Curl-free vector field for collision avoidance in a swarm of autonomous drones

Abstract

FIRAS function-based potential field method is the standard for collision avoidance as implemented in isolated drones. However, its use in an autonomous swarm can be problematic. Its complex interconnected structure causes one of the known issues when there are multiple simultaneously active control objectives. They are intra-swarm collision avoidance and reaching the target relative distance (normally referred to as formation control). This paper shows that with collision avoidance active, the standard potential field method will cause a local minima-like effect in an autonomous swarm. To prevent it, this paper proposes a modified curl-free vector field-based algorithm. This modification enables extended lateral circular motion to prevent swarm members from getting stuck in a local minimum. Stability theory methods are invoked to show that the formation remains stable when running the proposed algorithm. Comparative numerical experiments were run on a drone swarm model in MATLAB/Simulink to illustrate the functioning of this algorithm. To prove the proposed method effective, the paper presents simulation results for standard vs modified potential field.