This paper concerns the problem of dynamic modelling 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 paper develops a nonlinear, mechanistic simulation model that can subsequently be used to investigate physically meaningful disturbances. The second contribution is to optimise 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-objectivisation 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-objectivisation 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.

Missions in extreme environments such as exploration and mapping of chemical leakage, radiation and high temperature are hazardous for human workers. Therefore, robotic solutions can be utilised in these applications. Swarm robotics is an alternative approach to traditional robotics, with potential to increase the efficiency of robotic solutions in an extreme environment. Swarm robotic systems are low-cost, robust and scalable. In this paper, we focused on an exploration scenario for use in an extreme environment application. The scenario for this study was to explore the different levels of radioactive or chemical leakage from drums in a nuclear storage facility. The environment and mobile robot used for this work, Mona, were modelled in the V-REP simulation software. The effects of experimental parameters, i.e. the number of robots and complexity of the storage facility, on the efficiency of the exploration system were evaluated. The results revealed that, an increase in population size increased the performance of the exploration significantly. Moreover, the complexity of the environment did not have significant impact on the performance of the system.

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.