Robust Collision-Avoidance Formation Navigation of Velocity and Input-Constrained Multirobot Systems.

Area of Science:

• Robotics and Control Systems
• Multi-Agent Systems
• Autonomous Navigation

Background:

• Safe deployment of multiple robots in complex, obstacle-rich environments is a significant challenge.
• Velocity and input-constrained robots require robust collision-avoidance strategies for formation navigation.
• Constrained dynamics and external disturbances complicate safe formation navigation.

Purpose of the Study:

• To develop a novel robust control barrier function-based method for safe formation navigation of multiple robots.
• To enable collision avoidance under globally bounded control input for constrained robots.
• To address the challenges posed by constrained dynamics and external disturbances in multi-robot systems.

Main Methods:

• Design of a nominal velocity and input-constrained formation navigation controller using relative position information and a predefined-time convergent observer.
• Derivation of new robust safety barrier conditions specifically for collision avoidance.
• Proposal of a local quadratic optimization problem-based safe formation navigation controller for individual robots.

Main Results:

• The proposed method effectively enables collision avoidance for multiple robots operating under velocity and input constraints.
• The controller ensures safe transferring between areas in complex, obstacle-rich environments.
• Simulation examples demonstrate the controller's effectiveness and superiority compared to existing methods.

Conclusions:

• The novel robust control barrier function-based method provides a viable solution for safe multi-robot deployment.
• The approach successfully handles constrained dynamics and external disturbances, ensuring robust collision avoidance.
• The proposed controller facilitates safe and efficient formation navigation in challenging environments.