Adaptive Online Fault Diagnosis in Autonomous Robot Swarms.

Area of Science:

• Robotics
• Artificial Intelligence
• Swarm Intelligence

Background:

• Robot swarms often lack tolerance to partial robot failures, impacting collective behaviors.
• Active fault tolerance is crucial for swarm systems, requiring fault identification and resolution.
• Current active fault tolerance methods lack fault diagnosis, hindering long-term swarm autonomy.

Purpose of the Study:

• To propose a novel fault diagnosis method for active fault tolerance in robot swarms.
• To enhance the long-term autonomy of robot swarms through effective fault diagnosis.
• To improve the resilience of robot swarms to partial and complete robot failures.

Main Methods:

• Developed a fault diagnosis system mimicking functions of natural immune systems.
• Implemented and simulated the proposed method within a robot swarm context.
• Evaluated the system's performance in identifying and addressing electro-mechanical faults.

Main Results:

• The proposed fault diagnosis system demonstrated flexibility and scalability.
• The system significantly improved swarm tolerance to various electro-mechanical faults.
• Simulated experiments confirmed the effectiveness of the immune-inspired approach.

Conclusions:

• Fault diagnosis is a necessary component for achieving long-term autonomy in active fault-tolerant robot swarms.
• The immune-inspired fault diagnosis method offers a promising approach for enhancing swarm resilience.
• This work lays the foundation for more robust and autonomous swarm robotic systems.