CERES :: Browsing by Author "Savvaris, Al"

Browsing by Author "Savvaris, Al"

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Open Access
Advanced surface movement guidance and control system investigation and implementation in simulation
(Cranfield University, 2010) Ding, Ting; Savvaris, Al
The Surface Movement Guidance and Control System (SMGCS) is a system providing the surveillance, routing, guidance and control supports to the airport traffic. The moving objects being managed include all the aircraft and vehicles in the interested area on the surface; the personnel making use of this system are the pilots, vehicle drivers, and ground controllers. The airport surface traffic management has long been discussed because of the operational challenges; this includes the increasing complexity of the field movement management and the density of airport traffic. To improve airport operation qualities, the Advanced Surface Movement Control and Guidance System (A-SMGCS) was introduced. In terms of architecture and capability differences, there are two levels of the A-SMGCS, which are A-SMGCS I & II. The positive impacts on the airport surface operation are: safety, capacity, efficiency, human factor conditions, and economic issues. This project deals with an investigation on SMGCS baseline and the A-SMGCS, covering the system conception, background, current developments and relative technologies. The applications in practical operations are discussed as well. There is also an analysis about the airport surface incursion classification and severity. Based on this, a simulation is presented to illustrate the practical applications of the A-SMGCS. The simulation results show the functions of Human Machine Interface (HMI) in A-SMGCS, including the designation and diversion for clearance, the real-time view of surface target movements and the indications for contracted incursions. Over all, the research aims are to work on an investigation and explanation of A-SMGCS, and to implement a simulation of the system functions. The implementation includes the image processing, system architecture definition in Simulink, Graphical User Interface (GUI) design for the HMI, and the corresponding Matlab programming for simulation environment establishment.
Open Access
Aircraft head-up display surface guidance system
(Cranfield University, 2013-11) Gu, Jinxin; Savvaris, Al
The continues growth in aviation and passenger numbers is putting more pressure on airports to become more efficient in order to reduce the number of delays due to external factors such as weather, pilot deviation/errors and airport maintenance traffic. As major hubs (e.g. Heathrow, New York or Paris) expand in size to accommodate more traffic; aircraft surface movement and management become more complex and the margin for error is even lower. The traditional airport traffic management tools in large airports are increasingly stretched to the limit in meeting safety and traffic throughput requirements. This presents a huge challenge to the efficiency of airport operations because of the increased number of departures and arrivals at those airports. New technology for surface movement needs to be implemented in order to increase the safety and airport capacity. The federal aviation authorities in the USA was first to introduce the concept of Advanced Surface Movement Guidance and Control System (A-SMGCS) to address this problem in commercial airdrome operations. The system facilitates pilot recognition of the route designated by the traffic controllers and uses warning information to make them aware of any potential deviations/incursions. The system is introduced to enhance the efficiency of surface movement by increasing the aircraft taxiing speed and reducing any pilot errors during bad weather conditions. This thesis focuses on the surface guidance system for aircraft equipped with head-up display. A simulation model of the virtual environment using FlightGear and Simulink is developed based on the study of a moving map and surface guidance system for Head-Up Display (HUD) to assign the route, guide the aircraft on the designated taxiway and avoid potential conflict with other aircraft. A method of generating an airport in FlightGear and driving an airport moving map to rotate and move is also illustrated which includes the data processing flow chart and system flow chart. The Ordnance Survey National Grid and world coordinate system is discussed and used to transform from GPS latitude and longitude data to the position on Nation Grid. There is also an explanation of the 3D viewing process to generate the virtual taxiway geometries on the HUD. The communication between the traffic console and airplane is also discussed.
Open Access
Aircraft maintenance and development of a performance-based creep life estimation for aero engine
(Cranfield University, 2012-01) Toufexis, Dimitrios; Savvaris, Al
For any machine designed to generate power, or to fulfill its functions in general, maintenance actions will have an impact on many aspects of its overall capabilities, especially its performance and the length of its useful life. Since these are vital in order to generate maximum profit, the maintenance actions that affect them must be given serious consideration. For this reason, this research aims to propose a method that will enhance the cost saving potential with more accurately determined maintenance intervals and greater exploitation of the remaining life of the components by utilizing the capabilities of condition based monitoring. Initially, the research focuses on the description and the understanding of maintenance methods as they are performed within the aviation industry, but it also aims to investigate the state of the art Condition Based Monitoring Maintenance (CBMM) and its associated advantaged relating to the older methods. The thesis begins by describing the fundamental aviation maintenance management domains, paying particular attention to CBMM, and continues with the diagnostic and prognostic methods that are in use in order to support the condition monitoring concept. Next, a description is given of the actual implementations of the CBMM process, with the presentation of the maintenance enhancement systems, namely the Central Maintenance System and the Aircraft Condition Monitoring System. Lastly, a case study is presented of the estimation of the remaining useful life of a turbine blade, as it relates to the primary failure mode of creep. The case study endorses the use of the condition monitoring diagnostic methods discussed previously and also aims to demonstrate the predictive capabilities of the Engine Usage Diagnostics at both the design and the into-service stage. The created/simulated engine performance models concern several operating conditions of the engine while the impact of each of those on the remaining useful life of the blade is investigated. The benefit of this research is that it proposes a practical, effective, and relatively easy way to perform maintenance by predicting the need according to the usage. Additionally, the data required have already been measured, which paves the way for the creation of more intelligent engine control units. The contribution and innovation of the research is demonstrated by the fact that no similar approaches to creep life prediction have been published for the same type of engine, namely the CFM56 5B2. Last but not least, the results are presented in the most beneficial form of remaining hours before the failure.
Open Access
Analysis of optimization strategies for solving space manoeuvre vehicle trajectory optimization problem
(Springer, 2017-12-15) Chai, Runqi; Savvaris, Al; Tsourdos, Antonios
In this paper, two types of optimization strategies are applied to solve the Space Manoeuvre Vehicle (SMV) trajectory optimization problem. The SMV dynamic model is constructed and discretized applying direct multiple shooting method. To solve the resulting Nonlinear Programming (NLP) problem, gradient-based and derivative free optimization techniques are used to calculate the optimal time history with respect to the states and controls. Simulation results indicate that the proposed strategies are effective and can provide feasible solutions for solving the constrained SMV trajectory design problem.
Open Access
Artificial intelligence to enhance aerodynamic shape optimisation of the Aegis UAV
(MDPI, 2019-04-04) Azabi, Yousef; Savvaris, Al; Kipouros, Timoleon
This article presents an optimisation framework that uses stochastic multi-objective optimisation, combined with an Artificial Neural Network (ANN), and describes its application to the aerodynamic design of aircraft shapes. The framework uses the Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm and the obtained results confirm that the proposed technique provides highly optimal solutions in less computational time than other approaches to the same design problem. The main idea was to focus computational effort on worthwhile design solutions rather than exploring and evaluating all possible solutions in the design space. It is shown that the number of valid solutions obtained using ANN-MOPSO compared to MOPSO for 3000 evaluations grew from 529 to 1006 (90% improvement) with a penalty of only 8.3% (11 min) in computational time. It is demonstrated that including an ANN, the ANN-MOPSO with 3000 evaluations produced a larger number of valid solutions than the MOPSO with 5500 evaluations, and in 33% less computational time (64 min). This is taken as confirmation of the potential power of ANNs when applied to this type of design problem.
Open Access
Artificial neural network control strategies for fuel cell hybrid system
(Cranfield University, 2013-05) Oheda, Hakim; Savvaris, Al
The greening of air transport is the driver for developing technologies to reduce the environmental impact of aviation with the aim of halving the amount of carbon dioxide (COଶ) emitted by air transport, cutting specific emissions of nitrogen oxides (NO୶) by 80% and halving perceived noise by the year 2020. Fuel Cells (FC) play an important role in the new power generation field as inherently clean, efficient and reliable source of power especially when comparing with the traditional fossil-fuel based technologies. The project investigates the feasibility of using an electric hybrid system consisting of a fuel cell and battery to power a small model aircraft (PiperCub J3). In order to meet the desired power requirements at different phases of flight efficiently, a simulation model of the complete system was first developed, consisting of a Proton Exchange Membrane hybrid fuel cell system, 6DoF aircraft model and neural network based controller. The system was then integrated in one simulation environment to run in real-time and finally was also tested in hardware-in-the-loop with real-time control. The control strategy developed is based on a neural network model identification technique; specifically Model Reference Control (MRC), since neural network is well suited to nonlinear systems. To meet the power demands at different phases of flight, the controller controls the battery current and rate of charging/discharging. Three case studies were used to validate and assess the performance of the hybrid system: battery fully charged (high SOC), worst case scenario and taking into account the external factors such as wind speeds and wind direction. In addition, the performance of the Artificial Neural Network Controller was compared to that of a Fuzzy Logic controller. In all cases the fuel cell act as the main power source for the PiperCub J3 aircraft. The tests were carried-out in both simulation and hardware-in-the-loop.
Open Access
Automatic Pipeline Surveillance Air-Vehicle
(Cranfield University, 2016-02) Alqaan, Hani; Savvaris, Al
This thesis presents the developments of a vision-based system for aerial pipeline Right-of-Way surveillance using optical/Infrared sensors mounted on Unmanned Aerial Vehicles (UAV). The aim of research is to develop a highly automated, on-board system for detecting and following the pipelines; while simultaneously detecting any third-party interference. The proposed approach of using a UAV platform could potentially reduce the cost of monitoring and surveying pipelines when compared to manned aircraft. The main contributions of this thesis are the development of the image-analysis algorithms, the overall system architecture and validation of in hardware based on scaled down Test environment. To evaluate the performance of the system, the algorithms were coded using Python programming language. A small-scale test-rig of the pipeline structure, as well as expected third-party interference, was setup to simulate the operational environment and capture/record data for the algorithm testing and validation. The pipeline endpoints are identified by transforming the 16-bits depth data of the explored environment into 3D point clouds world coordinates. Then, using the Random Sample Consensus (RANSAC) approach, the foreground and background are separated based on the transformed 3D point cloud to extract the plane that corresponds to the ground. Simultaneously, the boundaries of the explored environment are detected based on the 16-bit depth data using a canny detector. Following that, these boundaries were filtered out, after being transformed into a 3D point cloud, based on the real height of the pipeline for fast and accurate measurements using a Euclidean distance of each boundary point, relative to the plane of the ground extracted previously. The filtered boundaries were used to detect the straight lines of the object boundary (Hough lines), once transformed into 16-bit depth data, using a Hough transform method. The pipeline is verified by estimating a centre line segment, using a 3D point cloud of each pair of the Hough line segments, (transformed into 3D). Then, the corresponding linearity of the pipeline points cloud is filtered within the width of the pipeline using Euclidean distance in the foreground point cloud. Then, the segment length of the detected centre line is enhanced to match the exact pipeline segment by extending it along the filtered point cloud of the pipeline. The third-party interference is detected based on four parameters, namely: foreground depth data; pipeline depth data; pipeline endpoints location in the 3D point cloud; and Right-of-Way distance. The techniques include detection, classification, and localization algorithms. Finally, a waypoints-based navigation system was implemented for the air- vehicle to fly over the course waypoints that were generated online by a heading angle demand to follow the pipeline structure in real-time based on the online identification of the pipeline endpoints relative to a camera frame.
Open Access
Autonomous Collision avoidance for Unmanned aerial systems
(Cranfield University, 2014) Melega, Marco; Savvaris, Al; Tsourdos, Antonios
Unmanned Aerial System (UAS) applications are growing day by day and this will lead Unmanned Aerial Vehicle (UAV) in the close future to share the same airspace of manned aircraft.This implies the need for UAS to define precise safety standards compatible with operations standards for manned aviation. Among these standards the need for a Sense And Avoid (S&A) system to support and, when necessary, sub¬stitute the pilot in the detection and avoidance of hazardous situations (e.g. midair collision, controlled flight into terrain, flight path obstacles, and clouds). This thesis presents the work come out in the development of a S&A system taking into account collision risks scenarios with multiple moving and fixed threats. The conflict prediction is based on a straight projection of the threats state in the future. The approximations introduced by this approach have the advantage of high update frequency (1 Hz) of the estimated conflict geometry. This solution allows the algorithm to capture the trajectory changes of the threat or ownship. The resolution manoeuvre evaluation is based on a optimisation approach considering step command applied to the heading and altitude autopilots. The optimisation problem takes into account the UAV performances and aims to keep a predefined minimum separation distance between UAV and threats during the resolution manouvre. The Human-Machine Interface (HMI) of this algorithm is then embedded in a partial Ground Control Station (GCS) mock-up with some original concepts for the indication of the flight condition parameters and the indication of the resolution manoeuvre constraints. Simulations of the S&A algorithm in different critical scenarios are moreover in-cluded to show the algorithm capabilities. Finally, methodology and results of the tests and interviews with pilots regarding the proposed GCS partial layout are covered.
Open Access
A comparison between guidance laws for AUVs using relative kinematics
(IEEE, 2017-10-26) Bilale, Abudureheman; Savvaris, Al; Tsourdos, Antonios
This paper presents a comparison between three popular guidance laws for path following of autonomous underwater vehicles: switching enclosure-based Line-Of-Sight (LOS), lookahead-based LOS, and vector field guidance laws. The equations of motion employ the concept of the relative kinematics, and a nonlinear controller is applied together with the guidance systems during path-following. The optimal tuning values for each guidance are selected using the Pareto efficiencies from multiple simulations in terms of providing low cross-track error and control effort. Performance analysis are carried out for a waypoint following scenario both with and without significant constant and irrotational ocean currents as disturbances. Simulation results are also presented using the model of an AUV.
Open Access
Convexification in energy optimization of a hybrid electric propulsion system for aerial vehicles
(Elsevier, 2022-03-30) Xie, Ye; He, Shaoming; Savvaris, Al; Tsourdos, Antonios; Zhang, Dan; Xie, Anhuan
This paper concerns the energy management of a hybrid electric propulsion system for aerial vehicles, using convex optimization. The main contribution of this paper is the proposal of a new convexification, which simplifies the formation of the convexified problem, and the proof of equality between the original problem and the convexified problem. The primary energy management is formulated from first principles and using experimental data. The convexity of the original problem is clarified via investigating the approximation to the experimental data. Then, change of variables and equality relaxation are implemented to convexify the concave constraints. The introduced variable—battery internal energy, is proposed to convexify the battery model. The relaxation of a non-affine equality yields to new convex inequality constraints. Numerical examples and forward simulations were carried out to validate the convexified problem. The first test case verifies that the convex relaxation does not sacrifice the optimality of the solution nor does the variable change lose the original bounds. Also, the optimal control from convex optimization is demonstrated to be robust to a disturbance in power demand. Comparison with the benchmark optimization—dynamic programming, shows that convex optimization achieves a minimal objective value with less fluctuation of the optimal control value. Most significant is that the convexification reduces the optimization computation time to a level compatible with implementation in practical application.
Open Access
Design and development of a novel spherical UAV
(Elsevier, 2016-10-03) Malandrakis, Konstantinos; Dixon, Roland; Savvaris, Al; Tsourdos, Antonios
This paper presents the design and system integration of a novel coaxial, flap actuated, spherical UAV for operations in complex environments, such as buildings, caves or tunnels. The spherical design protects the inner components of the vehicle and allows the UAV to roll along the floor if the environment permits. Furthermore, the UAV can land and takeoff from any orientation and come into contact with objects without putting the propellers at risk. Flaps at the base of the sphere will generate roll and pitch moments as opposed to conventional swash plate designs while the coaxial setup will provide the necessary yaw moments and increase in thrust to volume ratio of the system. The flaps, placed below the propellers allow for decoupled roll and pitch control in a thrust vectoring manner. The final result of this design is a well-protected, compact, easily controlled, flexible and agile UAV for operations in complex environments. The spherical UAV was successfully flight tested on a number of occasions with various PD and µ-synthesis robust control systems and was observed to be easily stabilised and resistant to external disturbances to certain extent.
Open Access
Design and energy management of aircraft hybrid electric propulsion system.
(2018-09) Xie, Ye; Savvaris, Al; Tsourdos, Antonios
This thesis investigates the design and development of a Hybrid Electric Propulsion System (HEPS) for aircraft. The main contributions of the study are the multi-objective system sizing and the two energy optimization algorithms. First, the system sizing method is employed to design the hybrid electric propulsion system for a prototype aircraft. The sized hybrid propulsion system can ensure that no significant performance is sacrificed and the fuel economy is improved. The novel approach in this work is a new non-dominated sorting algorithm for the Non-dominated Sorting Genetic Algorithm (NSGA). The new algorithm can improve the time complexity of non-dominated sorting process. The optimized hybrid aircraft can save up to 17% fuel, achieve higher cruising speed and rate of climb. It is concluded that the optimal results are more sensitive to the variation of battery energy density than other parameters. Next, the main components of the HEPS are modelled for example. The engine model provides an insight into the inherent relationship between the throttle command and the output torque. Regarding the d-q model of motor/generator, the estimation of torque loss at steady state is achieved using the efficiency map from experiments. The application of Shepherd model leads to the straightforward parameter identification. In this research, both non-causal and causal energy management strategies for HEPS are investigated. The main novelty when studying convex optimization is the proposal of a new lossless convexification, which simplifies the formation of the convexified problem, and the proof of equality between the original problem and convexified problem. The introduced variable—battery internal energy, is proposed to convexify the battery model. The first test case verifies that the convex relaxation does not sacrifice the optimality of the solution nor does the variable change lose the original bounds. Also, the optimal control from convex optimization is demonstrated to be robust to a disturbance in power demand. Comparison with the benchmark optimization—dynamic programming, shows that convex optimization achieves a minimal objective value with much less optimization time. Most significant is that the convexification reduces the optimization computation time to a level compatible with implementation in practical application. In causal control, the main focus is to extend the original Equivalent Consumption Minimization Strategy (ECMS) with the fuzzy control. The proposed algorithm can maintain the battery State of Charge (SoC) in a desirable range, without the requirement of off-line estimation of equivalence factor. By comparing with non-causal control—dynamic programming, the test cases validates that the fuzzy based ECMS succeeds in converting the non-causal optimization, with little sacrifice of the optimality of the solution. In other words, the prior-knowledge of flight mission is not a pre- requisite, and the fuzzy based ECMS can achieve the sub-optimal control for on-line implementation. The fuzzy based ECMS is also validated to outperform the adaptive ECMS, since it can reduce the computation time of optimization and save more fuel usage. The theoretical relationship between the equivalence factor of ECMS and the co-state variable of Hamiltonian function is also demonstrated in this thesis. The convex optimization and fuzzy based ECMS are combined to complete a flight mission with several sub-tasks. Each task has different power and SoC requirements. The test case demonstrates that only the combination of non-causal and causal optimization can satisfy the various constraints and requests of the test scenario. Compared with the engine-only powered aircraft, the hybrid powered aircraft saves 18.7% on fuel consumption. Furthermore, the hybrid propulsion system has better efficiency since it integrates the high efficient electric powertrain.
Open Access
Design and implementation of deep neural network-based control for automatic parking maneuver process
(IEEE, 2020-12-17) Chai, Runqi; Tsourdos, Antonios; Savvaris, Al; Chai, Senchun; Xia, Yuanqing; Chen, C. L. Philip
This article focuses on the design, test, and validation of a deep neural network (DNN)-based control scheme capable of predicting optimal motion commands for autonomous ground vehicles (AGVs) during the parking maneuver process. The proposed design utilizes a multilayer structure. In the first layer, a desensitized trajectory optimization method is iteratively performed to establish a set of time-optimal parking trajectories with the consideration of noise-perturbed initial configurations. Subsequently, by using the preplanned optimal parking trajectory data set, several DNNs are trained in order to learn the functional relationship between the system state-control actions in the second layer. To obtain further improvements regarding the DNN performances, a simple yet effective data aggregation approach is designed and applied. These trained DNNs are then utilized as the motion controllers to generate feedback actions in real time. Numerical results were executed to demonstrate the effectiveness and the real-time applicability of using the proposed control scheme to plan and steer the AGV parking maneuver. Experimental results were also provided to justify the algorithm performance in real-world implementations.
Open Access
Development of a fuel cell hybrid-powered unmanned aerial vehicle
(IEEE, 2016-08-08) Savvaris, Al; Xie, Ye; Malandrakis, Konstantinos; Tsourdos, Antonios
This paper describes the design and development of a hybrid fuel cell/battery propulsion system for a long endurance small UAV. The high level system architecture is presented, followed by the hardware-in-the-loop testing and performance analysis. A high fidelity 6-DoF simulation model of the complete system was developed and used to test the system under different battery state-of-charge. The simulation model included the power manager for the hybrid propulsion system configuration, which is based on rule-based control. The simulation results are compared with the experimental results obtained from the Hardware-in-the-Loop testing.
Open Access
Development of battery management system for hybrid electric propulsion system.
(2018-04) Wang, Letian; Savvaris, Al
Because of the high overall efficiency and low emissions, Hybrid Electric Propulsion System (HEPS) have become an attractive research area. In this research, a parallel HEPS architecture is adopted and a Hardware test platform is constructed. As a relative new power source in powertrains, battery system plays an important role in HEPS. Hence, a Battery Management System (BMS) is investigated in this research. Battery pack State of Charge (SOC) is a key feedback value in HEPS control. In order to estimate SOC, firstly, an operation-classification adaptive battery model is proposed for Li-Po batteries. Considering the fact that model parameter accuracy is of importance in model-based system state estimation method, an event triggered Adaptive Genetic Algorithm (AGA) is applied for online parameter identification. Secondly, the Extended Kalman Filter (EKF) is applied for single battery cell SOC estimation. Finally, a fuzzy estimator is proposed for battery pack SOC estimation based on maximum/minimum cell voltages and SOC values. Experimental results show that the proposed AGA can effectively track battery parameter variation and SOC estimation error for single cell as well as for the battery pack are both less than 1%. Moreover, considering the Li-Po battery characteristics, a converter based battery cell balancing method is proposed. Simulation result shows that proposed balancing method can be effective in balancing battery cells. In addition, in relation to safety and reliability concerns, a Discrete Wavelet Transform (DWT) based battery circuit detection method is proposed and simulation results showing its feasibility are presented.
Open Access
Efficient path planning algorithms for Unmanned Surface Vehicle
(Elsevier, 2016-11-01) Niu, Hanlin; Lu, Yu; Savvaris, Al; Tsourdos, Antonios
The C-Enduro Unmanned Surface Vehicle (USV) is designed to operate at sea for extended periods of time (up to 3 months). To increase the endurance capability of the USV, an energy efficient path planning algorithm is developed. The proposed path planning algorithm integrates the Voronoi diagram, Visibility algorithm, Dijkstra search algorithm and takes also into account the sea current data. Ten USV simulated mission scenarios at different time of day and start/end points were analysed. The proposed approach shows that the amount of energy saved can be up to 21%. Moreover, the proposed algorithm can be used to calculate a collision free and energy efficient path to keep the USV safe and improve the USV capability. The safety distance between the USV and the coastline can also be configured by the user.
Open Access
Energy efficient path planning and model checking for long endurance unmanned surface vehicles.
(2017-09) Niu, Hanlin; Savvaris, Al; Tsourdos, Antonios
In this dissertation, path following, path planning, collision avoidance and model checking algorithms were developed and simulated for improving the level of autonomy for Unmanned Surface Vehicle (USV). Firstly, four path following algorithms, namely, Carrot Chasing, Nonlinear Guidance Law, Pure pursuit and LOS, and Vector Field algorithms, were compared in simulation and Carrot Chasing was tested in Unmanned Safety Marine Operations Over The Horizon (USMOOTH) project. Secondly, three path planning algorithms, including Voronoi-Visibility shortest path planning, Voronoi-Visibility energy efficient path planning and Genetic Algorithm based energy efficient path planning algorithms, are presented. Voronoi-Visibility shortest path planning algorithm was proposed by integrating Voronoi diagram, Dijkstra’s algorithm and Visibility graph. The path quality and computational efficiency were demonstrated through comparing with Voronoi algorithms. Moreover, the proposed algorithm ensured USV safety by keeping the USV at a configurable clearance distance from the coastlines. Voronoi-Visibility energy efficient path planning algorithm was proposed by taking sea current data into account. To address the problem of time-varying sea current, Genetic Algorithm was integrated with Voronoi-Visibility energy efficient path planning algorithm. The energy efficiency of Voronoi-Visibility and Genetic Algorithm based algorithms were demonstrated in simulated missions. Moreover, collision avoidance algorithm was proposed and validated in single and multiple intruders scenarios. Finally, the feasibility of using model checking for USV decision-making systems verification was demonstrated in three USV mission scenarios. In the final scenario, a multi-agent system, including two USVs, an Unmanned Aerial Vehicle (UAV), a Ground Control Station (GCS) and a wireless mesh network, were modelled using Kripke modelling algorithm. The modelled uncertainties include communication loss, collision risk, fault event and energy states. Three desirable properties, including safety, maximum endurance, and fault tolerance, were expressed using Computational Tree Logic (CTL), which were verified using Model Checker for Multi-Agent System (MCMAS). The verification results were used to retrospect and improve the design of the decision-making system.
Open Access
Energy efficient wireless sensor network topologies and routing for structural health monitoring.
(2018-03) Baksh, Ahmad; Savvaris, Al
The applicability of wireless sensor networks (WSNs) has dramatically increased from the era of smart farming and environmental monitoring to the recent commercially successful internet of things (IoT) applications. Simultaneously, diversity in WSN applications has led to the application of specific performance requirements, such as fault tolerance, reliability, robustness and survivability. One important application is structural health monitoring (SHM) in airplanes. Airborne Wireless Sensor Network (AWSN) have received considerable attention in recent times, owing to the many issues that are intrinsic to traditional wire-based airplane monitoring systems, such as complicated cable routing, long wiring, wiring degradation over time, installation overhead, etc. This project examines the SHM of aircraft wing and WSN design (ZigBee), and aspects such as node deployment and power efficient routing, vis-à-vis energy harvesting. Node deployment and power efficient routing protocol are related problems, and so this thesis proposes solutions using optimization techniques for Ant Colony Optimization (ACO), and power transmission profiling using Computer Simulation Technology software (CST). There are three wing models; namely NACA64A410 model, Empty NACA64A410 model for the Wing, and Empty Prismatic model of the wing was specified and simulated in CST software. A simulation was carried out between the frequencies of 100 MHz to 5 GHz, and identified significant variations in the Sij parameter between the frequency range 2.4GHz and 2.5GHz. Critical analysis of the obtained results revealed the presence of a significant impact from wing shape and the wing’s inner structure on possible radio wave propagation in the aircraft wing. The different material composition of aircraft wings was also examined to establish the influence of aircraft wing material on radio wave propagation in an aircraft wing. The three materials tested were Perfect Electrical Conductor (PEC), Aluminium, and Carbon Fibre Composites (CFCs). For power transmission profiling (Sij parameter), 130 nodes were deployed in regular and periodic compartments, created by ribs and spars, usually at vantage points and rib openings, so that a direct line of sight could be established. However, four sink nodes were also placed at the wing root, as presented in NC37 and NC38 simulations for aluminium and CFC wing models respectively. The evaluation of signal propagation in aluminium and CFC aircraft wing models revealed CFC wing models allow less transmission than aluminium wing models. A multiple Travelling Salesman (mTSP) problem was formulated and solved, using Ant Colony Optimization in MATLAB to identify optimal topology and optimal routes to support radio propagation in ZigBee networks. Then solving the mTSP problem for different regular deployments of nodes in the wing geometry, it was found that an edgewise communication route was the shortest route for a large number of nodes, wherein 4 fixed sink nodes were placed at the wing root. For a realistic wing model, the different possible configuration of ZigBee units were deduced using rational reasoning, based on results from empty wing models. Besides the determined S-parameter, aircraft wing materials and optimal nodes, the residual energy of each sensor node is also considered an essential criterion to improve the efficiency of ZigBee communications on the aircraft wing. Therefore, a novel hybrid protocol called the Energy-Opportunistic Weighted Minimum Energy (EOWEME) protocol can be formulated and implemented in MATLAB. The comparative results revealed the energy saving of EOWEME protocol is 20% higher compared to the Ad Hoc On-Demand Distance Vector (AODV) routing protocol. However, the need for further energy savings resulted in development of an improved EOWEME protocol when incorporating the clustering concept and the previously determined S-parameter, a number of nodes, and their radiation patterns. Critical evaluation of this improved EOWEME protocol showed a maximum of 10% higher energy savings than the previous EOWEME protocol. To summarize key insights and the results of this thesis, it is apparent that the thesis addresses SHM in aircraft wings, using WSNs from a holistic perspective with the following major contributions, • CST simulations identify power transfer (S-parameter) profiling in various wing models, with no internal structural elements to identify realistic wing with spars, and bars. With an average S-parameter of -107 dB at around 3 m, the communication or transmission range of 1 m was identified to minimize loss of transmitted power. A range less than 1m would cause issues such as interference, reflection etc. • Using a transmission range of 1 m, WSN nodes were assessed for shortest route commensurate with energy efficient packet transmission to sink node from the farthest node; i.e. near the wing tip. The shortest routes converged to travel along the length of the wing in the case of an empty wing model, however it was also observed in a realistic wing model, where internal structural elements constrained node deployment. An average distance of nearly 13 m required data transmitted from the farthest nodes to reach the sink nodes. Increasing the nodes however increased the distance required to up to 20 m in the case of 240 nodes. • A new routing protocol, EOWEME was formulated, showing 20% greater energy savings than AODV in the realistic wing model.
Open Access
An energy-efficient path planning algorithm for unmanned surface vehicles
(Elsevier, 2018-05-25) Niu, Hanlin; Lu, Yu; Savvaris, Al; Tsourdos, Antonios
The sea current state affects the energy consumption of Unmanned Surface Vehicles (USVs) significantly and the path planning approach plays an important role in determining how long the USV can travel. To improve the endurance of the USV, an energy efficient path planning approach for computing feasible paths for USVs that takes the energy consumption into account based on sea current data is proposed. The approach also ensures that the USV remains at a user-configurable safety distance away from all islands and coastlines. In the proposed approach, Voronoi diagram, Visibility graph, Dijkstra's search and energy consumption function are combined, which allows USVs to avoid obstacles while at the same time using minimum amount of energy. The Voronoi-Visibility (VV) energy-efficient path and the corresponding shortest path were simulated and compared for ten missions in Singapore Strait and five missions for islands off the coast of Croatia. Impact of parameters such as mission time, the USV speed and sea current state on the results were analysed. It is shown that the proposed VV algorithm improves the quality of the Voronoi energy efficient path while keeping the same level of computational efficiency as that of the Voronoi energy efficient path planning algorithm.
Open Access
Fast generation of chance-constrained flight trajectory for unmanned vehicles
(IEEE, 2020-11-16) Chai, Runqi; Tsourdos, Antonios; Savvaris, Al; Wang, Shuo; Xia, Yuanqing; Chai, Senchun
In this work, a fast chance-constrained trajectory generation strategy incorporating convex optimization and convex approximation of chance constraints is designed so as to solve the unmanned vehicle path planning problem. A pathlength- optimal unmanned vehicle trajectory optimization model is constructed with the consideration of the pitch angle constraint, the curvature radius constraint, the probabilistic control actuation constraint, and the probabilistic collision avoidance constraint. Subsequently, convexification technique is introduced to convert the nonlinear problem formulation into a convex form. To deal with the probabilistic constraints in the optimization model, convex approximation techniques are introduced such that the probabilistic constraints are replaced by deterministic ones, while simultaneously preserving the convexity of the optimization model. Numerical results, obtained from a number of case studies, validate the effectiveness and reliability of the proposed approach. A number of comparative studies were also performed. The results confirm that the proposed design is able to produce more optimal flight paths and achieve enhanced computational performance than other chance-constrained optimization approaches investigated in this paper.