首页|Data from National Technical University of Athens Advance Knowledge in Machine L earning (An Open Extended Reality Platform Supporting Dynamic Robot Paths for St udying Human-robot Collaboration In Manufacturing)
Data from National Technical University of Athens Advance Knowledge in Machine L earning (An Open Extended Reality Platform Supporting Dynamic Robot Paths for St udying Human-robot Collaboration In Manufacturing)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting out of Athens, Greece, by N ewsRx editors, research stated, “Human-robot collaboration (HRC) in manufacturin g allows advantageous distribution of tasks, e.g. exploiting robot accuracy and human dexterity, safety being of paramount importance. Safety is mostly linked t o avoiding collisions between the human and the robot but the pertinent measures adopted should prolong task duration as little as possible.” Our news journalists obtained a quote from the research from the National Techni cal University of Athens, “In order to test such measures in HRC pertinent algor ithms need to be applied, which is made possible without jeopardising human safe ty only in an Extended Reality environment. In order to implement path planning algorithms and human-robot interaction rules freely the environment must be open . In this work, the development of such an environment is presented and demonstr ated by example of laying up carbon fibre fabric sheets in a mould. An existing open platform was substantially extended by embedding robot control functionalit y concerning motion, path and trajectory planning emphasizing static and dynamic obstacle detection, interactive input and manipulation and real-time path plann ing, whereas trajectory planning focused on ensuring acceptability of joint moti on solutions using inverse kinematics. Two different real-time path planning met hods are embedded in the environment as representative examples. The first one i s the established ‘Rapidly exploring Random Tree’ (RRT) algorithm followed by pa th optimization. The second one is ‘Machine-Learned Path Planning’ (MLPP) a prot otype machine learning model trained using linear regression with Gaussian noise based on safe path planning data generated by users. The evaluation criteria of these methods were the number and severity of collisions as well as the total c ompletion time of the manufacturing task. In the particular case examined, the m achine learning technique proved much faster than RRT but not as safe, despite i ts potential.”
AthensGreeceEuropeCyborgsEmergin g TechnologiesMachine LearningRobotRoboticsNational Technical University of Athens
NETL
NSTL
