Physics-Guided Reinforcement Learning System for Realistic Vehicle Active Suspension Control
University of Maryland（马里兰大学）；University of Michigan（密歇根大学）
Anh N. Nhu, Ngoc-Anh Le, Shihang Li, Thang D. V. Truong
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The suspension system is a crucial part of the automotive chassis, improving vehicle ride comfort and isolating passengers from rough road excitation. Unlike passive suspension, which has constant spring and damping coefficients, active suspension incorporates electronic actuators into the system to dynamically control stiffness and damping variables. However, effectively controlling the suspension system poses a challenging task that necessitates real-time adaptability to various road conditions. This paper presents the Physics-Guided Deep Reinforcement Learning (DRL) for adjusting an active suspension system's variable kinematics and compliance properties for a quarter-car model in real time. Specifically, the outputs of the model are defined as actuator stiffness and damping control, which are bound within physically realistic ranges to maintain the system's physical compliance. The proposed model was trained on stochastic road profiles according to ISO 8608 standards to optimize the actuator's control policy. According to qualitative results on simulations, the vehicle body reacts smoothly to various novel real-world road conditions, having a much lower degree of oscillation. These observations mean a higher level of passenger comfort and better vehicle stability. Quantitatively, DRL outperforms passive systems in reducing the average vehicle body velocity and acceleration by 43.58% and 17.22%, respectively, minimizing the vertical movement impacts on the passengers. The code is publicly available at github.com/anh-nn01/RL4Suspension-ICMLA23.
Fast Approximate Clearance Evaluation for Rovers with Articulated Suspension Systems
Jet Propulsion Laboratory（喷气推进实验室）；California Institute of Technology（加州理工学院）；Ècole Normale Supèrieure de Paris（巴黎高等师范学院）；Indian Institute of Technology（印度理工学院）
DART-S: Reachability-Audited Active-Suspension Preconditioning for Off-Road Vehicle Jumps
Research Center for Intelligent Computing Systems, Institute of Computing Technology, CAS（中国科学院计算技术研究所智能计算系统研究中心）；Dong Feng Off-Road Vehicle Co., Ltd（东风越野车有限公司）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）
Sim-to-real transfer of active suspension control using deep reinforcement learning
Umeå University（于默奥大学）；Algoryx Simulation AB（Algoryx仿真公司）；Skogforsk (the Forestry Research Institute of Sweden)（瑞典林业研究院（Skogforsk））；Karlstad University（卡尔斯塔德大学）
Control of automated guided vehicles without collision by quantum annealer and digital devices
Graduate School of Information Sciences, Tohoku University（东北大学信息科学研究生院）；Institute of Innovative Research, Tokyo Institute of Technology（东京技术大学创新研究所）；Electronics R & I Division, DENSO CORPORATION（DENSO公司电子研究与开发部）
Modeling and Analysis of Unmanned Remote Guided Vehicle on Rough and Loose Snow Terrain
College of Engineering Pune（浦那工程学院）；RMD Sinhgad School of Engineering（RMD辛加德工程学院）；Vellore Institute of Technology（韦洛尔理工大学）；Trinity Academy of Engineering（三一工程学院）
Department of Civil, Architectural, and Environmental Engineering, The University of Texas at Austin（德克萨斯大学奥斯汀分校土木、建筑与环境工程系）；School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（上海交通大学自动化与智能感知学院）；College of Electrical Engineering, Zhejiang University（浙江大学电气工程学院）；School of Automotive Studies, Tongji University（同济大学汽车学院）；School of Architecture, The University of Texas at Austin（德克萨斯大学奥斯汀分校建筑学院）；Department of Civil and Environmental Engineering, University of Wisconsin-Madison（威斯康星大学麦迪逊分校土木与环境工程系）
Modeling and Control for UAV with Off-center Slung Load
Dept. of Elec. and Comp. Eng. Univ. of Alberta（电子与计算机工程系阿尔伯塔大学）；School of Science, Dalian Minzu University（科学学院大连民族大学）；Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, and School of Control Science and Engineering, Dalian University of Technology（教育部智能控制与优化工业设备重点实验室，控制科学与工程学院，大连理工大学）
KG-ASG: Collision-Knowledge-Guided Closed-Loop Adversarial Scenario Generation With Primary-Support Attribution
Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University（广东省智能交通系统重点实验室，智能系统工程学院，中山大学深圳校区）
Integrating Decision-Making Into Differentiable Optimization Guided Learning for End-to-End Planning of Autonomous Vehicles
The Hong Kong University of Science and Technology（香港科技大学）；Lotus Robotics（路特斯机器人公司）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与宇航工程学院）
本文提出将决策与轨迹规划建模为可微非线性优化并融合神经网络预测器，构建端到端自动驾驶规划框架，在 Waymo Open Motion 上验证其安全、效率与舒适性优势。
