Multi-UAV Path Following using Vector-Field Guidance

Aiming at the problem that obstacle avoidance flexibility and formation integrity are difficult to coexist in multi-UAV formation motion in complex obstacle environments , and that the traditional artificial potential field (APF) method easily falls into local optima, a cooperative obstacle avoidance algorithm for multi-UAV formations integrating BI-APF-RRT and affine transformation is proposed. First, abandoning the traditional APF centroid path planning method , a goal-biased Bidirectional Artificial Potential Field method RRT (BI-APF-RRT) algorithm is adopted to conduct global collision-free path planning for the centroid of the leader formation. By introducing an improved artificial potential field and cubic B-spline interpolation, the smoothness and rapid convergence of the global path are ensured. Secondly, using the generated global path as the guiding trajectory for the formation's centroid , combined with an affine transformation matrix (including non-uniform scaling and rotation) , the formation can adaptively deform based on the distance to obstacles while moving along the optimal path. Finally, the followers track the leaders through a distributed control law , enabling the entire formation to safely cross complex obstacle areas without disassembling.

In this study, we propose a new sheepdog-inspired control method for a swarm of small unmanned aerial vehicles (UAVs), which predicts the swarm behavior while explicitly accounting for the motion constraints of real robots. Sheepdog-inspired guidance control refers to a framework in which a small number of navigator agents (sheepdog agents) indirectly drive a large number of autonomous agents (a flock of sheep agents) so as to steer the group toward a target position. In conventional studies on sheepdog-inspired guidance, both types of agents have typically been modeled as point masses, and the guidance law for the navigator agents has been designed using simple interaction vectors based on the instantaneous relative positions between the agents. However, when implementing such methods on real robots such as drones, it is necessary to consider each agent's motion constraints, including upper bounds on velocity and acceleration. Moreover, we argue that guidance can be made more efficient by predicting the future behavior of the autonomous swarm that is observable to the navigator agents. To this end, we propose a three-dimensional guidance control law based on behavior prediction of autonomous agents under motion constraints, inspired by the Dynamic Window Approach (DWA). At each control cycle, the navigator agent generates a set of feasible motion candidates that satisfy its motion constraints, and predicts the short-horizon swarm evolution using an internal model of the autonomous agents maintained within the navigator agent. The motion candidates are then evaluated according to criteria such as the progress velocity toward the target, the positioning strategy with respect to the swarm, and safety margins, and the optimal motion is selected to achieve safe and efficient guidance. Numerical simulation results demonstrate the effectiveness of the proposed guidance control law.

This paper addresses the problem of traffic congestion management in fixed-wing unmanned aerial vehicle (UAV) corridors by further developing a recently introduced loiter-lane framework. A semi-cooperative guidance strategy is developed for inserting fixed-wing UAVs into a loiter lane with minimal disruption to the UAVs already operating within it, while enabling a more compact fixed-wing UAV corridor. Building on the concepts of cooperative and non-disruptive loiter-lane insertion, the proposed strategy makes the incoming UAV first attempt, within its speed bounds, to rendezvous with an existing empty loiter slot. If direct insertion is infeasible, a minimal number of loitering UAVs perform coordinated slot hopping to create a suitably positioned empty slot. The feasibility and performance of the method are demonstrated through numerical simulations.