Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Unreliable failure detectors for reliable distributed systems
Journal of the ACM (JACM)
Group Behaviors for Systems with Significant Dynamics
Autonomous Robots
NASA's JPL Nanorover Outposts Project Develops Colony of Solar-Powered Nanorovers
IEEE Intelligent Systems
Flocking by a Set of Autonomous Mobile Robots
Flocking by a Set of Autonomous Mobile Robots
Coordination without communication: the case of the flocking problem
Discrete Applied Mathematics - Fun with algorithms 2 (FUN 2001)
Stabilizing flocking via leader election in robot networks
SSS'07 Proceedings of the 9h international conference on Stabilization, safety, and security of distributed systems
Fault-tolerant and self-stabilizing mobile robots gathering
DISC'06 Proceedings of the 20th international conference on Distributed Computing
Distributed Diagnosis in Formations of Mobile Robots
IEEE Transactions on Robotics
Oracle-Based Flocking of Mobile Robots in Crash-Recovery Model
SSS '09 Proceedings of the 11th International Symposium on Stabilization, Safety, and Security of Distributed Systems
Fault-tolerant flocking for a group of autonomous mobile robots
Journal of Systems and Software
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This paper studies the flocking problem, where mobile robots group to form a desired pattern and move together while maintaining that formation. Unlike previous studies of the problem, we consider a system of mobile robots in which a number of them may possibly fail by crashing. Our algorithm ensures that the crash of faulty robots does not bring the formation to a permanent stop, and that the correct robots are thus eventually allowed to reorganize and continue moving together. Furthermore, the algorithm makes no assumption on the relative speeds at which the robots can move. The algorithm relies on the assumption that robots' activations follow a k -bounded asynchronous scheduler, in the sense that the beginning and end of activations are not synchronized across robots (asynchronous), and that while the slowest robot is activated once, the fastest robot is activated at most k times (k -bounded). The proposed algorithm is made of three parts. First, appropriate restrictions on the movements of the robots make it possible to agree on a common ranking of the robots. Second, based on the ranking and the k -bounded scheduler, robots can eventually detect any robot that has crashed, and thus trigger a reorganization of the robots. Finally, the third part of the algorithm ensures that the robots move together while keeping an approximation of a regular polygon, while also ensuring the necessary restrictions on their movement.