Redundant manipulators offer a continuum of joint configurations which satisfy a specific end-effector pose, an advantage when operating within confined spaces. This, how-ever, challenges a controller to select a single goal configuration from a wide range when path planning. This paper outlines the use of the MySQL database management system for systematic goal selection during redundant manipulator path planning in confined spaces. We outline a sampling method to envelope all configurations of a redundant manipulator and utilise it to generate a complete database of configurations. We demonstrate the application of this method to generate a large data-set of (1 billion) manipulator configurations for a KUKA LBR iiwa 14 equipped with a Robotiq 2F-85 gripper. With this database, the controller systematically selects goal configurations during 50 path planning scenarios within the confined space of a glovebox. We compare this to an iterative method using existing kinematic solvers to select goal configurations as a baseline. The database method achieves a 100% success rate in 42% of the scenarios attempted. In comparison, the baseline method achieves >50% success rate in just 6% of the scenarios attempted. Our proposed method also produces repeatable paths, which are similar in length and link swept area for each attempt of the same scenario, whereas the baseline method generates a different path in every attempt.

The development and demonstration use of a hexapod robotic system for indoor gamma and neutron radiation mapping in confined spaces is presented. The prototype software is ROS-based and uses laser-based 2D SLAM techniques to navigate an unknown environment, while recording continuous measurements of gamma and neutron radiation at a 1Hz sampling rate. Experiments demonstrated that the robot could successfully map controlled environments, producing 2D radiation maps clearly identifying ‘hotspots’ that coincided with the location of radiation sources hidden in the test range. The tests confirmed that the hexapod platform exhibits superior performance regarding contamination pick-up when compared to other motion systems such as tracked rovers, wheeled rovers, etc. This was attributed to its minimal contact surface with the ground, which is considered particularly important in facilities where loose radioactive particulates are present. By using feet caps and a protective suit, the system can be decontaminated and fully recovered for subsequent redeployments.

Presented here is a compact power supply design for robots operating in nuclear environments, which must be tolerant to damage from ionising radiation. The proposed topology is fault-tolerant through intrinsic redundancy in its layout, with 8 independent channels operating in parallel. This application-specific implementation operates as a buck regulator (step-down) with an input voltage range of 12 V – 48 V DC and a continuous output power of 250 W without requiring passive or active cooling other than ambient air flow. The electrical efficiency of the prototype varies between 70% and 90% at the rated maximum input voltage of 48 V. The architecture of the design is presented, and an analysis of possible failure modes is conducted along with circuit level remediation efforts to make the design intrinsically fail-safe. The fault tolerance and redundancy are illustrated by artificially inducing faults on prototype boards during performance measurements. Finally, the prototype is assembled into a hexapod walker robot and deployed in a radioactive environment as a field test of its radiation and fault tolerant properties.

We evaluate AR for Plant maintenance by measuring how a prototype guidance system, tested under representative conditions, impacts performance. We are motivated to determine the cost-benefit of interactive guidance for hazardous, repetitive tasks and we observe an improvement of 21% efficiency, 50% accuracy and 19% reduced task load. AR has already been shown to deliver improvements in task performance, however, there is limited research exploring the integration of AR into complete task routines which presents a barrier to adoption. We apply mixed reality guidance via two within-group experiments. We measure efficiency and accuracy over a complete routine conducted under simulated conditions. Results compare AR versus Static and AR versus Adaptive. We conclude AR is best suited to demanding spatial translation and completion under pressure. We suggest AR offers potential in similar routines and propose further work to integrate in a live setting.