Motion planning for urban environments with numerous moving agents can be viewed as a combinatorial problem. With passing an obstacle before, after, right or left, there are multiple options an autonomous vehicle could choose to execute. These combinatorial aspects need to be taken into account in the planning framework. We address this problem by proposing a novel planning approach that combines trajectory planning and maneuver reasoning. We define a classification for dynamic obstacles along a reference curve that allows us to extract tactical decision sequences. We separate longitudinal and lateral movement to speed up the optimization-based trajectory planning. To map the set of obtained trajectories to maneuver variants, we define a semantic language to describe them. This allows us to choose an optimal trajectory while also ensuring maneuver consistency over time. We demonstrate the capabilities of our approach for a scenario that is still widely considered to be challenging.
Robust Spatiotemporal Motion Planning for Multi-Agent Autonomous Racing via Topological Gap Identification and Accelerated MPC
Department of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）；Center for Project-Based Learning, D-ITET, ETH Zurich（苏黎世联邦理工学院项目导向学习中心）
Coverage Path Planning for Autonomous Sailboats in Inhomogeneous and Time-Varying Oceans: A Spatiotemporal Optimization Approach
Institute of Deep-sea Science and Engineering, Chinese Academy of Sciences（中国科学院深海科学与工程研究所）；Institute for Ocean Engineering, Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院海洋工程研究所）；ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University（浙江大学 Hangzhou 全球科技创新中心）；Department of Engineering, Faculty of Computing, Engineering and the Built Environment, Birmingham City University（伯明翰城市大学工程系）；School of Ocean and Civil Engineering, Shanghai Jiao Tong University（上海交通大学海洋与Civil Engineering学院）
Learning Spatiotemporal Decision Priors for Efficient Path Planning under Partial Observability
College of Intelligent Robotics and Advanced Manufacturing, Fudan University（智能机器人与先进制造学院，复旦大学）；School of Information Science and Technology, Fudan University（信息科学与技术学院，复旦大学）；Department of Information Technology, Old Dominion University（信息技术系，老 Dominion 大学）
Multi-layer Motion Planning with Kinodynamic and Spatio-Temporal Constraints
The Thørväld Group（索尔瓦尔德集团）；Inria Paris-Rocquencourt（巴黎-罗quent court 国家科学研究中心）；Rajiv Gandhi University Doimukh（拉贾·甘地大学多伊穆克）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒姆研究实验室）；The Kumquat Consortium（昆克特联盟）
SL1M: Sparse L1-norm Minimization for contact planning on uneven terrain
The University of Edinburgh（爱丁堡大学）；Ewha Womans University（梨花女子大学）；LAAS-CNRS（法国国家科学研究中心分析与系统结构实验室）；Université de Toulouse（图卢兹大学）；University of Trento（特伦托大学）
Efficient Multi-Robot Motion Planning with Precomputed Translation-Invariant Edge Bundles
Smead Aerospace Engineering Sciences, University of Colorado Boulder（科罗拉多大学博尔德分校航空航天工程科学系）；Department of Computer Science, University of Colorado Boulder（科罗拉多大学博尔德分校计算机科学系）；Cherry Creek High School, Greenwood Village, CO, USA（科罗拉多州格里诺村樱桃溪高中）
FLIP: Real-Time and Resilient Formation Planning for Large-Scale DIstributed Swarms via Point Cloud Registration
Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院智能系统与控制研究所）；Huzhou Institute, Zhejiang University（浙江大学湖州研究院）
Department of Aeronautics and Astronautics, Massachusetts Institute of Technology（航空航天系，麻省理工学院）；Department of Electrical and Systems Engineering, University of Pennsylvania（电气与系统工程系，宾夕法尼亚大学）
QuadHand: A Compact Quadrotor Aerial Manipulator with MRC-SDF-Based Whole-Body Motion Planning
Nanyang Technological University（南洋理工大学）；NTU–VinUni Joint Research Laboratory for Embodied AI and Robotics（南洋理工大学-维纳大学具身智能与机器人联合研究实验室）；VinUniversity（维纳大学）
Risk Map As Middleware: Towards Interpretable Cooperative End-to-end Autonomous Driving for Risk-Aware Planning
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University（教育部道路与交通工程重点实验室，同济大学）；UCLA Mobility Lab, University of California, Los Angeles（加州大学洛杉矶分校UCLA移动实验室）
Spatiotemporal Receding Horizon Control with Proactive Interaction Towards Autonomous Driving in Dense Traffic
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Great Bay University（大湾区大学）；The Hong Kong University of Science and Technology（香港科技大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深港协同创新研究院）
