There are thousands of asteroids in near-Earth space and millions in the Main Belt. They are diverse in physical properties and composition and are time capsules of the early solar system. This makes them strategic locations for planetary science, resource mining, planetary defense/security and as inter-planetary depots and communication relays. However, asteroids are a chal-lenging target for surface exploration due it its low but highly nonlinear gravity field. In such conditions, mobility through ballistic hopping possess multiple advantages over conventional mobility solutions and as such hop-ping robots have emerged as a promising platform for future exploration of asteroids and comets. They can traverse large distances over rough terrain with the expenditure of minimum energy. In this paper we present ballistic hopping dynamics and its motion planning on an asteroid surface with highly nonlinear gravity fields. We do it by solving Lambert's orbital boundary val-ue problem in irregular gravity fields by a shooting method to find the initial velocity required to intercept a target. We then present methods to localize the hopping robot using pose estimation by successive scan matching with a 3D laser scanner. Using the above results, we provide methods for motion planning on the asteroid surface over long distances. The robot will require to perform multiple hops to reach a desired goal from its initial position while avoiding obstacles. The study is then be extended to find optimal tra-jectories to reach a desired goal by visiting multiple waypoints.
Benchmarking Classical Coverage Path Planning Heuristics on Irregular Hexagonal Grids for Maritime Coverage Scenarios
Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez（工程与科学学院，阿道弗·伊巴涅斯大学）；Dirección de Programas, Investigación y Desarrollo, Armada de Chile（智利海军计划、研究与开发 Directorate）；Millennium Nucleus for Social Data Science (SODAS)（社会科学数据科学千年核基金（SODAS））；Millennium Nucleus in Data Science for Plant Resilience (PhytoLearning)（植物韧性数据科学千年核基金（PhytoLearning））
Improving Efficiency of Sampling-based Motion Planning via Message-Passing Monte Carlo
CSAIL, MIT（MIT计算机科学与人工智能实验室）；ELLIS Institute（埃里克·林斯研究所）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Tübingen AI Center（图宾根人工智能中心）；Case Western Reserve University（凯斯西储大学）
Efficient and Safe Trajectory Planning for Autonomous Agricultural Vehicle Headland Turning in Cluttered Orchard Environments
ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University（浙江大学ZJU-杭州全球科技创新中心）；University of California, Davis（加州大学戴维斯分校）；College Of Biosystems Engineering And Food Science, Zhejiang University（浙江大学生物系统工程与食品科学学院）
Work Zones challenge VLM Trajectory Planning: Toward Mitigation and Robust Autonomous Driving
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Northwest A&F University（西北农林科技大学）；Nanyang Technological University（南洋理工大学）；Jinan University（济南大学）
