Neural networks impedance control of robots interacting with environments

In this paper, neural networks impedance control is proposed for robot-environment interaction. Iterative learning control is developed to make the robot dynamics follow a given target impedance model. To cope with the problem of unknown robot dynamics, neural networks are employed such that neither the robot structure nor the physical parameters are required for the control design. The stability and performance of the resulted closed-loop system are discussed through rigorous analysis and extensive remarks. The validity and feasibility of the proposed method are verified through simulation studies

Whole Body Control of a Dual-Arm Mobile Robot Using a Virtual Kinematic Chain

Dual-arm manipulators have more advanced manipulation abilities compared to single-arm manipulators and manipulators mounted on a mobile base have additional mobility and a larger workspace. Combining these advantages, mobile dual-arm robots are expected to perform a variety of tasks in the future. Kinematically, the configuration of two arms that branches from the mobile base results in a serial-to-parallel kinematic structure. In order to respond to external disturbances, this serial-to-parallel kinematic structure makes inverse kinematic computations non-trivial, as the motion of the base has to take the needs of both arms into account. Instead of using the dual-arm kinematics directly, we propose to use a virtual kinematic chain (VKC) to specify the common motion of the two arms. We formulate a constraint-based programming solution which consists of two parts. In the first part, we use an extended serial kinematic chain including the mobile base and the VKC to formulate constraints that realize the desired orientation and translation expressed in the world frame. In the second part, we use the resolved VKC motion to constrain the common motion of the two arms. In order to explore the redundancy of the two arms in an optimization framework, we also provide a VKC-oriented manipulability measure as well as its closed-form gradient. We verify the proposed approach with simulations and experiments that are performed on a PR2 robot, which has two 7 degrees of freedom (DoF) arms and a 3 DoF mobile base

A survey of robot manipulation in contact

Abstract In this survey, we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environment to complete the task. Robots can perform more and more manipulation tasks that are still done by humans, and there is a growing number of publications on the topics of (1) performing tasks that always require contact and (2) mitigating uncertainty by leveraging the environment in tasks that, under perfect information, could be performed without contact. The recent trends have seen robots perform tasks earlier left for humans, such as massage, and in the classical tasks, such as peg-in-hole, there is a more efficient generalization to other similar tasks, better error tolerance, and faster planning or learning of the tasks. Thus, in this survey we cover the current stage of robots performing such tasks, starting from surveying all the different in-contact tasks robots can perform, observing how these tasks are controlled and represented, and finally presenting the learning and planning of the skills required to complete these tasks