Cooperative Formation and Obstacle Avoidance Control for Multi-UAV Based on Guidance Route and Artificial Potential Field

Research on cooperative control of multi-UAV systems has gained significant attention in the flight control field, with a particular focus on formation control and obstacle avoidance due to their complexity and importance. This paper introduces an approach to a group of quadcopter control by integrating fuzzy controller, guidance route, and Artificial Potential Field (APF) methods. The quadcopter dynamic model, featuring six degrees of freedom, is controlled using a fuzzy state feedback controller in its inner loop. From the outer loop, the formation-making is guided by an easy-to-use and versatile guidance route approach while obstacle avoidance is tackled using the optimal APF method. There are two avoidance strategies that can be compared and analyzed, called "total avoidance" and "minimal avoidance", both individually and as a "combined" strategy. Simulations in various environments with different obstacle sizes show that all control algorithms can accomplish the tasks effectively. Both strategies have their own strength in terms of path length and formation maintenance. A formation performance index, which is calculated based on the difference between the desired position and the actual position of each quadcopter, is used to quantify the effectiveness of the method. A smaller value means better formation maintenance. The total avoidance strategy achieved an average index of 0.8000 and the minimal avoidance strategy reached 1.2227. These metrics highlight the trade-offs of each strategy in maintaining optimal formation. These findings offer valuable insights for the development of more robust multi-UAV systems, with potential applications in autonomous delivery services, surveillance, and environmental monitoring.