Progress-Based Fault Detection and Health-Aware Task Allocation for Heterogeneous Multi-Robot Systems

TL;DR

This paper introduces a progress-based fault detection system integrated with health-aware task allocation for heterogeneous robot teams, improving fault detection and task reallocation efficiency under various operational disturbances.

Contribution

It presents a novel fault detection module using Kalman filters and a new integrated task allocation method that accounts for robot health, enhancing robustness and reactivity in multi-robot systems.

Findings

Timely detection of faults under noise and bias conditions.

Maintained task completion with limited reassignments.

Effective detection despite communication dropouts.

Abstract

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.