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Operation of the Collaborative Composite Manufacturing CCM System

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Research on the Multiagent Joint Proximal Policy Optimization Algorithm Controlling Cooperative Fixed-Wing UAV

Weiwei Zhao^1,2, Hairong Chu¹, Xikui Miao³

¹Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, No. 3888, Dongnanhu Rd., Changchun 130033, China.

Sensors (Basel, Switzerland)

|August 23, 2020

Summary

This summary is machine-generated.

This study introduces the Multi-Agent Joint Proximal Policy Optimization (MAJPPO) algorithm for enhanced multi-unmanned aerial vehicle (UAV) cooperation. The novel approach improves control intelligence and adaptability in complex environments.

Keywords:

multiagent cooperative multiple unmanned aerial vehicles (multi-UAV) formation obstacle avoidance proximal policy optimization (PPO)reinforcement learning the joint state-value function

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Area of Science:

Robotics
Artificial Intelligence
Control Systems

Background:

Multi-unmanned aerial vehicle (UAV) collaboration offers significant potential for complex tasks.
Traditional multi-agent reinforcement learning faces challenges in non-stationary environments due to evolving agent strategies.

Purpose of the Study:

To enhance the intelligence and environmental adaptability of multi-UAV cooperative control.
To address the non-stationarity problem in multi-agent reinforcement learning environments.

Main Methods:

Proposes the Multi-Agent Joint Proximal Policy Optimization (MAJPPO) algorithm, featuring centralized learning and decentralized execution.
Employs a moving window averaging method to provide agents with a centralized state value function for improved collaboration.
Utilizes a six-degree of freedom, 12-state UAV dynamics model with an attitude control loop to simplify control complexity.

Main Results:

The MAJPPO algorithm demonstrates enhanced collaboration among multi-UAV systems.
The proposed algorithm leads to an increased sum of reward values for the multi-agent system.
Evaluations in multi-UAV formation and multi-obstacle crossing tasks show superior performance and environmental adaptability.

Conclusions:

The MAJPPO algorithm effectively improves multi-UAV cooperative control by mitigating non-stationarity.
The approach enhances system performance, collaboration, and adaptability in dynamic environments.
This work provides a robust framework for intelligent multi-UAV systems.