History-Based Response Threshold Model for Division of Labor in Multi-Agent Systems

Area of Science:

• Robotics
• Artificial Intelligence
• Multi-agent Systems

Background:

• Effective task allocation is crucial for decentralized multi-robot systems.
• Previous models often rely on direct communication, which can be a bottleneck.
• Dynamic environments necessitate adaptive task allocation strategies.

Purpose of the Study:

• To develop and evaluate a communication-free dynamic task allocation algorithm for robots.
• To enhance the efficiency and robustness of task allocation in decentralized multi-robot foraging.
• To investigate the impact of task demand history on robot behavior and system performance.

Main Methods:

• Application of the response threshold model for task selection in a simulated multi-robot foraging environment.
• Each robot utilizes a task switching function based on local task demand and a constant-sized task demand history.
• Robots independently manage task switching based on environmental stimuli without inter-robot communication.

Main Results:

• The proposed algorithm enables robots to autonomously regulate the division of labor through specialized task tendencies.
• Maintaining a history of task demands proved highly effective for dynamic foraging scenarios.
• Simulations demonstrated superior performance of the proposed algorithm compared to conventional models.

Conclusions:

• The communication-free response threshold model with task demand history is an effective strategy for dynamic task allocation in multi-robot systems.
• This approach enhances system efficiency and adaptability in decentralized foraging tasks.
• The findings suggest a viable method for improving coordination in swarm robotics without explicit communication.