activity
20162023
most citedFinding the Right Place: Sensor Placement for UWB Time Difference of Arrival Localization in Cluttered Indoor Environments

61 citations · 184 across the 45 of their papers we have counts for

collaborators
Showing 2017Show all

12 papers · 1 filter

cs.RO2017

Model Predictive Path-Following for Constrained Differentially Flat Systems

Melissa Greeff, Angela P. Schoellig

For many tasks, predictive path-following control can significantly improve the performance and robustness of autonomous robots over traditional trajectory tracking control. It doe…

cs.RO2017★ 7 cited

Learning of Coordination Policies for Robotic Swarms

Qiyang Li, Xintong Du, Yizhou Huang +2

Inspired by biological swarms, robotic swarms are envisioned to solve real-world problems that are difficult for individual agents. Biological swarms can achieve collective intelli…

cs.RO2017

Data-Efficient Multirobot, Multitask Transfer Learning for Trajectory Tracking

Karime Pereida, Mohamed K. Helwa, Angela P. Schoellig

Transfer learning has the potential to reduce the burden of data collection and to decrease the unavoidable risks of the training phase. In this letter, we introduce a multirobot,…

cs.RO2017

An Inversion-Based Learning Approach for Improving Impromptu Trajectory Tracking of Robots with Non-Minimum Phase Dynamics

Siqi Zhou, Mohamed K. Helwa, Angela P. Schoellig

This paper presents a learning-based approach for impromptu trajectory tracking for non-minimum phase systems, i.e., systems with unstable inverse dynamics. Inversion-based feedfor…

cs.RO2017★ 8 cited

Aerial Rock Fragmentation Analysis in Low-Light Condition Using UAV Technology

Thomas Bamford, Kamran Esmaeili, Angela P. Schoellig

In recent years, Unmanned Aerial Vehicle (UAV) technology has been introduced into the mining industry to conduct terrain surveying. This work investigates the application of UAVs…

cs.RO2017

Multi-Robot Transfer Learning: A Dynamical System Perspective

Mohamed K. Helwa, Angela P. Schoellig

Multi-robot transfer learning allows a robot to use data generated by a second, similar robot to improve its own behavior. The potential advantages are reducing the time of trainin…