Publication
We provide a comparison of a series of original coordination mechanisms for the distributed boundary coverage problem with a swarm of miniature robots. Our analysis is based on real robot experimentation and models at different levels of abstraction. Distributed boundary coverage is an instance of the distributed coverage problem and has applications such as inspection of structures, de-mining, cleaning, and painting. Coverage is a particularly good example for the benefits of a multi-robot approach due to the potential for parallel task execution and additional robustness out of redundancy. The constraints imposed by a potential application, the autonomous inspection of a jet turbine engine, were our motivation for the algorithms considered in this thesis. Thus, there is particular emphasis on how algorithms perform under the influence of sensor and actuator noise, limited computational and communication capabilities, as well as on the policies about how to cope with such problems. The algorithms developed in this dissertation can be classified into reactive and deliberative algorithms, as well as non-collaborative and collaborative algorithms. The performance of these algorithms ranges from very low to very high, corresponding to highly redundant coverage to near-optimal partitioning of the environments, respectively. At the same time, requirements and assumptions on the robotic platform and the environment (from no communication to global communication, and from no localization to global localization) are incrementally raised. All the algorithms are robust to sensor and actuator noise and gracefully decay to the performance of a randomized algorithm as a function of an increased noise level and/or additional hardware constraints. Although the deliberative algorithms are fully deterministic, the actual performance is probabilistic due to inevitable sensor and actuator noise. For this reason, probabilistic models are used for predicting time to complete coverage and take into account sensor and actuator noise calibrated by using real hardware. For reactive systems with limited memory, the performance is captured using a compact representation based on rate equations that track the expected number of robots in a certain state. As the number of states explode for the deliberative algorithms that require a substantial use of memory, this approach becomes less tractable with the amount of deliberation performed, and we use Discrete Event System (DES) simulation in these cases. Our contribution to the domain of multi-robot systems is three-fold. First, we provide a methodology for system identification and optimal control of a robot swarm using probabilistic models. Second, we develop a series of algorithms for distributed coverage by a team of miniature robots that gracefully decay from a near-optimal performance to the performance of a randomized approach under the influence of sensor and actuator noise. Third, we design an implement a miniature in