Heuristic-Based Trajectory Optimization for Drone Flocking with Obstacle Avoidance

The flocking behavior of aerial robots in a cluttered environment is often associated with factors such as limited communication range, energy constraints, and information security among multiple Unmanned Aerial Vehicles (UAVs). To overcome these challenges, the development of efficient trajectories is vital. In this study, a heuristic-based flocking approach is employed to determine the safest and shortest trajectories among the UA Vs for achieving a target point while avoiding obstacles. For this purpose, first, the Genetic optimization algorithm is applied to find the best parameters of the decentralized flocking guidance algorithm. Consequently, the appropriate velocity commands are generated for all UA Vs. To accurately track these commands, a nonlinear swarm control algorithm based on the sliding mode technique is utilized for each drone. The results demonstrate that the proposed flocking guidance method provides effective performance to ensure non-collision among the UA Vs and the minimum time to reach the target during the flight.