A Hybrid Approach for Trajectory Control Design
This addresses trajectory control for robotics by avoiding complex terramechanics analysis, but it appears incremental as it combines existing methods.
The authors tackled the problem of trajectory tracking control by embedding non-parametric statistical models like Gaussian Processes to minimize unmodeled dynamics such as slippage, and experiments in real and simulated environments showed the methodology is promising.
This work presents a methodology to design trajectory tracking feedback control laws, which embed non-parametric statistical models, such as Gaussian Processes (GPs). The aim is to minimize unmodeled dynamics such as undesired slippages. The proposed approach has the benefit of avoiding complex terramechanics analysis to directly estimate from data the robot dynamics on a wide class of trajectories. Experiments in both real and simulated environments prove that the proposed methodology is promising.