首页|Study Results from National University of Defense Technology Broaden Understandi ng of Robotics and Automation (Ticop: Timecritical Coordinated Planning for Fix ed-wing Uavs In Unknown Unstructured Environments)

Study Results from National University of Defense Technology Broaden Understandi ng of Robotics and Automation (Ticop: Timecritical Coordinated Planning for Fix ed-wing Uavs In Unknown Unstructured Environments)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Fresh data on Robotics - Robotics and Automation are presented in a new report.According to news reporting from Chang sha, People's Republic of China, by NewsRx journalists, researchstated, "Safe c oordination of fixed-wing UAVs in unstructured environments poses challenges due to theintricate coupling of UAV cooperation, obstacle avoidance, and motion co nstraints. One task is timecriticalcoordination, which means that all UAVs can safely reach their destinations simultaneously."Financial support for this research came from National Natural Science Foundatio n of China (NSFC).The news correspondents obtained a quote from the research from the National Uni versity of DefenseTechnology, "Existing methods, which rely on a spatio-tempora l decoupling framework and design thecoordination law at the control layer, oft en compromise maneuverability and safety in coordination scenarios.To address t he challenges, we propose a planner-based framework that enables fixed-wing UAVs tonavigate in unknown, unstructured environments with time-critical coordinati on. This framework, which isproposed with theoretical analysis, coordinates the UAVs at the planning layer rather than the control layer.Moreover, a different ial-flatness-based trajectory planning method is presented within this framework ,after which a two-step method is designed to undermine the problem of local mi nima."

ChangshaPeople's Republic of ChinaAs iaRobotics and AutomationRoboticsNational University of Defense Technology

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Oct.31)