Robust Near-Optimal Coordination in Uncertain Multiagent Networks With Motion Constraints

Area of Science:

• Robotics
• Control Theory
• Artificial Intelligence

Background:

• Multiagent systems face challenges in robust coordination, especially with nonlinear dynamics and motion constraints like velocity saturation.
• Ensuring collision avoidance while maintaining coordinated motion is critical for safe and efficient multiagent operations.

Purpose of the Study:

• To develop a robust coordination strategy for nonlinear uncertain second-order multiagent networks.
• To simultaneously achieve multiagent coordination and collision avoidance while respecting motion constraints.

Main Methods:

• Employed a single-critic neural network-based approximate dynamic programming approach.
• Utilized exact estimation of unknown dynamics for online learning of optimal value functions and controllers.
• Incorporated avoidance penalties and designed novel value functions and learning algorithms.

Main Results:

• Achieved simultaneous multiagent coordination and collision avoidance.
• Guaranteed uniformly ultimately bounded convergence of closed-loop dynamical stability.
• Ensured strict adherence to all motion constraints, including velocity saturation and collision avoidance.

Conclusions:

• The proposed feedback-based coordination strategy effectively addresses robust coordination and collision avoidance in complex multiagent systems.
• The method's effectiveness is validated through numerical simulations, demonstrating its practical applicability.