], and later extended to a single rigid object [9]. In this paper we present an application of the Sandros planner to multiple moving objects.…”

Section: Introductionmentioning

confidence: 99%

Motion planning for multiple moving objects

Hwang

Proceedings. IEEE International Symposium on Assembly and Task Planning

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We present a motion planner for multiple moving objects in two dimensions. The search for collisionfree paths is performed in the composite configuration space of all the moving objects to guarantee a solution, and the efficiency of our planner is demonstrated with examples. Our motion planner can be characterized with a hierarchical, multi-resolution search of the configuration space along with a generate-and-test paradigm for solution paths. Because of the high dimensionality of the composite configuration space, our planner is most useful for cases with a small number of moving objects. Some of the potential applications are navigation of several mobile robots, and planning part motions for a multi-handed assembly operation.

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A heuristic and complete planner for the classical mover's problem

Cited by 29 publications

References 22 publications

The Toggle Local Planner for sampling-based motion planning

The Toggle Local Planner for sampling-based motion planning

A Random Sampling Scheme for Path Planning

Motion planning for multiple moving objects

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