The design and evaluation of an ergonomic contactless gesture control system for industrial robots

In industrial human-robot collaboration, variability commonly exists in the operation environment and the components, which induces uncertainty and error that require frequent manual intervention for rectification. Conventional teach pendants can be physically demanding to use and require user training prior to operation. Thus, a more effective control interface is required. In this paper, the design and evaluation of a contactless gesture control system using Leap Motion is described. The design process involves the use of RULA human factor analysis tool. Separately, an exploratory usability test was conducted to compare three usability aspects between the developed gesture control system and an off-the-shelf conventional touchscreen teach pendant. This paper focuses on the user-centred design methodology of the gesture control system. The novelties of this research are the use of human factor analysis tools in the human-centred development process, as well as the gesture control design that enable users to control industrial robot’s motion by its joints and tool centre point position. The system has potential to use as an input device for industrial robot control in a human-robot collaboration scene. The developed gesture control system was targeting applications in system recovery and error correction in flexible manufacturing environment shared between humans and robots. The system allows operators to control an industrial robot without the requirement of significant training

The development and evaluation of Robot Light Skin: A novel robot signalling system to improve communication in industrial human–robot collaboration

In a human–robot collaborative production system, the robot could make request for interaction or notify the human operator if an uncertainty arises. Conventional industrial tower lights were designed for generic machine signalling purposes which may not be the ultimate solution for robot signalling in a collaborative setting. In this type of system, human operators could be monitoring multiple robots while carrying out a manual task so it is important to minimise the diversion of their attention. This paper presents a novel robot signalling solution, the Robot Light Skin (RLS),which is an integrated signalling system that could be used on most articulated robots. Our experiment was conducted to validate this concept in terms of its effect on improving operator's reaction time, hit-rate, awareness and task performance. The results showed that participants reacted faster to the RLS as well as achieved higher hit-rate. An eye tracker was used in the experiment which shows a reduction in diversion away from the manual task when using the RLS. Future study should explore the effect of the RLS concept on large-scale systems and multi-robot systems

A self-learning case and rule-based reasoning algorithm for intelligent technology evaluation and selection [Abstract]

This research programme proposes to fulfill the existing gap in knowledge by providing an experience-oriented decision algorithm to solve technology selection problems based on cases and expert’s experience. The approach adopts historical case-based data to extract rules through the ID3 rule induction algorithm. The decision model integrates a rule induction approach in a rule-based knowledge system and database management system to support automated knowledge mining and usage. The adoption of a pair-wise comparison algorithm within the similarity index assists in relating the importance of the criteria within the knowledgebases reasoner. A series of historical and new solutions are presented in a scoring index based on the requirements of a new case

Transverse impact response analysis of graphene panels: Impact limits

Explicit numerical studies were conducted to determine the transverse impact response of graphene panels. Although the mechanical properties of graphene are well documented in both quasi-static and dynamic conditions via nanoand microscopic studies, the impact behaviour of the material at the macroscale has not yet been studied and would provide interesting and crucial insight in to the performance of the material on a more widely recognizable scale. Firstly, a numerical impact model was validated against an analytical impact model based on continuum mechanics which showed good correlation between contact-force histories. The performance of graphene panels subjected to impact was compared to the performance of panels composed of aerospace-grade aluminium and carbon fiber reinforced polymer (CFRP) composite. The graphene panel was found to exhibit lower specific energy than aluminium and CFRP at the low-energy range due to its inherently superior stiffness and intrinsic strength. On the other hand, the ballistic limit of 3 mm thick graphene panels was found to be 3375 m/s, resulting in an impact resistance 100 times greater than for aluminium or CFRP, making graphene the most suitable material for high-velocity impact protection

Are We Driving Strategic Results or Metric Mania? Evaluating Performance in the Public Sector

A strategy is irrelevant if you cannot implement it. That is the collective realization of public and private leaders after decades of obsession with strategy and strategic thinking. That realization has led to a voracious market for ideas on execution, alignment around strategy and predictable achievement of strategic results. Many performance management systems or tools, all meant to help organizational leaders implement their strategic goals and objectives, fail to provide results. We suggest a framework in which strategic and operational goals can be translated into a handful of meaningful metrics that we define as whole goals. Whole goals can then used to drive decision-making and to hold leadership accountable for achieving measurable results. We believe the ability of a public organization to measure and evaluate its performance is of critical importance if today’s leaders and managers are expected to promote successful execution of organizational strategic goals and objectives

‘Educating RITTA’: evaluation of an artificial intelligence programme in opioid prescribing - a pilot project and needs assessment

Background/aims: Through a person centred, design thinking process, our cancer hospital palliative care team in conjunction with IBM Watson developed an Artificial Intelligence (AI) enabled virtual assistant, trained in giving basic advice on opioids. This dialogue agent is currently trained to answer a limited number of patient generated queries to demonstrate capability. Our patient/carer group suggested a hospital virtual chatbot, that could answer queries at any time of day or night. Methods: Patients, carers and healthcare professionals were tasked with creating common queries and answers around opioid prescribing. Questions and answers were programmed into the IBM Watson machine learning appliance ‘RITTA’ (Realtime Information Technology Towards Activation) with help from IBM IT engineers. Results: Phase 1 testing results: 10 patients in a palliative care outpatient clinic who had recently been prescribed opioids, were invited to write down questions on the topic of these medications in palliative care. These queries were put to RITTA after the first programming phase. 50% of questions were answered well, with further programming needs identified due to language specifics, human misspellings, dialects, jargon and variations. Programming weaknesses were also identified. Conclusions: A key theme in the development of AI is the time, care and resources required to develop Machine Learning (ML) layers. Technical work included expanding patient generated queries and machine learning in areas like palliative opioid prescribing, where a lot of repetition occurs and human medication errors or omissions can happen repeatedly and cause harm. Machine learning in palliative care has potential, but will require significant time commitment to enter thousands of question/answer variations, even for small topic areas. We identified a need for local language, slang/dialect programmes, as well as check systems on how up to date clinical guidance remains

Managing delays for realtime error correction and compensation of an industrial robot in an open network

The calibration of articulated arms presents a substantial challenge within the manufacturing domain, necessitating sophisticated calibration systems often reliant on the integration of costly metrology equipment for ensuring high precision. However, the logistical complexities and financial burden associated with deploying these devices across diverse systems hinder their widespread adoption. In response, Industry 4.0 emerges as a transformative paradigm by enabling the integration of manufacturing devices into networked environments, thereby providing access through cloud-based infrastructure. Nonetheless, this transition introduces a significant concern in the form of network-induced delays, which can significantly impact realtime calibration procedures. To address this pivotal challenge, the present study introduces an innovative framework that adeptly manages and mitigates network-induced delays. This framework leverages two key components: controller and optimiser, specifically the MPC (Model Predictive Controller) in conjunction with the Extended Kalman Filter (EKF), and a Predictor, characterised as the Dead Reckoning Model (DRM). Collectively, these methodologies are strategically integrated to address and ameliorate the temporal delays experienced during the calibration process. Significantly expanding upon antecedent investigations, the study transcends prior boundaries by implementing an advanced realtime error correction system across networked environments, with particular emphasis on the intricate management of delays originating from network traffic dynamics. The fundamental aim of this research extension is twofold: firstly, it aims to enhance realtime system performance on open networks, while concurrently achieving an impressive level of error correction precision at 0.02 mm. The employment of the proposed methodologies is anticipated to effectively surmount the intricacies and challenges associated with network-induced delays. Subsequently, this endeavour serves to catalyse accurate and efficient calibration procedures in the context of realtime manufacturing scenarios. This research significantly advances the landscape of error correction systems and lays a robust groundwork for the optimised utilisation of networked manufacturing devices within the dynamic realm of Industry 4.0 applications

Realtime calibration of an industrial robot

In large scale, complex and low volume manufacturing systems, robotics are now considered unavoidable for automating the factory operations. The aerospace industry focuses on a high variety and quality but extremely low volume. The precision it requires for numerous tasks is unique and distinct from any other manufacturing industry. This can comprise accurate position, module assembly, inspection, fastening, etc. The scale of the robot invites different types of errors during operation, which can be either because of the kinematics of the robot or because of the environment (noise, temperature, load, etc.). There are packages available from robot manufacturers for the correction and compensation of errors on the robot to achieve accuracy. There are two associated problems: 1. cost and 2. static nature. They are very costly and they do not provide correction in realtime fashion (dynamic); the robot stops, waits for the correction, and then moves to the next position. The external tool to monitor the accuracy also requires attaching with the robot permanently. These are dedicated resources. These tools for accurate measurement are expensive and attached permanently to a robot, which means wastage of resources. These measuring tools are called metrology devices and attaching these devices and the robot to the network means that other robots/machines can also use these expensive tools for measurement. Our aim was to address two problems in this project: 1. calibration (error correction and compensation of robot) and 2. dynamic and realtime processing. It helped to perform the dynamic error correction and the compensation of an industrial robot. The results showed the error correction was achieved in the region of 0.02 mm

Using myoelectric signals for gesture detection: a feasibility study

Farshid Amirabdollahian, Michael Walter, Rory Heffernan, Sarah Fletcher, and Phil Webb, ‘Using myoelectric signals for gesture detection: a feasibility study’. Paper presented at the Ergonomics and Human Factors 2017 Conference, 25 – 27 April 2017, Daventry, United Kingdom.Abstract The propose of this study was to assess the feasibility of using myoelectric signals acquired using an off the shelf device, the Myo armband from Thalmic Lab. Background: With the technological advances in sensing human motion, and its potential to drive and control mechanical interfaces remotely, a multitude of input mechanisms are used to link actions between the human and the robot. In this study we explored the feasibility of using human arm’s myoelectric signals with the aim of identifying a number of gestures automatically. Material and methods: Participants (n = 26) took part in a study with the aim to assess the gesture detection accuracy using myoelectric signals. The Myo armband was used worn on the forearm. The session was divided into three phases, familiarisation: where participant learned how to use the armband, training: when participants reproduced a number of requested gestures to train our machine learning algorithm and recognition: when gestures presented on screen where reproduced by participants, and simultaneously recognised using the machine learning routines. Results: One participant did not complete the study due to technical errors during the session. The remaining (n = 25) participants completed the study allowing to calculate individual accuracy for grasp detection using this medium. Our overall accuracy was 65.06%, with the cylindrical grasp achieving the highest accuracy of around 7.20% and the tripod grasp achieving lowest recognition accuracy of 60.15%. Discussions: The recognition accuracy for the grasp performed is significantly lower compared to our earlier work where a mechatronic device was used. This could be due to the choice of grasps for this study, as it is not ideal to the placement of the armband. While tripod, cylindrical and lateral grasps have different finger and wrist articulations, their demand on supporting forearm muscles (mainly biceps and triceps) is less definite and therefore their myoelectric signals are less distinct. Furthermore, drop in accuracy could be caused by the fact that human muscles and consequently the myoelectric signals are substantially variable over time. Muscles change their relative intensity based on the speed of the produced gesture. In our earlier study, the gesture production speed was damped by the worn orthosis, leading to normalising the speed of gestures. This is while in our current study, hand motion is not restricted. Despite these, the recognition accuracy is still significant. Future work: There are remaining questions related to the feasibility of using myoelectric signals as an input to a remote controlled robot in a factory floor as it is anticipated that such a system would enhance control and efficiency in production processes. These questions therefore require further investigations regarding usability of the armband in its intended context, to ensure users are able to effectively control and manipulate the robot using the myoelectric system and enjoy a positive user experience. Future studies will focus on the choice of gestures, so that they are distinct and better identifiable, but also on other key human factors and system design features that will enhance performance, in compliance with relevant standards such as ISO 9241-210:2010 (standards for human-system interaction ergonomic design principles) . Furthermore, aspects of whether a machine learning algorithm should use individually learned events in order to recognise an individual’s gestures, or if it is possible to use normative representation of a substantial set of learnt events, to achieve higher accuracy remains an interesting area for our future work.Peer reviewe