Networks of Realistic Robots

The goal of this thesis is to survey and analyze the current work done by the distributed robotics community to find the more realistic variations of the standard OBLOT model, develop new such variations, and determine which approach should be used in the long term. We develop a new, optimal Rendezvous algorithm using lights, and prove it using a model checking framework based on the SPIN model checker. The same luminous model is used to build robust Leader Election algorithms, which allow for stricter constraints. We design a new vision model for mobile robots, Uncertain Visibility, which introduces a vision adversary to model false negatives in sensors, and prove tight bounds under this new model for several benchmark problems. We then define and investigate a new problem, Obstruction Detection, for the obstructed visibility model. To facilitate analysis of robot networks, we develop a framework for Monte-Carlo simulations of mobile robots, designed to simulate any model or algorithm with minimal effort. It is designed as a complement to researcher "intuition" to look for unexpected behavior. We test this simulator against numerous algorithms and settings, yielding encouraging results. Finally, we introduce another two algorithms: the first ensures the distance traveled for convergence in ASYNC is minimal ; the second allows for Leader Election with errors in vision.