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

Jack Cline, Christian Macaranas, Siavash Farzan

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.

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

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 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.
Paper Structure (27 sections, 15 equations, 4 figures, 6 tables)

This paper contains 27 sections, 15 equations, 4 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Heterogeneous Task Allocation
  4. Fault Detection and Diagnosis for Robot Teams
  5. Fault-Tolerant Allocation
  6. Preliminaries and Problem Formulation
  7. Notation and System Model
  8. Dynamic Heterogeneous Task Allocation
  9. Fault Model and Objectives
  10. Progress-Based Fault Detection
  11. Progress Signal Construction
  12. Estimator
  13. NIS Decision Rule with Operational Gates
  14. Properties
  15. Communication Dropout Analysis
  16. ...and 12 more sections

Figures (4)

  • Figure 1: Overview of the proposed methodology: progress is monitored to infer robot health, which conditions allocation decisions; execution affects the environment and produces new progress measurements.
  • Figure 2: Initial robot and task positions (in meters) used across scenarios. Tasks are placed at $(0.35,0.50)$, $(0.75,0.75)$, and $(0.85,0.55)$. Robots are initially placed at $(0.3, 0.1)$, $(0.5, 0.1)$, $(0.75, 0.1)$, and $(0.9, 0.1)$.
  • Figure 3: Representative measurement-noise fault experiment for robots $i\in\{0,1,2,3\}$. After the measurement-noise increase is injected on Robot 2, the NIS crosses the detection threshold, triggering the integration layer to reallocate the affected task while maintaining limited reassignment churn.
  • Figure 4: ROC curves over fault-magnitude sweeps. As measurement noise and bias increase, discriminability improves and AUC approaches one, matching residual-test theory.