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Toward Human-like Teleoperated Robot Motion: Performance and Perception of a Choreography-inspired Method in Static and Dynamic Tasks for Rapid Pose Selection of Articulated Robots

机译:迈向类似人的遥控机器人运动:在静态和动态任务中为铰接式机器人快速选择姿势的舞美启发方法的性能和感知

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In some applications, operators may want to create fluid, human-like motion on a remotely-operated robot, for example, a device used for remote telepresence. This paper examines two methods of controlling the pose of a Baxter robot via an Xbox One controller. The first method is a joint- by-joint (JBJ) method in which one joint of each limb is specified in sequence. The second method of control, named Robot Choreography Center (RCC), utilizes choreographic abstractions in order to simultaneously move multiple joints of the limb of the robot in a predictable manner. Thirty-eight users were asked to perform four tasks with each method. Success rate and duration of successfully completed tasks were used to analyze the performances of the participants. Analysis of the preferences of the users found that the joint-by-joint (JBJ) method was considered to be more precise, easier to use, safer, and more articulate, while the choreography-inspired (RCC) method of control was perceived as faster, more fluid, and more expressive. Moreover, performance data found that while both methods of control were over 80% successful for the two static tasks, the RCC method was an average of 11.85% more successful for the two more difficult, dynamic tasks. Future work will leverage this framework to investigate ideas of fluidity, expressivity, and human-likeness in robotic motion through online user studies with larger participant pools.
机译:在某些应用中,操作员可能希望在远程操作的机器人(例如用于远程网真的设备)上产生类似于人的流畅运动。本文研究了通过Xbox One控制器控制Baxter机器人的姿势的两种方法。第一种方法是逐个关节(JBJ)方法,其中按顺序指定每个肢体的一个关节。第二种控制方法称为机器人编排中心(RCC),它利用编排抽象来以可预测的方式同时移动机器人肢体的多个关节。 38名用户被要求用每种方法执行四个任务。成功完成任务的成功率和持续时间用于分析参与者的表现。对用户的偏好进行分析后发现,逐个关节(JBJ)的方法被认为更精确,更易于使用,更安全,更清晰,而编舞灵感(RCC)的控制方法被认为是更快,更流畅,更富有表现力。此外,性能数据还发现,虽然两种控制方法对两种静态任务的成功率均超过80%,但RCC方法对两种较困难的动态任务的成功率平均高出11.85%。未来的工作将利用此框架,通过具有较大参与者池的在线用户研究,来研究机器人运动中的流动性,表达性和人性化观念。

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