Dragline automation

This paper examines the feasibility of automation of dragline bucket excavators used to strip over-burden from open cut mines. In particular the automatic control of bucket carry angle and bucket trajectory are addressed. Open-loop dynamics of a 1:20 scale model dragline bucket are identified, through measurement of frequency response between carry angle and drag motor input voltage. A strategy for automatic control of carry angle is devised and implemented using bucket angle and rate feedback. System compensation and tuning are explained and closed loop frequency and time responses are measured.

Autonomous control of underground mining vehicles using reactive navigation

Describes how many of the navigation techniques developed by the robotics research community over the last decade may be applied to a class of underground mining vehicles (LHDs and haul trucks). We review the current state-of-the-art in this area and conclude that there are essentially two basic methods of navigation applicable. We describe an implementation of a reactive navigation system on a 30 tonne LHD which has achieved full-speed operation at a production mine.

Modelling and control of a large quadrotor robot

Typical quadrotor aerial robots used in research weigh inlMMLBox and carry payloads measured in hundreds of grams. Several obstacles in design and control must be overcome to cater for expected industry demands that push the boundaries of existing quadrotor performance. The X-4 Flyer, a 4 kg quadrotor with a 1 kg payload, is intended to be prototypical of useful commercial quadrotors. The custom-built craft uses tuned plant dynamics with an onboard embedded attitude controller to stabilise flight. Independent linear SISO controllers were designed to regulate flyer attitude. The performance of the system is demonstrated in indoor and outdoor flight.

Modelling and control of a quad-rotor robot

To date, most quad-rotor aerial robots have been based on flying toys. Although such systems can be used as prototypes, they are not sufficiently robust to serve as experimental robotics platforms. We have developed the X-4 Flyer, a quad-rotor robot using custom-built chassis and avionics with off-the-shelf motors and batteries, to be a highly reliable experimental platform. The vehicle uses tuned plant dynamics with an onboard embedded attitude controller to stabilise flight. A linear SISO controller was designed to regulate flyer attitude.

System identification and control of an aerobot drive system

Fast thrust changes are important for authoritive control of VTOL micro air vehicles. Fixed-pitch rotors that alter thrust by varying rotor speed require high-bandwidth control systems to provide adequate performace. We develop a feedback compensator for a brushless hobby motor driving a custom rotor suitable for UAVs. The system plant is identified using step excitation experiments. The aerodynamic operating conditions of these rotors are unusual and so experiments are performed to characterise expected load disturbances. The plant and load models lead to a proportional controller design capable of significantly decreasing rise-time and propagation of disturbances, subject to bus voltage constraints.

Experiments with cooperative networked control of underwater robots

We present details and results obtained with an underwater system comprising two different autonomous underwater robots (AUV) and ten static underwater nodes (USN) networked together optically and acoustically. The AUVs can locate and hover above the static nodes for data upload, and they can perform network maintenance functions such as deployment, relocation, and recovery. The AUVs can also locate each other, dock, and move using coordinated control that takes advantage of each AUV’s strength.

DDX : A distributed software architecture for robotic systems

The Dynamic Data eXchange (DDX) is our third generation platform for building distributed robot controllers. DDX allows a coalition of programs to share data at run-time through an efficient shared memory mechanism managed by a store. Further, stores on multiple machines can be linked by means of a global catalog and data is moved between the stores on an as needed basis by multi-casting. Heterogeneous computer systems are handled. We describe the architecture of DDX and the standard clients we have developed that let us rapidly build complex control systems with minimal coding.

Experiments in learning helicopter control from a pilot

This paper details the development of a machine learning system which uses the helicopter state and the actions of an instructing pilot to synthesise helicopter control modules online. Aggressive destabilisation/restabilisation sequences are used for training, such that a wide state space envelope is covered during training. The performance of heading, roll, pitch, height and lateral velocity control learning is presented using our Xcell 60 experimental platform. The helicopter is demonstrated to be stabilised on all axes using the “learning from a pilot” technique. To our knowledge, this is the first time a “learning from a pilot” technique has been successfully applied to all axes.

Active flow control bump design using hybrid Nash-game coupled to evolutionary algorithms

In this paper, the optimal design of an active flow control device; Shock Control Bump (SCB) on suction and pressure sides of transonic aerofoil to reduce transonic total drag is investigated. Two optimisation test cases are conducted using different advanced Evolutionary Algorithms (EAs); the first optimiser is the Hierarchical Asynchronous Parallel Evolutionary Algorithm (HAPMOEA) based on canonical Evolutionary Strategies (ES). The second optimiser is the HAPMOEA is hybridised with one of well-known Game Strategies; Nash-Game. Numerical results show that SCB significantly reduces the drag by 30% when compared to the baseline design. In addition, the use of a Nash-Game strategy as a pre-conditioner of global control saves computational cost up to 90% when compared to the first optimiser HAPMOEA.

Design Optimisation of UAVs Systems achieved on a Framework Environment via Evolution and Game Theory

Over recent years, Unmanned Air Vehicles or UAVs have become a powerful tool for reconnaissance and surveillance tasks. These vehicles are now available in a broad size and capability range and are intended to fly in regions where the presence of onboard human pilots is either too risky or unnecessary. This paper describes the formulation and application of a design framework that supports the complex task of multidisciplinary design optimisation of UAVs systems via evolutionary computation. The framework includes a Graphical User Interface (GUI), a robust Evolutionary Algorithm optimiser named HAPEA, several design modules, mesh generators and post-processing capabilities in an integrated platform. These population –based algorithms such as EAs are good for cases problems where the search space can be multi-modal, non-convex or discontinuous, with multiple local minima and with noise, and also problems where we look for multiple solutions via Game Theory, namely a Nash equilibrium point or a Pareto set of non-dominated solutions. The application of the methodology is illustrated on conceptual and detailed multi-criteria and multidisciplinary shape design problems. Results indicate the practicality and robustness of the framework to find optimal shapes and trade—offs between the disciplinary analyses and to produce a set of non dominated solutions of an optimal Pareto front to the designer.

Visual control of robots : high-performance visual servoing

The application of high-speed machine vision for close-loop position control, or visual servoing, of a robot manipulator. It provides a comprehensive coverage of all aspects of the visual servoing problem: robotics, vision, control, technology and implementation issues. While much of the discussion is quite general the experimental work described is based on the use of a high-speed binary vision system with a monocular "eye-in-hand" camera.

Exercise, appetite control, and energy balance

Given the present worldwide epidemic of obesity, it is pertinent to ask how effective exercise could be in helping people to lose weight or to prevent weight gain. There is a widely held belief that exercise is futile for weight reduction because any energy expended in exercise is automatically compensated for by a corresponding increase in energy intake (EI). In other words, exercise elevates the intensity of hunger and drives food consumption. This “commonsense” view appears to originate in an energy-balance model of appetite control, which stipulates that energy expended will drive EI as a consequence of the regulation of energy balance. However, it is very clear that EI (food consumption or eating) is not just a biological matter. Eating does not occur solely to rectify some internal need state. Indeed, an examination of the relation between exercise and appetite control has shown a very weak coupling; most studies have demonstrated that food intake does not immediately rise after exercise, even after very high energy expenditure (EE).[1] The processes of exercise-induced EE and food consumption do not appear to be tightly linked. After exercise, there is only slow and partial compensation for the energy expended. Therefore, exercise can be very useful in helping to bring about weight loss and is even more important in preventing weight gain or weight regain. This editorial explores this issue.

Analytical modeling and sensitivity analysis for travel time estimation on signalized urban networks

This paper presents a model for estimation of average travel time and its variability on signalized urban networks using cumulative plots. The plots are generated based on the availability of data: a) case-D, for detector data only; b) case-DS, for detector data and signal timings; and c) case-DSS, for detector data, signal timings and saturation flow rate. The performance of the model for different degrees of saturation and different detector detection intervals is consistent for case-DSS and case-DS whereas, for case-D the performance is inconsistent. The sensitivity analysis of the model for case-D indicates that it is sensitive to detection interval and signal timings within the interval. When detection interval is integral multiple of signal cycle then it has low accuracy and low reliability. Whereas, for detection interval around 1.5 times signal cycle both accuracy and reliability are high.