高效避障控制框架，让无人机在三维空间中灵活避开椭球障碍物。
Efficient Collision-Avoidance Constraints for Ellipsoidal Obstacles in Optimal Control: Application to Path-Following MPC and UAVs
- 基于连续可微的碰撞检测条件，实现高效椭球体避障
- 通过两阶段优化解决数值不稳定性问题，提升求解可靠性
- 首次在真实无人机上验证了此类模型预测控制算法
本文提出一种模块化的最优控制框架，用于局部三维椭球障碍物避障，以模型预测路径跟随控制为例。考虑静态和动态障碍物。核心是计算高效且连续可微的椭球障碍物碰撞检测条件。一种新颖的两阶段优化方法缓解了由此产生的最优控制问题结构带来的数值问题。通过仿真和使用Crazyflie四旋翼无人机的真实实验验证了该方法的有效性。这是首个在真实无人机上实现此类模型预测控制器的三维任务硬件演示。
原文摘要 · Abstract (English)
This article proposes a modular optimal control framework for local three-dimensional ellipsoidal obstacle avoidance, exemplarily applied to model predictive path-following control. Static as well as moving obstacles are considered. Central to the approach is a computationally efficient and continuously differentiable condition for detecting collisions with ellipsoidal obstacles. A novel two-stage optimization approach mitigates numerical issues arising from the structure of the resulting optimal control problem. The effectiveness of the approach is demonstrated through simulations and real-world experiments with the Crazyflie quadrotor. This represents the first hardware demonstration of an MPC controller of this kind for UAVs in a three-dimensional task.
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