Anticipating Degradation: A Predictive Approach to Fault Tolerance in Robot Swarms
James O'Keeffe
AI summary
Problem
Existing swarm fault tolerance research primarily addresses sudden, spontaneous failures using reactive strategies like shutting down faulty robots, overlooking the common reality of gradual hardware degradation that progressively undermines swarm performance.
Approach
The authors model gradual motor and sensor degradation and introduce a novel distributed, immune-inspired fault detection algorithm that identifies degrading robots early and directs them to a maintenance base before failure occurs.
Key results
- Novel distributed fault detection model inspired by natural immune systems
- Predictive fault resolution matches or exceeds reactive shutdown strategies across tested environments and fault levels
- Reactive approaches prove unsustainable in constrained spaces where stranded faulty robots obstruct swarm progress
- Validated across swarm sizes of 5, 10, and 20 robots with 20% to 60% fault affliction rates
Why it matters
Enables long-term autonomy for robot swarms in inaccessible or dangerous real-world deployments where physical robot retrieval or repair is impractical.
Abstract
An active approach to fault tolerance is essential for robot swarms to achieve long-term autonomy. Previous efforts have focused on responding to spontaneous electro-mechanical faults and failures. However, many faults occur gradually over time. This work argues that the principles of predictive mainte- nance, in which potential faults are resolved before they hinder the operation of the swarm, offer a promising means of achieving long-term fault tolerance. This is a novel approach to swarm fault tolerance, which is shown to give a comparable or improved performance when tested against a reactive approach in almost all cases tested.