Path Planning for a UAV Swarm Using Formation Teaching-Learning-Based Optimization
Van Truong Hoang, Manh Duong Phung
TL;DR
The paper tackles multi-UAV formation path planning by formulating a centroid-based optimization problem that jointly minimizes path length, safety violations, and task performance via a multi-objective fitness $Φ(q) = αΦ_{length}(q) + βΦ_{safe}(q) + γΦ_{task}(q)$. It introduces formation TLBO (FTLBO), which augments the standard TLBO with mutation, elitism, and multi-subject learning to efficiently search the 3D space and avoid local minima, ensuring the UAVs maintain a triangular formation around the centroid while avoiding cylindrical obstacles. Through simulations and real UAV tests, the method achieves obstacle-free centroid paths with stable formation, outperforming GA, TLBO, and θ-PSO in convergence and solution quality. The approach demonstrates practical applicability for coordinated surveys (e.g., orchard monitoring) by producing feasible, formation-preserving trajectories that can be executed with off-the-shelf UAV platforms.
Abstract
This work addresses the path planning problem for a group of unmanned aerial vehicles (UAVs) to maintain a desired formation during operation. Our approach formulates the problem as an optimization task by defining a set of fitness functions that not only ensure the formation but also include constraints for optimal and safe UAV operation. To optimize the fitness function and obtain a suboptimal path, we employ the teaching-learning-based optimization algorithm and then further enhance it with mechanisms such as mutation, elite strategy, and multi-subject combination. A number of simulations and experiments have been conducted to evaluate the proposed method. The results demonstrate that the algorithm successfully generates valid paths for the UAVs to fly in a triangular formation for an inspection task.