To err is robotic, to tolerate immunological: fault detection in multirobot systems

Area of Science:

• Robotics
• Artificial Intelligence
• Complex Systems

Background:

• Fault detection and tolerance are critical challenges in multirobot systems (MRS).
• Existing methods struggle with dynamic behavioral changes and require predefined normality models.
• The vertebrate immune system offers a model for adaptive abnormality detection without hardwired normality.

Purpose of the Study:

• To develop a generic abnormality detection approach for MRS inspired by the adaptive immune system.
• To evaluate the effectiveness of this approach in detecting faulty robots within a swarm.
• To assess the scalability and robustness of the method against temporal behavioral variations.

Main Methods:

• Modeled abnormality detection on the principles of the vertebrate adaptive immune system.
• Implemented and evaluated the approach in a simulated swarm of robots.
• Simulated common electro-mechanical and software faults in robot behaviors.

Main Results:

• The proposed method robustly detected abnormal robots exhibiting simulated faults.
• Detection accuracy was maintained despite temporal variations in swarm behavior.
• The abnormality detection approach demonstrated scalability with increasing swarm size and behavioral complexity.

Conclusions:

• An immune system-inspired approach provides a robust and adaptable solution for fault detection in MRS.
• This method overcomes limitations of traditional approaches by not requiring a fixed characterization of normal behavior.
• The technique is scalable and effective for long-term, reliable operation of multirobot systems.