Progress-Based Fault Detection and Health-Aware Task Allocation for Heterogeneous Multi-Robot Systems
This work addresses reliability and efficiency issues for heterogeneous multi-robot systems, but it is incremental as it builds on existing methods like Kalman filters and convex optimization.
The paper tackled fault detection and task allocation in heterogeneous multi-robot systems by developing a progress-based detector with a Kalman filter and integrating it into a health-aware allocator, resulting in timely detection in noise and bias cases while maintaining task completion with limited reassignment.
We present a progress-based fault detection module and its integration with dynamic task allocation for heterogeneous robot teams. The detector monitors a normalized task-completion signal with a lightweight Kalman filter (KF) and a normalized innovation squared (NIS) test, augmented with a low-rate stall gate, an uncertainty gate, and debounce logic. Health estimates influence the allocator via health-weighted costs and health-dependent masks; reallocation is event-triggered and regularized with an $\ell_1$ assignment-change penalty to limit reassignment churn while preserving feasibility through slack variables. The detector has constant per-robot update cost, and the allocation remains a convex quadratic program (QP). Experiments on a common team-task setup evaluate measurement-noise increases, velocity-slip biases, communication dropouts, and task abandonment. The results show timely detection in the noise and bias cases, maintained task completion with limited reassignment, and the expected observability delays under communication dropouts.