Aerial robots can enhance their safe and agile navigation in complex and cluttered environments by efficiently exploiting the information collected during a given task. In this paper, we address the learning model predictive control problem for quadrotors. We design a learning receding--horizon nonlinear control strategy directly formulated on the system nonlinear manifold configuration space SO(3)xR^3. The proposed approach exploits past successful task iterations to improve the system performance over time while respecting system dynamics and actuator constraints. We further relax its computational complexity making it compatible with real-time quadrotor control requirements. We show the effectiveness of the proposed approach in learning a minimum time control task, respecting dynamics, actuators, and environment constraints. Several experiments in simulation and real-world set-up validate the proposed approach.
翻译:空中机器人可以通过有效利用在特定任务中收集的信息,在复杂和杂乱的环境中加强其安全和机动的导航。本文述及对四甲状腺的学习模型预测控制问题。我们设计了在系统非线性多元配置空间SO(3)xR}3.拟议办法利用过去成功的任务迭代来改进系统的长期性能,同时尊重系统动态和动作器限制。我们进一步放宽其计算复杂性,使之符合实时二次钻探控制要求。我们展示了拟议办法在学习最低限度时间控制任务、尊重动态、操作器和环境限制方面的有效性。模拟和现实世界设置的若干试验证实了拟议办法。