  Fault-tolerant coverage control involves determining a trajectory that
enables an autonomous agent to cover specific points of interest, even in the
presence of actuation and/or sensing faults. In this work, the agent encounters
control inputs that are erroneous; specifically, its nominal controls inputs
are perturbed by stochastic disturbances, potentially disrupting its intended
operation. Existing techniques have focused on deterministically bounded
disturbances or relied on the assumption of Gaussian disturbances, whereas
non-Gaussian disturbances have been primarily been tackled via scenario-based
stochastic control methods. However, the assumption of Gaussian disturbances is
generally limited to linear systems, and scenario-based methods can become
computationally prohibitive. To address these limitations, we propose a
hierarchical coverage controller that integrates mixed-trigonometric-polynomial
moment propagation to propagate non-Gaussian disturbances through the agent's
nonlinear dynamics. Specifically, the first stage generates an ideal reference
plan by optimising the agent's mobility and camera control inputs. The
second-stage fault-tolerant controller then aims to follow this reference plan,
even in the presence of erroneous control inputs caused by non-Gaussian
disturbances. This is achieved by imposing a set of deterministic constraints
on the moments of the system's uncertain states.
