PolyFly: Polytopic Optimal Planning for Collision-Free Cable-Suspended Aerial Payload Transportation
Mrunal Sarvaiya, Guanrui Li, Giuseppe Loianno
AI summary
Problem
Existing planners over-approximate cable-suspended aerial systems using spheres or single prisms, resulting in conservative maneuvers and longer flight times in cluttered spaces.
Approach
PolyFly represents the quadrotor, cable, and payload as distinct polytopes and uses duality theory to convert non-linear collision constraints into smooth, differentiable forms for optimal global planning.
Key results
- Faster trajectories across all tested maze-like environments compared to state-of-the-art
- Successful navigation through narrow gaps just wider than the cable width
- Real-world hardware validation demonstrating tracking accuracy and reliability
- Open-source implementation released for community use
Why it matters
Enables safer and more efficient aerial payload delivery for disaster response and logistics by overcoming geometric conservatism in existing planners.
Abstract
Aerial transportation robots using suspended cables have emerged as versatile platforms for disaster response and rescue operations. To maximize the capabilities of these systems, robots need to aggressively fly through tightly constrained environ- ments,suchasdenseforestsandstructurallyunsafebuildings,while minimizing flight time and avoiding obstacles. Existing methods geometrically over-approximate the vehicle and obstacles, leading to conservative maneuvers and increased flight times. We eliminate these restrictions by proposing PolyFly, an optimal global planner which considers a non-conservative representation for aerial trans- portation by modeling each physical component of the environ- ment, and the robot (quadrotor, cable and payload), as independent polytopes. We further increase the model accuracy by incorpo- rating the attitude of the physical components by constructing orientation-aware polytopes. The resulting optimal control prob- lem is efficiently solved by converting the polytope constraints into smooth differentiable constraints via duality theory. We compare our method against the existing state-of-the-art approach in eight maze-like environments and show that PolyFly produces faster trajectories in each scenario. We also experimentally validate our proposed approach on a real quadrotor with a suspended payload, demonstrating the practical reliability and accuracy of our method.