Multi drone teams balance cable forces for smoother payload flying
AFD-CAMLs: Agile Force-Distribution-Aware Planning and Control for Cable-Suspended Aerial Multi-Lifting Systems
Robotics
Summary
Carrying big, heavy things with multiple flying drones is tricky because the cables holding the load can pull unevenly, making the drones work harder or fail. The authors developed a new way to plan and control drone movements that keeps the cables' tension balanced, even during tricky or fast moves. They designed a system that plans the load's path while adjusting the forces each drone must handle, and then makes on-the-fly corrections when real conditions differ from the plan. Tests with four to ten drones showed their method keeps cable tensions more even without slowing down or losing control.
What this means in practice
- •For drone fleet operators: Coordinate multiple drones to carry heavy loads more safely by ensuring cables share forces evenly during agile maneuvers.
- •For industrial logistics teams: Improve aerial transport of goods by reducing cable stress that can cause damage or delivery delays in multi-drone setups.
Authors
Antreas Kourris, Sihao Sun
Abstract
Multiple UAVs can cooperatively transport heavy payloads while controlling their position and orientation. Trajectory-based methods offer high agility while satisfying system constraints, but can produce uneven force distributions when the tension-to-wrench allocation is redundant or ill-conditioned, particularly under geometric mismatch and low-level tracking errors. We propose a hybrid planning-and-control framework to address this problem. A global planner generates payload trajectories and cable-force references by exploring the allocation null space under a prescribed internal-force setting. These references augment the cost of a centralized local planner, promoting feasible force distributions while generating trajectories for all UAVs. An admittance filter then compares the planned forces with onboard cable-tension estimates and adjusts the kinematic references to improve force tracking in degenerate or near-degenerate configurations. Simulations and experiments involving four to ten UAVs demonstrate more balanced tension distributions during both hovering and demanding agile maneuvers, without compromising agility or payload-tracking performance.