Obstacle Avoidance Control for Multiagent Systems Based on Model-Free Adaptive Control.

Area of Science:

• Robotics
• Control Systems Engineering
• Artificial Intelligence

Background:

• Multiagent systems face challenges in collision and obstacle avoidance, particularly in nonlinear control scenarios.
• Existing methods often require complex models or centralized control, limiting scalability and adaptability.
• Data-driven approaches offer potential for more flexible and robust solutions in dynamic environments.

Purpose of the Study:

• To develop a data-driven cooperative output control strategy for collision and obstacle avoidance in nonlinear multiagent systems.
• To design a safe reference trajectory planning method that enables agents to dynamically bypass obstacles.
• To create a distributed tracking controller that utilizes only input-output information for adaptive collision prevention.

Main Methods:

• A safe reference trajectory planning method dynamically projects unsafe trajectory segments to circumvent obstacles.
• A distributed tracking controller with two dynamical barrier functions is designed using input-output data.
• Sufficient conditions are derived to guarantee successful obstacle avoidance and safe navigation.

Main Results:

• The proposed strategy enables agent formations to effectively avoid stationary and moving obstacles.
• The method prevents collisions between individual agents within the formation.
• Agents successfully track desired trajectories while maintaining formation integrity.

Conclusions:

• The data-driven cooperative output approach provides an effective solution for collision and obstacle avoidance in nonlinear multiagent control.
• The proposed trajectory planning and adaptive control methods ensure safe and efficient navigation.
• Simulation results validate the robustness and performance of the strategy in complex scenarios.