Autonomous Shepherding Behaviors of Multiple Target Steering Robots

Area of Science:

• Robotics
• Artificial Intelligence
• Swarm Intelligence

Background:

• Multi-robot systems require effective coordination strategies for complex tasks.
• Biologically-inspired approaches offer novel solutions for decentralized control.

Purpose of the Study:

• To develop a distributed coordination methodology for multi-robot systems.
• To implement a biologically-inspired control law for a robotic shepherding task.

Main Methods:

• Utilized nearest-neighbor interactions for decentralized robot control.
• Designed a control strategy for shepherd robots guiding flocking robots.
• Implemented flocking behavior in sheep robots and steering behavior in shepherd robots.
• Introduced a novel rule for flock consolidation.

Main Results:

• Shepherd robots effectively guided flocking robots to a desired location.
• A few shepherd robots were sufficient to control the entire flock.
• The system demonstrated effective flock consolidation from scattered formations.
• Simulation results validated the approach's effectiveness in terms of time cost and path accuracy.

Conclusions:

• The proposed distributed coordination methodology is effective for multi-robot shepherding.
• Biologically-inspired control laws enable efficient decentralized task execution in swarm robotics.
• The approach offers a scalable and robust solution for controlling large robot groups.