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

TL;DR

This paper introduces a real-time SQP-based optimization method to improve tension distribution and safety in multi-drone load transport systems, enhancing energy efficiency and collision avoidance.

Contribution

It presents a novel SQP-based tension allocation algorithm integrated into a hierarchical controller for multi-drone systems, enabling real-time, safe, and energy-efficient load transport.

Findings

SQP routine runs in milliseconds on standard hardware.

The method improves tension distribution and safety margins.

Tuning the cable-alignment penalty balances safety and energy use.

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.