基于SQP的多无人机负载运输电缆张力分配
SQP-Based Cable-Tension Allocation for Multi-Drone Load Transport
AI总结：
本文提出基于SQP算法的多无人机负载运输电缆张力分配方法，通过实时优化提升负载运输的安全性和能耗平衡。
摘要：
多智能体空中负载运输系统（MAATS）相比单机解决方案具有更大的载重能力和更高的容错性。然而，它们存在一个欠定的张力分配问题，导致能量分布不均、电缆松弛或无人机与电缆之间的碰撞。本文提出了一层实时优化层，通过引入顺序二次规划（SQP）算法来改进分层的负载-位置-姿态控制器。SQP公式最小化电缆张力的平方和，同时施加一个电缆对齐惩罚项，以防止小的电缆夹角，从而在不改变参考轨迹的情况下防止电缆收敛。我们通过运行四架四旋翼无人机的数值模拟，在名义条件下测试了该方法。基于数值模拟的计算实验表明，SQP程序在标准硬件上可在几毫秒内运行，表明其适用于实时应用。敏感性分析证实，电缆对齐惩罚项的增益可以在线调节，从而在不降低模拟跟踪性能的情况下，实现安全裕度和能耗之间的可控权衡。该框架为实际部署中安全且能量平衡的协同负载运输提供了可扩展的路径。
英文摘要：
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.
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