Power of Finite Memory and Finite Communication Robots under Asynchronous Scheduler
In swarm robotics, a set of robots has to perform a given task with specified internal
capabilities (model) and under a given adversarial scheduler. Relation between a model $
M_1 $ under scheduler $ S_1 $, and that of a model $ M_2 $ under scheduler $ S_2 $ can
be of four different types: not less powerful, more powerful, equivalent and orthogonal. In
literature there are four main models of robots with lights: $\mathcal {LUMI} $, where robots
have the power of observing the lights of all the robots, $\mathcal {FSTA} $, where each …
capabilities (model) and under a given adversarial scheduler. Relation between a model $
M_1 $ under scheduler $ S_1 $, and that of a model $ M_2 $ under scheduler $ S_2 $ can
be of four different types: not less powerful, more powerful, equivalent and orthogonal. In
literature there are four main models of robots with lights: $\mathcal {LUMI} $, where robots
have the power of observing the lights of all the robots, $\mathcal {FSTA} $, where each …
In swarm robotics, a set of robots has to perform a given task with specified internal capabilities (model) and under a given adversarial scheduler. Relation between a model under scheduler , and that of a model under scheduler can be of four different types: not less powerful, more powerful, equivalent and orthogonal. In literature there are four main models of robots with lights: , where robots have the power of observing the lights of all the robots, , where each robot can see only its own light, , where each robot can observe the light of all other robots except its own and , where the robots do not have any light. In this paper, we investigate the computational power of and model under asynchronous scheduler by comparing it with other model and scheduler combinations. Our main focus is to understand and compare the power of persistent memory and explicit communication in robots under asynchronous scheduler.
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