In this task we devise fault-tolerant planning and control techniques that address various types of faults occurring during planning and execution, such as those encountered in wildfire monitoring and coverage tasks. We aim to explore how different stochastic disturbances (e.g., disturbances arising from various probability distributions) can be integrated into the planning process to ensure robust operation. This approach will enable the system to predict and, to some extent, anticipate persistent disturbances during real-time operations, thereby enhancing fault tolerance and ensuring mission continuity.
Specifically, we demonstrate the proposed approach for fault-tolerant coverage control (i.e., which 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). We assume, that 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.
The details of the proposed approach can be found in the references listed below.
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