用改进的控制方法让无人机群在视野有限下安全避障
Decentralized Nonlinear Model Predictive Control for Safe Collision Avoidance in Quadrotor Teams with Limited Detection Range
- 用指数控制屏障函数增强多无人机系统的安全性和最优性
- 在10架无人机20个障碍物仿真中验证了避障有效性
- 适合研究无人机协同控制或安全飞行的开发者
多旋翼飞行器系统在去中心化控制中面临显著挑战，尤其是在感知与通信受限条件下的安全与协调问题。现有方法虽利用控制屏障函数（CBFs）提供安全保证，但常忽略执行器约束和有限探测范围。为此，本文提出一种新型去中心化非线性模型预测控制（NMPC），集成指数控制屏障函数（ECBFs）以提升多旋翼系统的安全性和最优性。我们推导出保持ECBF安全保证的保守与实用最小探测范围边界。通过高达10架无人机与20个障碍物的大量仿真及3架无人机的真实实验验证了该方法在真实场景中的有效性，展示了其在可靠无人机集群作业中的潜力。
原文摘要 · Abstract (English)
Multi-quadrotor systems face significant challenges in decentralized control, particularly with safety and coordination under sensing and communication limitations. State-of-the-art methods leverage Control Barrier Functions (CBFs) to provide safety guarantees but often neglect actuation constraints and limited detection range. To address these gaps, we propose a novel decentralized Nonlinear Model Predictive Control (NMPC) that integrates Exponential CBFs (ECBFs) to enhance safety and optimality in multi-quadrotor systems. We provide both conservative and practical minimum bounds of the range that preserve the safety guarantees of the ECBFs. We validate our approach through extensive simulations with up to 10 quadrotors and 20 obstacles, as well as real-world experiments with 3 quadrotors. Results demonstrate the effectiveness of the proposed framework in realistic settings, highlighting its potential for reliable quadrotor teams operations.
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