To build commercial robots, skid-steering mechanical design is of increased popularity due to its manufacturing simplicity and unique mechanism. However, these also cause significant challenges on software and algorithm design, especially for the pose estimation (i.e., determining the robot's rotation and position) of skid-steering robots, since they change their orientation with an inevitable skid. To tackle this problem, we propose a probabilistic sliding-window estimator dedicated to skid-steering robots, using measurements from a monocular camera, the wheel encoders, and optionally an inertial measurement unit (IMU). Specifically, we explicitly model the kinematics of skid-steering robots by both track instantaneous centers of rotation (ICRs) and correction factors, which are capable of compensating for the complexity of track-to-terrain interaction, the imperfectness of mechanical design, terrain conditions and smoothness, etc. To prevent performance reduction in robots' long-term missions, the time- and location- varying kinematic parameters are estimated online along with pose estimation states in a tightly-coupled manner. More importantly, we conduct in-depth observability analysis for different sensors and design configurations in this paper, which provides us with theoretical tools in making the correct choice when building real commercial robots. In our experiments, we validate the proposed method by both simulation tests and real-world experiments, which demonstrate that our method outperforms competing methods by wide margins.
Tightly-Coupled LiDAR-IMU-Wheel Odometry with an Online Neural Kinematic Model Learning via Factor Graph Optimization
The Space Robotics Lab. in the Department of Aerospace Engineering, Graduate School of Engineering, Tohoku University（东京大学工学研究生院航空航天工程系空间机器人实验室）；National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究院）
Dynamics and multi-stability of a rotor-actuated Twistcar robot with passive steering joint
Faculty of Mechanical Engineering, Technion - Israel Institute of Technology, Haifa 3200003, Israel（机械工程系，以色列技术学院）；Deptartment of Aerospace and Mechanical Engineering, University of Southern California, USA（航空航天与机械工程系，南加州大学）
The Geometry of Optimal Gait Families for Steering Kinematic Locomoting Systems
Collaborative Robotics and Intelligent Systems (CoRIS) Institute at Oregon State University（俄勒冈州立大学协作机器人与智能系统（CoRIS）研究所）；Johns Hopkins University（约翰斯·霍普金斯大学）
EmbodiSteer: Steering Embodiment-Agnostic Visuomotor Policies with Joint-Space Guidance for Zero-Shot Cross-Embodiment Deployment
Department of Automation, Tsinghua University（清华大学自动化系）；Beijing Key Laboratory of Embodied Intelligence Systems（北京具身智能系统重点实验室）；Institute for Embodied Intelligence and Robotics, Tsinghua University（清华大学具身智能与机器人研究所）
Towards Safe Maneuvering of Double-Ackermann-Steering Robots with a Soft Actor-Critic Framework
Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800（波尔多大学、国家科学研究中心、波尔多国立理工学院、LaBRI、UMR 5800）；School of Computer Science, University of Nottingham, UK（nottingham大学计算机科学学院）；Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza（阿拉贡工程研究所（I3A）、 Zaragoza大学）
Nonholonomic Narrow Dead-End Escape with Deep Reinforcement Learning
ZJUI Institute, International Campus\ University Haining China；Tianjin University Tianjin China；Beijing Jiaotong University Beijing China；University of Nottingham Ningbo China Ningbo China；ZJUI Institute, International Campus\ University；Tianjin University；Beijing Jiaotong University；University of Nottingham Ningbo China
Multi-AUV Kinematic Task Assignment based on Self-organizing Map Neural Network and Dubins Path Generator
College of Information Engineering, Shanghai Maritime University（上海海事大学信息工程学院）；Logistics Engineering College, Shanghai Maritime University（上海海事大学物流工程学院）；Mechanics Institute, University of Shanghai for Science and Technology（上海理工大学力学研究所）
