Gestelt: A Framework for Accelerating the Sim-To-Real Transition for Swarm UAVs
John Tan, Tianchen Sun, Lin Ping Feng, Rodney Teo, Boo Cheong Khoo · 2024
Research in aerial swarms have gained traction in recent years and there appears to be a lack of user-friendly frameworks with a focus on bringing swarm UAVs from simulation to actual flight. Furthermore, spatial constraints and resource challenges hinder the validation of larger-scale swarm algorithms. To tackle these issues, we propose Gestelt, a relatively lightweight framework that accelerates the sim-to-real transition for swarm algorithms. First, the modular design of Gestelt is highlighted to illustrate it's generalization to multiple types of planning algorithms and paradigms. Next, we outline an approach to model any given quadrotor platform for use in our framework's simulation environment. Another unique feature of Gestelt is it's virtual-physical environment, which can simultaneously host both virtual and physical agents, thereby providing an intermediate platform for testing larger-scale swarm algorithms safely. Finally, we implement an asymptotically stable closed-loop control technique known as Robust Perfect Tracking (RPT) to track the reference trajectories of the swarm agents in the face of disturbances. We demonstrate our framework through physical experiments featuring our custom swarm platform, the NUSwarm drone, where we show a swarm navigation scenario for 3 physical and 3 virtual drones. A video demonstrating the virtual-physical environment can be found at https://youtu.be/FY1wz2yZxLE