4 accepted papers
Predicting lane change maneuvers is essential for ensuring safe autonomous driving, especially in complex urban environments. Building upon prior multi-modal and graph-based approaches, this work introduces a novel transformer-based architecture for multi-horizon lane change prediction that jointly …
A novel deep neural network (DNN) based controller for omnidirectional robots is proposed. The controller decomposes the prescribed reference path, corresponding to a fixed prediction horizon, into multiple paths of shorter horizons. This implicitly enforces a Hankel structure in the input and conse…
We suggest a procedure for dynamic parameter estimation of serial robot manipulators. Its basic idea relies on the synthesis of an optimal manipulation trajectory, which is based on properly introduced parameter aggregates to ensure a collection of numerically well-conditioned data-sets, yielding an…
A hierarchical algorithm involving two-layer optimization-based control policies with varying degrees of abstraction is proposed, including upper layer task scheduling and lower layer local path planning. A scenario with two robot arms performing cooperative pick-and-place tasks for moving objects i…