A graph-theory-based C-space path planner for mobile robotic manipulators in close-proximity environments

In this thesis a novel guidance method for a 3-degree-of-freedom robotic manipulator arm in 3 dimensions for Improvised Explosive Device (IED) disposal has been developed. The work carried out in this thesis combines existing methods to develop a technique that delivers advantages taken from several other guidance techniques. These features are necessary for the IED disposal application. The work carried out in this thesis includes kinematic and dynamic modelling of robotic manipulators, T-space to C-space conversion, and path generation using Graph Theory to produce a guidance technique which can plan a safe path through a complex unknown environment. The method improves upon advantages given by other techniques in that it produces a suitable path in 3-dimensions in close-proximity environments in real time with no a priori knowledge of the environment, a necessary precursor to the application of this technique to IED disposal missions. To solve the problem of path planning, the thesis derives the kinematics and dynamics of a robotic arm in order to convert the Euclidean coordinates of measured environment data into C-space. Each dimension in C-space is one control input of the arm. The Euclidean start and end locations of the manipulator end effector are translated into C-space. A three-dimensional path is generated between them using Dijkstra’s Algorithm. The technique allows for a single path to be generated to guide the entire arm through the environment, rather than multiple paths to guide each component through the environment. The robotic arm parameters are modelled as a quasi-linear parameter varying system. As such it requires gain scheduling control, thus allowing compensation of the non-linearities in the system. A Genetic Algorithm is applied to tune a set of PID controllers for the dynamic model of the manipulator arm so that the generated path can then be followed using a conventional path-following algorithm. The technique proposed in this thesis is validated using numerical simulations in order to determine its advantages and limitations

Intelligent Based Terrain Preview Controller for a 3-axle Vehicle

Presented at 13th International Symposium on Advanced Vehicle Control, AVEC'16; Munich 13-16/09/2016The paper presents a six-wheel half longitudinal model and the design of a dual level control architecture. The first (top) level is designed using a Sugeno fuzzy inference feedforward architecture with and without preview. The second level of controllers are locally managing each wheel for each axle. As the vehicle is moving forward the front wheels and suspension units will have less time to respond when compared to the middle and rear units, hence a preview sensor is used to compensate. The paper shows that the local active suspensions together with the Sugeno Fuzzy, (locally optimised using subtractive clustering), Feedforward control strategy is more effective and this architecture has resulted in reducing the sprung mass vertical acceleration and pitch accelerations

Quasi-real-time confined environment path generation for mobile robotic manipulator arms

Path generation for mobile robotic manipulator arms is challenging in dynamic environments because high-speed calculations are required to deal with fast-moving obstacles. A novel path-planning algorithm has been developed which solves in quasi-real time the problem of path generation in confined environments for interconnected multi-body systems, specifically a robotic manipulator arm with three links. The work presented in this article builds upon the previous work by reformulating the technique to increase the speed at which the algorithm is able to calculate a safe path. The complexity of the task space has increased substantially compared to previous work, and the algorithm has been reformulated to speed up the calculation in order to maintain or even improve its ability to plan a safe path in real time. The method is now able to calculate a safe path through environments significantly more quickly than the previous method, and the results presented in this article expand the complexity of the environment by a large amount and test the ability of the reformulated algorithm to still operate in real time, which the method achieves. It was found that the reformulated method reduces the calculation time for path generation exponentially when used to plan safe paths through test environments involving different numbers of obstacles. The new algorithm thus has the potential to facilitate path planning in challenging dynamic environments, such as those used in sensitive manufacturing and maintenance tasks as well as bomb disposal and similar applications

Subtractive clustering Takagi-Sugeno position tracking for humans by low-cost inertial sensors and velocity classification

In this work, open-loop position tracking using low-cost inertial measurement units is aided by Takagi-Sugeno velocity classification using the subtractive clustering algorithm to help generate the fuzzy rule base. Using the grid search approach, a suitable window of classified velocity vectors was obtained and then integrated to generate trajectory segments. Using publicly available experimental data, the reconstruction accuracy of the method is compared against four competitive pedestrian tracking algorithms. The comparison on selected test data, has demonstrated more competitive relative and absolute trajectory error metrics. The proposed method in this paper is also verified on an independent experimental data set. Unlike the methods which use deep learning, the proposed method has shown to be transparent (fuzzy rule base). Lastly, a sensitivity analysis of the velocity classification models to perturbations from the training orientation at test time is investigated, to guide developers of such data-driven algorithms on the granularity required in an ensemble modelling approach. The accuracy and transparency of the approach may positively influence applications requiring low-cost inertial position tracking such as augmented reality headsets for emergency responders.Engineering and Physical Sciences Research Council (EPSRC): EP/S513623/1 BAE System

Hypersurface normalised gain-scheduled controller for a non-linear 6-DOF fast jet

This paper describes a novel approach for improving the dynamic response of a bank-to-turn autopilot for a non-linear six degree-of-freedom (6-DoF) aircraft model. The autopilot consists of a series of gain-scheduled (GS) proportional, integral and derivative (PID) controllers that govern the aircraft's angular velocities for roll, pitch and yaw. The controller gains have been optimised for localised trim points and applied continuously to the controllers using linear interpolation to form a hypersurface. Our novel solution has been achieved by implementing a set of scheduled gains for near-zero reference signals and integrating this with a set of gains that are normalised to the reference signal. The proposed approach has been compared to conventional gain scheduling techniques using a series of step input simulated manoeuvres, applied individually to the roll and pitch controllers. The results show improved rise and fall times, steady state errors, as well as reduced controller effor

H∞/LQR optimal control for a supersonic air-breathing missile of asymmetric configuration

Robust control is challenging to achieve for air-breathing missiles operating in a high Mach number regime, such as at high supersonic speeds (M > 3). The challenge arises because of strong couplings, significant non-linearities and large uncertainties in the aerodynamics and propulsion system. The feasibility of achieving robust control in such applications is strongly linked to the development of an appropriate control design structure. The purpose of this paper is to illustrate that in order to stabilise a highly unstable airframe and achieve the required performance, a hybrid of two control schemes may be used to achieve best results. A state feedback linear quadratic regulator is used to stabilise the plant and a forward path H∞ optimal controller is used to achieve the required performance and robustness. We also highlight the complementary attributes of the two control schemes that together can generate a more robust controller; LQR is used since it can achieve good gain and phase margins, whereas, the H∞ control method is better equipped to deal with uncertainties

Indirect tuning of a complementary orientation filter using velocity data and a genetic algorithm

In this paper, the accuracy of inertial sensor orientation relative to the level frame is improved through optimal tuning of a complementary filter by a genetic algorithm. While constant filter gains have been used elsewhere, these may introduce errors under dynamic motions when gyroscopes should be trusted more than accelerometers. Optimal gains are prescribed by a Mamdani fuzzy rule base whose membership functions are found using a genetic algorithm and experimental data. Furthermore, model fitness is not based directly on orientation but the error between estimated and ground truth velocities. This paper has three interrelated novel elements. The main novelty is the indirect tuning method, which is simple, low-cost and requires a single camera and inertial sensor. The method is shown to increase tracking accuracy compared with popular baseline filters. Secondary novel elements are the bespoke genetic algorithm and the time agnostic velocity error metric. The contributions from this work can help improve the localization accuracy of assets and human personnel. This research has a direct impact in command and control by improving situational awareness and the ability to direct assets to safe locations using safer routes. This results in increasing safety in applications such as firefighting and battlespace.This research is funded by the Engineering and Physical Sciences Research Council (EPSRC) iCASE Grant reference EP/S513623/1 and BAE Systems

Reduced Cardiovascular Capacity and Resting Metabolic Rate in Men with Prostate Cancer Undergoing Androgen Deprivation: A Comprehensive Cross-Sectional Investigation

Objectives. To investigate if androgen deprivation therapy exposure is associated with additional risk factors for cardiovascular disease and metabolic treatment-related toxicities. Methods. One hundred and seven men (42-89 years) with prostate cancer undergoing androgen deprivation therapy completed a maximal graded objective exercise test to determine maximal oxygen uptake, assessments for resting metabolic rate, body composition, blood pressure and arterial stiffness, and blood biomarker analysis. A cross-sectional analysis was undertaken to investigate the potential impact of therapy exposure with participants stratified into two groups according to duration of androgen deprivation therapy (<3 months and ≥3 months). Results. Maximal oxygen uptake (26.1 ± 6.0 mL/kg/min versus 23.2 ± 5.8 mL/kg/min, = 0.020) and resting metabolic rate (1795 ± 256 kcal/d versus 1647 ± 236 kcal/d, = 0.005) were significantly higher in those with shorter exposure to androgen deprivation. There were no differences between groups for peripheral and central blood pressure, arterial stiffness, or metabolic profile. Conclusion. Three months or longer exposure to androgen deprivation therapy was associated with reduced cardiorespiratory capacity and resting metabolic rate, but not in a range of blood biomarkers. These findings suggest that prolonged exposure to androgen deprivation therapy is associated with negative alterations in cardiovascular outcomes. Trial registry is: ACTRN12609000200280

Weight loss for overweight and obese patients with prostate cancer: A study protocol of a randomised trial comparing clinic-based versus telehealth delivered exercise and nutrition intervention (the TelEX trial)

Introduction Obese men with prostate cancer have an increased risk of biochemical recurrence, metastatic disease and mortality. For those undergoing androgen deprivation therapy (ADT), substantial increases in fat mass are observed in the first year of treatment. Recently, we showed that a targeted supervised clinic-based exercise and nutrition intervention can result in a substantial reduction in fat mass with muscle mass preserved in ADT-treated patients. However, the intervention needs to be accessible to all patients and not just those who can access a supervised clinic-based programme. The purpose of this study was to evaluate the efficacy of telehealth delivered compared with supervised clinic-based delivered exercise and nutrition intervention in overweight/obese patients with prostate cancer. Methods and analysis A single-blinded, two-arm parallel group, non-inferiority randomised trial will be undertaken with 104 overweight/obese men with prostate cancer (body fat percentage ≥ 25%) randomly allocated in a ratio of 1:1 to a telehealth-delivered, virtually supervised exercise and nutrition programme or a clinic-based, face-to-face supervised exercise and nutrition programme. Exercise will consist of supervised resistance and aerobic exercise performed three times a week plus additional self-directed aerobic exercise performed 4 days/week for the first 6 months. Thereafter, for months 7-12, the programmes will be self-managed. The primary endpoint will be fat mass. Secondary endpoints include lean mass and abdominal aortic calcification, anthropometric measures and blood pressure assessment, objective measures of physical function and physical activity levels, patient-reported outcomes and blood markers. Measurements will be undertaken at baseline, 6 months (post intervention), and at 12 months of follow-up. Data will be analysed using intention-to-treat and per protocol approaches. Ethics and dissemination Ethics approval has been obtained from the Edith Cowan University Human Research Ethics Committee (ID: 2021-02157-GALVAO). Outcomes from the study will be published in academic journals and presented in scientific and consumer meetings. Trial registration number: ACTRN12621001312831

A phase III clinical trial of exercise modalities on treatment side-effects in men receiving therapy for prostate cancer

Background: Androgen deprivation therapy (ADT) is accompanied by a number of adverse side effects including reduced bone mass and increased risk for fracture, reduced lean mass and muscle strength, mood disturbance and increased fat mass compromising physical functioning, independence, and quality of life. The purpose of this investigation is to examine the effects of long term exercise on reversing musculoskeletal-related side effects, and cardiovascular and diabetes risk factors in men receiving androgen deprivation for their prostate cancer. Specifically, we aim to investigate the effects of a 12-month exercise program designed to load the musculoskeletal system and reduce cardiovascular and diabetes disease progression on the following primary endpoints: 1) bone mineral density; 2) cardiorespiratory function and maximal oxygen capacity; 3) body composition (lean mass and fat mass); 4) blood pressure and cardiovascular function; 5) lipids and glycemic control; and 6) quality of life and psychological distress