This paper presents a technique to cope with the gap between high-level planning, e.g., reference trajectory tracking, and low-level controlling using a learning-based method in the plan-based control paradigm. The technique improves the smoothness of maneuvering through cluttered environments, especially targeting low-speed velocity profiles. In such a profile, external aerodynamic effects that are applied on the quadrotor can be neglected. Hence, we used a simplified motion model to represent the motion of the quadrotor when formulating the Nonlinear Model Predictive Control (NMPC)-based local planner. However, the simplified motion model causes residual dynamics between the high-level planner and the low-level controller. The Sparse Gaussian Process Regression-based technique is proposed to reduce these residual dynamics. The proposed technique is compared with Data-Driven MPC. The comparison results yield that an augmented residual dynamics model-based planner helps to reduce the nominal model error by a factor of 2 on average. Further, we compared the proposed complete framework with four other approaches. The proposed approach outperformed the others in terms of tracking the reference trajectory without colliding with obstacles with less flight time without losing computational efficiency.

翻译：本文提出了一种技术，用于在基于规划的控制框架中，通过基于学习的方法弥合高层规划（如参考轨迹跟踪）与底层控制之间的差距。该技术提升了在复杂环境中机动操作的平滑性，尤其针对低速速度剖面。在此类剖面中，施加于四旋翼飞行器的外部空气动力学效应可忽略不计。因此，我们在构建基于非线性模型预测控制（NMPC）的局部规划器时，采用简化运动模型表示四旋翼飞行器的运动。然而，简化运动模型会导致高层规划器与底层控制器之间存在残差动力学。本文提出基于稀疏高斯过程回归的技术来减小这些残差动力学。该技术与数据驱动MPC进行了比较。比较结果表明，基于增强残差动力学模型的规划器平均能将标称模型误差降低两倍。此外，我们将所提出的完整框架与其他四种方法进行了对比。所提方法在跟踪参考轨迹、避免与障碍物碰撞方面表现更优，且飞行时间更短，同时未降低计算效率。