Reliability-Aware Multi-UAV Coverage Path Planning using a Genetic Algorithm

Graceful degradation is a desirable trait in applications that require coverage with real, failure-prone robots. Thus paper uses methods informed by Reliability Engineering to study the Reliability-Aware Multi-Agent Coverage Path Planning (RA-MCPP) problem. An augmented stochastic framework is applied to evaluate a strategy's probability of mission completion. An augmented stochastic framework is applied to evaluate a strategy's probability of mission completion (PoC) on 3D lattice graph environments. A Genetic Algorithm optimisation approach is then proposed to find RA-MCPP path plans which maximise PoC. It is shown that the GA provides good solutions at reasonable runtimes, complementing previous approaches which focused on global optimality guarantees at the cost of massive computation, especially for medium and large environments.