Searching for close alternative plans

Felner, Ariel; Stern, Roni; Rosenschein, Jeffrey S.; Pomeransky, Alex

doi:10.1007/s10458-007-9012-y

Cited by 2 publications

(1 citation statement)

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“…There may be many ways to repair a plan (Felner, Stern, Rosenschein, & Pomeransky, 2007), but in this work we focus on repair execution policies that do so by forcing some of the agents that were not delayed to wait. We distinguish between two types of such repair policies:…”

Section: How To Modify a Planmentioning

confidence: 99%

Robust Multi-Agent Path Finding and Executing

Atzmon

Stern

Felner

et al. 2020

jair

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Multi-agent path-finding (MAPF) is the problem of finding a plan for moving a set of agents from their initial locations to their goals without collisions. Following this plan, however, may not be possible due to unexpected events that delay some of the agents. In this work, we propose a holistic solution for MAPF that is robust to such unexpected delays. First, we introduce the notion of a k-robust MAPF plan, which is a plan that can be executed even if a limited number (k) of delays occur. We propose sufficient and required conditions for finding a k-robust plan, and show how to convert several MAPF solvers to find such plans. Then, we propose several robust execution policies. An execution policy is a policy for agents executing a MAPF plan. An execution policy is robust if following it guarantees that the agents reach their goals even if they encounter unexpected delays. Several classes of such robust execution policies are proposed and evaluated experimentally. Finally, we present robust execution policies for cases where communication between the agents may also be delayed. We performed an extensive experimental evaluation in which we compared different algorithms for finding robust MAPF plans, compared different ro- bust execution policies, and studied the interplay between having a robust plan and the performance when using a robust execution policy.

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Section: How To Modify a Planmentioning

confidence: 99%

Robust Multi-Agent Path Finding and Executing

Atzmon

Stern

Felner

et al. 2020

jair

Self Cite

View full text Add to dashboard Cite

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Multi-robot plan adaptation by constrained minimal distortion feature mapping

Takács¹,

Demiris²

2009

2009 IEEE International Conference on Robotics and Automation

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We propose a novel method for multi-robot plan adaptation which can be used for adapting existing spatial plans of robotic teams to new environments or imitating collaborative spatial teamwork of robots in novel situations. The algorithm selects correspondences between previous and current spatial features by the application of pairwise constraints, and generates the transformation function with a fast regular grid approximation which minimizes distortion. The algorithm requires minimal domain knowledge, is capable of transforming the spatial aspects of collaborative team behavior and performs better in noisy problems with large displacements than the most generally used quadratic differences method. The algorithm can be utilized for rapid plan adaptation, plan generalization or team behavior imitation. Methods are demonstrated on a multi-robot control problem in a random environment.

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Searching for close alternative plans

Cited by 2 publications

References 0 publications

Robust Multi-Agent Path Finding and Executing

Robust Multi-Agent Path Finding and Executing

Multi-robot plan adaptation by constrained minimal distortion feature mapping

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