This article concerns the problem of dynamic modeling and parameter estimation for a seven degree of freedom hydraulic manipulator. The laboratory example is a dual-manipulator mobile robotic platform used for research into nuclear decommissioning. In contrast to earlier control model orientated research using the same machine, the article develops a nonlinear, mechanistic simulation model that can subsequently be used to investigate physically meaningful disturbances. The second contribution is to optimize the parameters of the new model, i.e. to determine reliable estimates of the physical parameters of a complex robotic arm which are not known in advance. To address the nonlinear and non-convex nature of the problem, the research relies on the multi-objectivization of an output error single performance index. The developed algorithm utilises a multi-objective Genetic Algorithm (GA) in order to find a proper solution. The performance of the model and the GA is evaluated using both simulated (i.e. with a known set of 'true' parameters) and experimental data. Both simulation and experimental results show that multi-objectivization has improved convergence of the estimated parameters compared to the single objective output error problem formulation. This is achieved by integrating the validation phase inside the algorithm implicitly and exploiting the inherent structure of the multi-objective GA for this specific system identification problem.

This article investigates visual servoing for a hydraulically actuated dual-arm robot, in which the user selects the object of interest from an on-screen image, whilst the computer control system implements via feedback control the required position and orientation of the manipulators.To improve on the current joystick direct tele-operation commonly used as standard in the nuclear industry, which is slow and requires extensive operator training, the proposed assisted tele-operation makes use of a single camera mounted on the robot. Focusing on pipe cutting as an example, the new system ensures that one manipulator automatically grasps the user-selected pipe, and appropriately positions the second for a cutting operation. Initial laboratory testing (using a plastic pipe) shows the efficacy of the approach for positioning the manipulators, and suggests that for both experienced and inexperienced users, the task is completed significantly faster than via tele-operation.

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The need for nuclear decommissioning is increasing globally, as power stations and other facilities utilising nuclear reaches the end of their operational life. Currently the majority of decommissioning tasks are carried out by workers in protective air fed suits, which is slow, expensive and dangerous. The work presented here is the early stages in the development of a flexible mobile manipulator platform, combining a Clearpath Husky, a Universal UR5 manipulator and various sensors. The system will be used for research specifically in the area of exploration of contaminated environments, map building to aid in task planning, and also to investigate manipulation for waste sorting. The aim is to develop a system that can, in the short term, be used in real world tasks but longer term function as a research platform to allow continued research and development. As well as developing a hardware platform, a detailed simulation model is also being developed to allow testing of algorithms in simulation before being deployed on hardware. The use of the simulation model for operator training is also an area that will be investigated in the future. This article focuses on the planned work for developing the system, as well as discussing the progress on the simulation model.

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A mobile camera is used to support an assisted teleoperation pipe-cutting system for nuclear decommissioning. The base system consists of dual-manipulators with a single mounted Kinect camera. The user selects the object from an onscreen image, whilst the computer control system automatically grasps the pipe with one end-effector and positions the second for cutting. However, the system fails in some cases because of data limitations, for example a partially obscured pipe in a challenging decommissioning scenario (simulated in the laboratory). Hence, the present article develops a new method to increase the use case scenarios via the introduction of mobile cameras e.g. for mounting on a drone. This is a non-trivial problem, with SLAM and ArUco fiducials introduced to locate the cameras, and a novel error correction method proposed for finding the ArUco markers. Preliminary results demonstrate the validity of the approach but improvements will be required for robust autonomous cutting. Hence, to reduce the pipe position estimation errors, suggestions are made for various algorithmic and hardware refinements.

The robotic platform in this study is being used for research into assisted tele-operation for common nuclear decommissioning tasks, such as remote pipe cutting. It has dual, seven-function, hydraulically actuated manipulators mounted on a mobile base unit. For the new visual servoing system, the user selects an object from an on-screen image, whilst the computer control system determines the required position and orientation of the manipulators; and controls the joint angles for one of these to grasp the pipe and the second to position for a cut. Preliminary testing shows that the new system reduces task completion time for both inexperienced and experienced operators, in comparison to tele-operation. In a second contribution, a novel state-dependent parameter (SDP) control system is developed, for improved resolved motion of the manipulators. Compared to earlier SDP analysis of the same device, which used a rather ad hoc scaling method to address the dead-zone, a state-dependent gain is used to implement inverse dead-zone control. The new approach integrates input signal calibration, system identification and nonlinear control design, allowing for straightforward recalibration when the dynamic characteristics have changed or the actuators have deteriorated due to age.

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