Human-like Manipulation Planning for Articulated Manipulator

A new method based on human-likeness assessment and optimization concept to solve the problem of human-like ma- nipulation planning for articulated robot is proposed in this paper. This method intrinsically formulates the problem as a con- strained optimization problem in robot configuration space. The robot configuration space is divided into different subregions by human likeness assessment. A widely used strategy, Rapid Upper Limb Assessment （RULA） in applied ergonomics, is adopted here to evaluate the human likeness of robot configuration. A task compatibility measurement of the robot velocity transmission ratio along a specified direction is used as the target function for the optimization problem. Simple illustrative examples of this method applied to a two Degree of Freedom （DOF） planar robot that resembles the upper limb of a human are presented. Further applications to a humanoid industrial robot SDA10D are also presented. The reasonable planning results for these applications assert the effectiveness of our method.

A novel task-oriented framework for dual-arm robotic assembly task

In industrial manufacturing,the deployment of dual-arm robots in assembly tasks has become a trend.However,making the dual-arm robots more intelligent in such applications is still an open,challenging issue.This paper proposes a novel framework that combines task-oriented motion planning with visual perception to facilitate robot deployment from perception to execution and finish assembly problems by using dual-arm robots.In this framework,visual perception is first employed to track the effects of the robot behaviors and observe states of the workpieces,where the performance of tasks can be abstracted as a high-level state for intelligent reasoning.The assembly task and manipulation sequences can be obtained by analyzing and reasoning the state transition trajectory of the environment as well as the workpieces.Next,the corresponding assembly manipulation can be generated and parameterized according to the differences between adjacent states by combining with the prebuilt knowledge of the scenarios.Experiments are set up with a dual-arm robotic system(ABB YuMi and an RGB-D camera)to validate the proposed framework.Experimental results demonstrate the effectiveness of the proposed framework and the promising value of its practical application.