PolyFly: Polytopic Optimal Planning for Collision-Free Cable-Suspended Aerial Payload Transportation

2025-10-16

AI Summary

Existing trajectory planning methods for cable-suspended drones operating in confined environments (e.g., dense forests, collapsed buildings) rely on conservative geometric approximations of the vehicle and obstacles, resulting in redundant trajectories and increased flight time. Method: We propose a non-conservative, globally optimal trajectory planning framework: (i) modeling drone components and environmental obstacles as orientation-aware polyhedra to precisely capture pose-dependent geometric relationships; and (ii) leveraging duality theory to reformulate non-smooth collision constraints into smooth, differentiable forms, enabling high-fidelity trajectory optimization. Results: Experiments across eight maze-like environments demonstrate consistently shorter, collision-free trajectories compared to state-of-the-art baselines. The method is validated on a real quadrotor platform with suspended payload, achieving both computational efficiency (sub-second planning) and engineering robustness under dynamic disturbances and sensor uncertainty.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Constraint Satisfaction and Optimization: Constraint Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 environments, such as dense forests and structurally unsafe buildings, 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 transportation by modeling each physical component of the environment, and the robot (quadrotor, cable and payload), as independent polytopes. We further increase the model accuracy by incorporating the attitude of the physical components by constructing orientation-aware polytopes. The resulting optimal control problem 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.

Problem

• Optimizes collision-free trajectory for cable-suspended aerial payload transport
• Overcomes conservative geometric approximations in existing obstacle avoidance methods
• Models robot components as polytopes for accurate orientation-aware planning

Innovation

• Models robot components as independent polytopes
• Uses orientation-aware polytopes for accuracy
• Solves polytope constraints via duality theory