We present early pilot-studies of a new international project, developing advanced robotics to handle nuclear waste. Despite enormous remote handling requirements, there has been remarkably little use of robots by the nuclear industry. The few robots deployed have been directly teleoperated in rudimentary ways, with no advanced control methods or autonomy. Most remote handling is still done by an aging workforce of highly skilled experts, using 1960s style mechanical Master-Slave devices. In contrast, this paper explores how novice human operators can rapidly learn to control modern robots to perform basic manipulation tasks; also how autonomous robotics techniques can be used for operator assistance, to increase throughput rates, decrease errors, and enhance safety. We compare humans directly teleoperating a robot arm, against humansupervised semi-autonomous control exploiting computer vision, visual servoing and autonomous grasping algorithms. We show how novice operators rapidly improve their performance with training; suggest how training needs might scale with task complexity; and demonstrate how advanced autonomous robotics techniques can help human operators improve their overall task performance. An additional contribution of this paper is to show how rigorous experimental and analytical methods from human factors research, can be applied to perform principled scientific evaluations of human test-subjects controlling robots to perform practical manipulative tasks.
Gripper
CameraSample test rig 7 DOF Robot Test objects for stacking task
Abstract-Redundant and non-operational buildings at nuclear sites are decommissioned over a period of time. The process involves demolition of physical infrastructure resulting in large quantities of residual waste material. The resulting waste materials are packed into import containers to be delivered for post-processing, containing either sealed canisters or assortments of miscellaneous objects. At present postprocessing does not happen within the United Kingdom. Sellafield Ltd. and National Nuclear Laboratory are developing a process for future operation so that upon an initial inspection, imported waste materials undergo two stages of post-processing before being packed into export containers, namely sort and segregate or sort and disrupt. The postprocessing facility will remotely treat and export a wide range of wastes before downstream encapsulation. Certain wastes require additional treatment, such as disruption, before export to ensure suitability for long-term disposal. This article focuses on the design, development, and demonstration of a reconfigurable rational agent-based robotic system that aims to highly automate these processes removing the need for close human supervision. The proposed system is being demonstrated through a downsized, lab-based setup incorporating a smallscale robotic arm, a time-of-flight camera, and high-level rational agentbased decision making and control framework.
Robots are increasingly being required to perform tasks which involve contacts with the environment. This paper addresses the problem of estimating environmental constraints on the robot's motion. We present a method which estimates such constraints, by computing the null space of a set of velocity vectors which differ from commanded velocities during contacts. We further extend this method to handle unilateral constraints, for example when the robot touches a rigid surface. Unlike previous work, our method is based on kinematics analysis, using only proprioceptive joint encoders, thus there is no need for either expensive force-torque sensors or tactile sensors at the contact points or any use of vision. We first show results of experiments with a simulated robot in a variety of situations, and we analyse the effect of various levels of observation noise on the resulting contact estimates. Finally we evaluate the performance of our method on two sets of experiments using a KUKA LWR IV manipulator, tasked with exploring and estimating the constraints caused by a horizontal surface and an inclined surface.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.