New paper in Nature Scientific Report on “Residual dynamics learning for trajectory tracking for multi-rotor aerial vehicles”

L-CAS colleagues Geesara Kulathunga and Alexandr Klimchik have published their latest research in the prestigious Nature Scientific Reports Journal.

This paper presents a technique to model the residual dynamics between a high-level planner and a low-level controller by considering reference trajectory tracking in a cluttered environment as an example scenario. We focus on minimising residual dynamics that arise due to only the kinematical modelling of high-level planning. The kinematical modelling is sufficient for such scenarios due to safety constraints and aggressive manoeuvres that are difficult to perform when the environment is cluttered and dynamic. We used a simplified motion model to represent the motion of the quadrotor when formulating the high-level planner. The Sparse Gaussian Process Regression-based technique is proposed to model the residual dynamics. Recently proposed Data-Driven MPC is targeting aggressive manoeuvres without considering obstacle constraints. The proposed technique is compared with Data-Driven MPC to estimate the residual dynamics error without considering obstacle constraints. The comparison results yield that the proposed technique helps to reduce the nominal model error by a factor of 2 on average. Further, the proposed complete framework was compared with four other trajectory-tracking approaches in terms of tracking the reference trajectory without colliding with obstacles. The proposed approach outperformed the others with less flight time without losing computational efficiency.

cite as: Kulathunga, G., Hamed, H. & Klimchik, A. Residual dynamics learning for trajectory tracking for multi-rotor aerial vehicles. Sci Rep 14, 1858 (2024).