Robot control of a 3,500 tonne mining machine

The mining industry is highly suitable for the application of robotics and automation technology since the work is arduous, dangerous and often repetitive. This paper describes the development of an automation system for a physically large and complex field robotic system - a 3,500 tonne mining machine (a dragline). The major components of the system are discussed with a particular emphasis on the machine/operator interface. A very important aspect of this system is that it must work cooperatively with a human operator, seamlessly passing the control back and forth in order to achieve the main aim - increased productivity.

Vision-based control for mining automation

The mining industry is highly suitable for the application of robotics and automation technology since the work is both arduous and dangerous. Visual servoing is a means of integrating noncontact visual sensing with machine control to augment or replace operator based control. This article describes two of our current mining automation projects in order to demonstrate some, perhaps unusual, applications of visual servoing, and also to illustrate some very real problems with robust computer vision

Rubbing shoulders with mobile service robots

This paper is not about the details of yet another robot control system, but rather the issues surrounding realworld robotic implementation. It is a fact that in order to realise a future where robots co-exist with people in everyday places, we have to pass through a developmental phase that involves some risk. Putting a “Keep Out, Experiment in Progress” sign on the door is no longer possible since we are now at a level of capability that requires testing over long periods of time in complex realistic environments that contain people. We all know that controlling the risk is important – a serious accident could set the field back globally – but just as important is convincing others that the risks are known and controlled. In this article, we describe our experience going down this path and we show that mobile robotics research health and safety assessment is still unexplored territory in universities and is often ignored. We hope that the article will make robotics research labs in universities around the world take note of these issues rather than operating under the radar to prevent any catastrophic accidents.

Sensors and control for mining robotics

This paper discusses some of the sensing technologies and control approaches available for guiding robot manipulators for a class of underground mining tasks including drilling jumbos, bolting arms, shotcreters or explosive chargers. Data acquired with such sensors, in the laboratory and underground, is presented.

Distributed Robotic Vision as a service

The vision sense of standalone robots is limited by line of sight and onboard camera capabilities, but processing video from remote cameras puts a high computational burden on robots. This paper describes the Distributed Robotic Vision Service, DRVS, which implements an on-demand distributed visual object detection service. Robots specify visual information requirements in terms of regions of interest and object detection algorithms. DRVS dynamically distributes the object detection computation to remote vision systems with processing capabilities, and the robots receive high-level object detection information. DRVS relieves robots of managing sensor discovery and reduces data transmission compared to image sharing models of distributed vision. Navigating a sensorless robot from remote vision systems is demonstrated in simulation as a proof of concept.

Open source computer-vision based guidance system for UAVs on-board decision making

The use of UAVs for remote sensing tasks; e.g. agriculture, search and rescue is increasing. The ability for UAVs to autonomously find a target and perform on-board decision making, such as descending to a new altitude or landing next to a target is a desired capability. Computer-vision functionality allows the Unmanned Aerial Vehicle (UAV) to follow a designated flight plan, detect an object of interest, and change its planned path. In this paper we describe a low cost and an open source system where all image processing is achieved on-board the UAV using a Raspberry Pi 2 microprocessor interfaced with a camera. The Raspberry Pi and the autopilot are physically connected through serial and communicate via MAVProxy. The Raspberry Pi continuously monitors the flight path in real time through USB camera module. The algorithm checks whether the target is captured or not. If the target is detected, the position of the object in frame is represented in Cartesian coordinates and converted into estimate GPS coordinates. In parallel, the autopilot receives the target location approximate GPS and makes a decision to guide the UAV to a new location. This system also has potential uses in the field of Precision Agriculture, plant pest detection and disease outbreaks which cause detrimental financial damage to crop yields if not detected early on. Results show the algorithm is accurate to detect 99% of object of interest and the UAV is capable of navigation and doing on-board decision making.

A Distributed Robotic Vision Service

Robotic vision is limited by line of sight and onboard camera capabilities. Robots can acquire video or images from remote cameras, but processing additional data has a computational burden. This paper applies the Distributed Robotic Vision Service, DRVS, to robot path planning using data outside line-of-sight of the robot. DRVS implements a distributed visual object detection service to distributes the computation to remote camera nodes with processing capabilities. Robots request task-specific object detection from DRVS by specifying a geographic region of interest and object type. The remote camera nodes perform the visual processing and send the high-level object information to the robot. Additionally, DRVS relieves robots of sensor discovery by dynamically distributing object detection requests to remote camera nodes. Tested over two different indoor path planning tasks DRVS showed dramatic reduction in mobile robot compute load and wireless network utilization.

A model eliciting framework for integrating mathematics and robotics learning

Robotics is taught in many Australian ICT classrooms, in both primary and secondary schools. Robotics activities, including those developed using the LEGO Mindstorms NXT technology, are mathematics-rich and provide a fertile round for learners to develop and extend their mathematical thinking. However, this context for learning mathematics is often under-exploited. In this paper a variant of the model construction sequence (Lesh, Cramer, Doerr, Post, & Zawojewski, 2003) is proposed, with the purpose of explicitly integrating robotics and mathematics teaching and learning. Lesh et al.’s model construction sequence and the model eliciting activities it embeds were initially researched in primary mathematics classrooms and more recently in university engineering courses. The model construction sequence involves learners working collaboratively upon product-focussed tasks, through which they develop and expose their conceptual understanding. The integrating model proposed in this paper has been used to design and analyse a sequence of activities in an Australian Year 4 classroom. In that sequence more traditional classroom learning was complemented by the programming of LEGO-based robots to ‘act out’ the addition and subtraction of simple fractions (tenths) on a number-line. The framework was found to be useful for planning the sequence of learning and, more importantly, provided the participating teacher with the ability to critically reflect upon robotics technology as a tool to scaffold the learning of mathematics.

Combining unsupervised and invigilated assessment of introductory programming

We compared student performance on large-scale take-home assignments and small-scale invigilated tests that require competency with exactly the same programming concepts. The purpose of the tests, which were carried out soon after the take home assignments were submitted, was to validate the students' assignments as individual work. We found widespread discrepancies between the marks achieved by students between the two types of tasks. Many students were able to achieve a much higher grade on the take-home assignments than the invigilated tests. We conclude that these paired assessments are an effective way to quickly identify students who are still struggling with programming concepts that we might otherwise assume they understand, given their ability to complete similar, yet more complicated, tasks in their own time. We classify these students as not yet being at the neo-Piagetian stage of concrete operational reasoning.

Automated Feedback for 'Fill in the Gap' Programming Exercises

Timely feedback is a vital component in the learning process. It is especially important for beginner students in Information Technology since many have not yet formed an effective internal model of a computer that they can use to construct viable knowledge. Research has shown that learning efficiency is increased if immediate feedback is provided for students. Automatic analysis of student programs has the potential to provide immediate feedback for students and to assist teaching staff in the marking process. This paper describes a “fill in the gap” programming analysis framework which tests students’ solutions and gives feedback on their correctness, detects logic errors and provides hints on how to fix these errors. Currently, the framework is being used with the Environment for Learning to Programming (ELP) system at Queensland University of Technology (QUT); however, the framework can be integrated into any existing online learning environment or programming Integrated Development Environment (IDE)