Dual-quaternion learning control for autonomous vehicle trajectory tracking with safety guarantees
Universidad Politécnica de Madrid（西班牙马德里理工大学）；School of Science and Technology, IE University（IE大学科学与技术学院）；Universidad de San Andrés (UdeSA) and CONICET（阿根廷圣安德烈斯大学（UdeSA）和CONICET）；Centre for Automation and Robotics (CSIC-UPM)（自动化与机器人中心（CSIC-UPM））
Omayra Yago Nieto, Alexandre Anahory Simoes, Juan I. Giribet, Leonardo Colombo
We propose a learning-based trajectory tracking controller for autonomous robotic platforms whose motion can be described kinematically on $\mathrm{SE}(3)$. The controller is formulated in the dual quaternion framework and operates at the velocity level, assuming direct command of angular and linear velocities, as is standard in many aerial vehicles and omnidirectional mobile robots. Gaussian Process (GP) regression is integrated into a geometric feedback law to learn and compensate online for unknown, state-dependent disturbances and modeling imperfections affecting both attitude and position, while preserving the algebraic structure and coupling properties inherent to rigid-body motion.
The proposed approach does not rely on explicit parametric models of the unknown effects, making it well-suited for robotic systems subject to sensor-induced disturbances, unmodeled actuation couplings, and environmental uncertainties. A Lyapunov-based analysis establishes probabilistic ultimate boundedness of the pose tracking error under bounded GP uncertainty, providing formal stability guarantees for the learning-based controller.
Simulation results demonstrate accurate and smooth trajectory tracking in the presence of realistic, localized disturbances, including correlated rotational and translational effects arising from magnetometer perturbations. These results illustrate the potential of combining geometric modeling and probabilistic learning to achieve robust, data-efficient pose control for autonomous robotic systems.
Dual Quaternion Control of UAVs with Cable-suspended Load
Technical University of Munich（慕尼黑工业大学）；School of Engineering and Design（工程与设计学院）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）；Autonomous Aerial Systems（自主航空系统（机构））
Safe and Real-Time Consistent Planning for Autonomous Vehicles in Partially Observed Environments via Parallel Consensus Optimization
Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）机器人与自主系统方向）；School of Engineering, Great Bay University（大湾大学工程学院）；Division of Emerging Interdisciplinary Areas, The Hong Kong University of Science and Technology（香港科学与技术大学新兴交叉领域学院）
Improving Autonomous Driving Safety with POP: A Framework for Accurate Partially Observed Trajectory Predictions
The Hong Kong University of Science and Technology（香港科技大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳产学研合作研究院）；Lotus Technology Ltd（路特斯科技有限公司）
Safety-Oriented Trajectory Ranking from BEV Perception Outputs
University of the Bundeswehr Munich（德国联邦国防军慕尼黑大学）；Ludwig-Maximilians-Universität München（慕尼黑大学）；Research Institute CODE, University of the Bundeswehr Munich（德国联邦国防军慕尼黑大学CODE研究院）
A Semi-decentralized and Variational-Equilibrium-Based Trajectory Planner for Connected and Autonomous Vehicles
Tongji University（同济大学）；The Shanghai Research Institute for Intelligent Autonomous Systems（上海智能自主系统研究院）；Shanghai Institute of Intelligent Science and Technology, Tongji University（同济大学上海智能科学技术研究院）
Overcoming Blind Spots: Occlusion Considerations for Improved Autonomous Driving Safety
Technical University of Munich（慕尼黑工业大学）；TUM School of Engineering and Design（慕尼黑工业大学工程与设计学院）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）；Institute of Automotive Technology（汽车技术研究所）；Tier IV（Tier IV公司）
Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving
University of Southern California（南加州大学）；Virginia Tech（弗吉尼亚理工大学）；Miami University（迈阿密大学）；Northern Arizona University（北亚利桑那大学）；Northeastern University（东北大学）
Reachability-based Trajectory Safeguard (RTS): A Safe and Fast Reinforcement Learning Safety Layer for Continuous Control
School of Mechanical Engineering, University of Michigan（密歇根大学机械工程学院）；Robotics Institute, University of Michigan（密歇根大学机器人研究所）；Stanford University（斯坦福大学）
Real-Time Parallel Trajectory Optimization with Spatiotemporal Safety Constraints for Autonomous Driving in Congested Traffic
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Monash University（莫纳什大学）；The Hong Kong University of Science and Technology（香港科技大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（港科大深港协同创新研究院）
Robust H-infinity kinematic control of manipulator robots using dual quaternion algebra
Munich School of Robotics and Machine Intelligence, Technische Universität München（慕尼黑工业大学慕尼黑机器人与机器智能学院）；The University of Manchester（曼彻斯特大学）；University of Brasília（巴西利亚大学）
Safety Evaluation of Motion Plans Using Trajectory Predictors as Forward Reachable Set Estimators
Department of Electrical Engineering, University of Southern California（电气工程系，美国南加州大学）；Department of Aeronautics and Astronautics, Stanford University（航空与宇航系，斯坦福大学）；NVIDIA Research（NVIDIA研究）
RTD-RAX: Fast, Safe Trajectory Planning for Systems under Unknown Disturbances
Georgia Institute of Technology（佐治亚理工学院）；School of Electrical and Computer Engineering（电子与计算机工程学院）；School of Mechanical Engineering（机械工程学院）；School of Civil and Environmental Engineering（土木与环境工程学院）
Automatic driving lane change safety prediction model based on LSTM
Trine University（特莱恩大学）；Yantai University（烟台大学）；University of Electronic Science and Technology of China（电子科技大学）；Northern Arizona University（北亚利桑那大学）；University of Aberdeen（阿伯丁大学）
Dynamic High-Order Control Barrier Functions with Diffuser for Safety-Critical Trajectory Planning at Signal-Free Intersections
The Hong Kong Polytechnic University（香港理工大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Washington（华盛顿大学）；Southeast University（东南大学）
