6 accepted papers
We propose a novel real-time excavation trajectory modulation framework on a slope for an autonomous excavator with a low-level digital kinematic control as common for hydraulic industrial excavators. Excavation on a slope is challenging because of a higher risk of slips and rollovers. To deal with
Recent advances in Reinforcement Learning (RL) have demonstrated promising results in autonomous car racing. However, two fundamental challenges remain: sparse rewards, which hinder efficient learning process, and the quality of demonstrations, which directly affects the effectiveness of RL from Dem
In this paper, we introduce time-correlated model predictive path integral (TC-MPPI), a novel approach to mitigate action noise in sampling-based control methods. Unlike conventional smoothing techniques that rely on post-processing or additional state variables, TC-MPPI directly incorporates tempor
In this paper, we propose a real-time clothoid tree-based path planning for self-driving robots. Clothoids, curves that exhibit linear curvature profiles, play an important role in road design and path planning due to their appealing properties. Nevertheless, their real-time applications face consid…
This work proposes a novel precision motion control framework of robotized industrial hydraulic excavators via data-driven model inversion. Rather than employing a single neural network to approximate the whole excavator dynamics, including input delays and dead-zones, we construct a physics-inspire
We propose a novel distributed control framework for teleoperated platooning of multiple three-dimensional (3D) fixed-wing unmanned aerial vehicles (UAVs), consisting of the following two layers: 1) virtual frame layer, which generates the target 3D nonholonomic motion of the virtual nonholonomic fr