Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Roadmap-Based Flocking for Complex Environments
PG '02 Proceedings of the 10th Pacific Conference on Computer Graphics and Applications
ACM SIGGRAPH 2006 Papers
Planning Algorithms
Reconfigurations in Graphs and Grids
SIAM Journal on Discrete Mathematics
Coordinating Pebble Motion On Graphs, The Diameter Of Permutation Groups, And Applications
SFCS '84 Proceedings of the 25th Annual Symposium onFoundations of Computer Science, 1984
Multiple path coordination for mobile robots: a geometric algorithm
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Sampling-based algorithms for optimal motion planning
International Journal of Robotics Research
CGAL Arrangements and Their Applications: A Step-by-Step Guide
CGAL Arrangements and Their Applications: A Step-by-Step Guide
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We present a simple and natural extension of the multi-robot motion planning problem where the robots are partitioned into groups (colors), such that in each group the robots are interchangeable. Every robot is no longer required to move to a specific target, but rather to some target placement that is assigned to its group. We call this problem k-color multi-robot motion planning and provide a sampling-based algorithm specifically designed for solving it. At the heart of the algorithm is a novel technique where the k-color problem is reduced to several discrete multi-robot motion planning problems. These reductions amplify basic samples into massive collections of free placements and paths for the robots. We demonstrate the performance of the algorithm by an implementation for the case of disc robots and polygonal robots translating in the plane. We show that the algorithm successfully and efficiently copes with a variety of challenging scenarios, involving many robots, while a simplified version of this algorithm, that can be viewed as an extension of a prevalent sampling-based algorithm for the k-color case, fails even on simple scenarios. Interestingly, our algorithm outperforms a well established implementation of PRM for the standard multi-robot problem, in which each robot has a distinct color.