Abstract
Robot swarm combined with wireless communication has been a key driving force in recent few years and has currently expanded to wireless multihop networks, which include ad hoc radio networks, sensor networks, wireless mesh networks, etc. The aim of this paper is to propose an approach which introduces a polynomial time approximation path navigation algorithm and constructs dynamic state-dependent navigation policies. The proposed algorithm uses an inductive approach based on trial/error paradigm combined with swarm adaptive approaches to optimize simultaneously two criteria: cumulative cost path and end-to-end delay path. The approach samples, estimates, and builds the model of pertinent aspects of the environment. It uses a model that combines both a stochastic planned prenavigation for the exploration phase and a deterministic approach for the backward phase. To show the robustness and performances of the proposed approach, simulation scenario is built through the specification of the interested network topology and involved network traffic between robots. For this, this approach has been compared to traditional optimal path routing policy.
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