SQP-Based Cable-Tension Allocation for Multi-Drone Load Transport

TL;DR

The paper tackles the challenge of underdetermined tension allocation in multi-UAV payload transport (MAATS), which can cause energy imbalance and safety risks. It introduces a real-time Sequential Quadratic Programming (SQP) based tension allocator as an optimization layer within a four-layer hierarchical MAATS controller, aiming to minimize $J = \tfrac{1}{2}\sum_{i=1}^n T_{id}^2 + \mu \sum_{i<j}(\alpha_{id}^T \alpha_{jd})^2$ under the constraint $\sum_{i=1}^n T_{id} \alpha_{id} = -u_L$ with $T_{id} \ge 0$ and $\|\alpha_{id}\| = 1$. The approach yields energy-balanced tensions and maintains safe inter-cable angles while preserving centimeter-level payload tracking, with real-time solver times on standard hardware and tunable safety-energy trade-offs via the alignment weight $\mu$. The framework is scalable to larger teams and does not impose a fixed geometric formation, though it currently relies on a centralized optimizer and is based on simulation validation. Future work includes hardware-in-the-loop testing, distributed optimization to remove a single point of failure, and extending the payload model to richer dynamics.

Abstract

Multi-Agent Aerial Load Transport Systems (MAATS) offer greater payload capacity and fault tolerance than single-drone solutions. However, they have an underdetermined tension allocation problem that leads to uneven energy distribution, cable slack, or collisions between drones and cables. This paper presents a real-time optimization layer that improves a hierarchical load-position-attitude controller by incorporating a Sequential Quadratic Programming (SQP) algorithm. The SQP formulation minimizes the sum of squared cable tensions while imposing a cable-alignment penalty that discourages small inter-cable angles, thereby preventing tether convergence without altering the reference trajectory. We tested the method under nominal conditions by running numerical simulations of four quadrotors. Computational experiments based on numerical simulations demonstrate that the SQP routine runs in a few milliseconds on standard hardware, indicating feasibility for real-time use. A sensitivity analysis confirms that the gain of the cable-alignment penalty can be tuned online, enabling a controllable trade-off between safety margin and energy consumption with no measurable degradation of tracking performance in simulation. This framework provides a scalable path to safe and energy-balanced cooperative load transport in practical deployments.

Figures (8)

• Figure 1: Multi-Agent Aerial Transportation System (MAATS). Nomenclature and reference frames for an n-UAV MAATS.
• Figure 2: Block Diagram of the Control Strategy in a four-layer cascade.
• Figure 3: Three-dimensional view of the four-UAV system transporting the payload during the spiral trajectory. The SQP allocator maintains safe cable angles while ensuring balanced tension distribution.
• Figure 4: Payload trajectory tracking performance during the ascending spiral mission. The SQP-based controller maintains tight tracking with RMS error of 2.97cm.
• Figure 5: Cable tension profiles comparison. The SQP allocator (b) achieves significantly more balanced force distribution compared to the baseline geometric pattern approach (a).