Iterative Learning-Based Admittance Control for Autonomous Excavation

Iterative Autonomous Excavation

This paper introduces a Cartesian impedance control framework in which reaction forces exceeding control authority directly reshape bucket motion during successive excavation passes. This novel approach to excavation results in an iterative process that does not require explicit prediction of terrain forces. This is in contrast to most excavation control approaches that are based on the generat...

Estimation-based iterative learning control

i i i i i i Cover illustration: ILC applied to a problem where, for example, a robot tool is supposed to track a circular path. In the beginning, the tracking performance is poor, but as the ILC algorithm " learns " , the performance improves and comes very close to a perfect circle. The orange colour represents the connection to the experiments performed on ABB robots. Linköping studies in sci...

Simulation of the Admittance Control Based Haptic Interface Device

Autonomous Multirobot Excavation for Lunar Applications

In this paper, a control approach called Artificial Neural Tissue (ANT) is applied to multirobot excavation for lunar base preparation tasks including clearing landing pads and burying of habitat modules. We show for the first time, a team of autonomous robots excavating a terrain to match a given 3D blueprint. Constructing mounds around landing pads will provide physical shielding from debris ...

Bilateral Teleoperation Systems Using Backtracking Search optimization Algorithm Based Iterative Learning Control

This paper deals with the application of Iterative Learning Control (ILC) to further improve the performance of teleoperation systems based on Smith predictor. The goal is to achieve robust stability and optimal transparency for these systems. The proposed control structure make the slave manipulator follow the master in spite of uncertainties in time delay in communication channel and model pa...