On multiple moving objects

Erdmann, Michael; Lozano-Pérez, Tomás

doi:10.1007/bf01840371

Cited by 543 publications

(223 citation statements)

References 22 publications

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“…Some of the most popular algorithms are based on decomposition techniques, which assume that the articulated model can be decomposed into components with low-correlation and then use suitable strategies to coordinate between different components [5], [24], [32]. However, these methods may not work well in dealing with narrow passages.…”

Section: A Motion Planning For Articulated Modelsmentioning

confidence: 99%

Retraction-based RRT planner for articulated models

Pan

Zhang

Manocha

2010

2010 IEEE International Conference on Robotics and Automation

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Abstract-We present a new retraction algorithm for high DOF articulated models and use our algorithm to improve the performance of RRT planners in narrow passages. The retraction step is formulated as a constrained optimization problem and performs iterative refinement on the boundary of CObstacle space. We also combine the retraction algorithm with decomposition planners to handle very high DOF articulated models. The performance of our approach is analyzed using Voronoi diagrams and we show that our retraction algorithm provides a good approximation to the ideal RRT-extension in constrained environments. We have implemented our algorithm and tested its performance on robots with more than 40 DOFs in complex environments. In practice, we observe significant performance (2-80X) improvement over prior RRT planners on challenging scenarios with narrow passages.

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Section: A Motion Planning For Articulated Modelsmentioning

confidence: 99%

Retraction-based RRT planner for articulated models

Pan

Zhang

Manocha

2010

2010 IEEE International Conference on Robotics and Automation

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“…Efficient methods to solve the single robot motion planning are available [2][3][4] and will not be further discussed here (recent books on motion planning like [5] and [6] provide extensive and up to date coverage of the topic). A common approach to solve the multi-robot motion planning problem consists in assigning priorities to robots and planning their motion according to them [7]. Paths for robots are computed one after the other, according to their priority.…”

Section: Related Workmentioning

confidence: 99%

An experimental study of distributed robot coordination

Carpin

Pagello

2009

Robotics and Autonomous Systems

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Coordinating the path of multiple robots along assigned paths is a computationally hard problem with great potential for applications. We here provide a detailed experimental study of a randomized algorithm for scheduling priorities we have developed, also comparing it with exact and approximated solutions. It turns out that for problems of reasonable size our algorithm exhibits an appealing compromise between speed and quality.

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“…However, such regions, representing reachable sets by the dynamic obstacles, are typically hard to compute exactly in the general case, and conservative approximations are used, e.g., to estimate regions of inevitable collision [16]. Other recent contributions in this area include [17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Incremental Sampling-Based Algorithms for a Class of Pursuit-Evasion Games

Karaman

2010

Springer Tracts in Advanced Robotics

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Pursuit-evasion games have been used for modeling various forms of conflict arising between two agents modeled as dynamical systems. Although analytical solutions of some simple pursuit-evasion games are known, most interesting instances can only be solved using numerical methods requiring significant offline computation. In this paper, a novel incremental sampling-based algorithm is presented to compute optimal open-loop solutions for the evader, assuming worst-case behavior for the pursuer. It is shown that the algorithm has probabilistic completeness and soundness guarantees. As opposed to many other numerical methods tailored to solve pursuit-evasion games, incremental sampling-based algorithms offer anytime properties, which allow their real-time implementations in online settings.

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On multiple moving objects

Cited by 543 publications

References 22 publications

Retraction-based RRT planner for articulated models

Retraction-based RRT planner for articulated models

An experimental study of distributed robot coordination

Incremental Sampling-Based Algorithms for a Class of Pursuit-Evasion Games

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