摘要
机器人与机器学习每日新闻-机器人与自动化的最新研究结果已经发表。根据NewsRx记者来自中华人民共和国武汉的消息，研究表明：“连续可靠的自速度信息对于各种机器人任务的高性能运动控制和规划具有重要意义。”当线性速度作为一阶运动学可以与其他状态同时估计或通过与里程计等自我运动估计器的位置不同而明确地获得时，高耦合会导致估计器退化时的不稳定甚至失效。本研究经费来源于国家重点研究开发项目。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Robotics - Ro botics and Automation have been published. According to news originating from Wu han, People’s Republic of China, by NewsRx correspondents, research stated, “Con tinuous and reliable ego-velocity information is significant for high-performanc e motion control and planning in a variety of robotic tasks, such as autonomous navigation and exploration. While linear velocities as first-order kinematics ca n be simultaneously estimated with other states or explicitly obtained by differ entiation from positions in ego-motion estimators such as odometers, the high co upling leads to instability and even failures when estimators degenerate.” Financial support for this research came from National Key Research and Developm ent Program of China.
NETL
NSTL