Robot

Engineering

Fault detection and isolation

Swarm robotics

Actuator

Real-time computing

Ant robotics

Systems engineering

Anomaly detection

Computer science

Swarm behaviour

Fault (geology)

Robot control

Seismology

Geology

Outlier

Task (project management)

Pattern recognition (psychology)

Mobile robot

Robotics

Artificial intelligence

Computer vision

IEEE Transactions on Robotics

The ability to reliably detect faults is essential in many realworld tasks that robot swarms have the potential to perform.Most studies on fault detection in swarm robotics have been conducted exclusively in simulation, and they have focused on a single type of fault or a specific task.In a series of previous studies, we have developed a robust fault-detection approach in which robots in a swarm learn to distinguish between normal and faulty behaviors online.In this paper, we assess the performance of our fault-detection approach on a swarm of seven physical mobile robots.We experiment with three classic swarm robotics tasks and consider several types of faults in both sensors and actuators.Experimental results show that the robots are able to reliably detect the presence of hardware faults in one another even when the swarm behavior is changed during operation.This paper is thus an important step toward making robot swarms sufficiently reliable and dependable for real-world applications.

Fault Detection in a Swarm of Physical Robots Based on Behavioral Outlier Detection