Predictive search model of flocking for quadcopter swarm in the presence of static and dynamic obstacles

One of the main challenges in swarm robotics is to achieve robust and scalable flocking, such that large numbers of robots can move together in a coordinated and cohesive manner while avoiding obstacles or threats. Flocking models in swarm robotic systems typically use reactive behaviors, such as cohesion, alignment, and avoidance. The use of potential fields has enabled the derivation of reactive control laws using obstacles and neighboring robots as sources of force for flocking. However, reactive behaviors, especially when a multitude of them are simultaneously active, as in the case of flocking, are prone to cause collisions or inefficient motion within the flock due to its short-sighted approach. Approaches that aimed to generate smoother and optimum flocking, such as the use of model predictive control, would either require centralized coordination, or distributed coordination which requires low-latency and high-bandwidth communication requirements within the swarm as well as high computational resources. In this paper, we present a predictive search model that can generate smooth and safe flocking of robotic swarms in the presence of obstacles by taking into account the predicted states of other robots in a computationally efficient way. We tested the proposed model in environments with static and dynamic obstacles and compared its performance with a potential field flocking model in simulation. The results show that the predictive search model can generate smoother and faster flocking in swarm robotic systems in the presence of static and dynamic obstacles. Furthermore, we tested the predictive search model with different numbers of robots in environments with static obstacles in simulations and demonstrated that it is scalable to large swarm sizes. The performance of the predictive search model is also validated on a swarm of six quadcopters indoors in the presence of static and dynamic obstacles.