Environment-Adaptive Synergistic Swarm With Flexible Obstacle Avoidance via Active and Passive Strategy
Kai Shen, Shiying Li, Yinghe Ding, Zheng Ming Xu, Pengfei Yang · IEEE Transactions on Systems Man and Cybernetics Systems · 2025
The fascinating collective behaviors of natural swarm systems have inspired extensive studies on configuration generation of drone swarm. In this article, we propose a synergistic swarm algorithm (SSA) to realize stable spacing configuration and consistent flight of drones. In order to cope with complex mission requirements and achieve safe and fast flight in dense environments, we further propose a flexible obstacle avoidance (FOA) strategy via passive and active environmental adaption. POA algorithm provides drones with self-adaptive forces along drone-obstacle linkages for getting rid of dangerous position and keeping swarm safe. AOA algorithm provides drones with lateral forces at a certain distance for correcting course of traversal and keeping swarm rapid. We carried out a series of simulation experiments, including swarms of up to 16 drones in mass point model and of up to four drones in 6DOF model. Simulation results illustrated that our strategy and algorithms can ensure fast flight speed and safety of the swarm in dense environments.