Gait identification and optimisation for amphi-underwater robot by using ant colony algorithm

Manoeuvrable robot commonly has become the focus of the latest heated issues especially in applications that involved disaster rescue, military missions and underwater or extra-terrestrial explorations. Currently, the manoeuvrable robot is controlled manually by the operator and it’s a wheeled type. It is used for rescue missions to transport people from disaster area to the safe zone. However, the robot is incapable of moving automatically, and it goes through terrain or landscape like swarm. Therefore, a suitable platform is required to transport or for other uses especially in dangerous mission. It is very difficult to estimate the movement of the robot to avoid obstacles and choose the alternative path. Hence, this research presents the point-to-point gait identification or path planning of the behavious of the robot to manuever autonomously on both on-land and underwater environment. For the optimization, the robot will travel from one specific point to another with the predefined position within optimized gait and fastest time by using Ant Colony Optimization (ACO) technique. The algorithm being compared, between Ant Colony Algorithm (ACO) and the Particle Swarm Optimisation (PSO) in terms of time and distance. The ACO been chosen because of the positive feedback for rapid discovery and able to use in dynamic applications for example adapts to changes like new distances. The performance of the algorithm showed that the execution time of ACO is more realistic. Hence, Matlab is used to determine the best cost extracted from the ACO with the pre-define of number of iteration and the number of ants. The laboratory-scaled prototype for amphibious vehicle was developed to test the design controlled with ACO technique where Global Positioning System (GPS) is used for the coordination of the robot and Magnetometer for the position of the robot. The robot prototype is able to move autonomously and optimized by the ant colony optimization with predefined position and terrain condition © BEIESP

Real-time UAV Complex Missions Leveraging Self-Adaptive Controller with Elastic Structure

The expectation of unmanned air vehicles (UAVs) pushes the operation environment to narrow spaces, where the systems may fly very close to an object and perform an interaction. This phase brings the variation in UAV dynamics: thrust and drag coefficient of the propellers might change under different proximity. At the same time, UAVs may need to operate under external disturbances to follow time-based trajectories. Under these challenging conditions, a standard controller approach may not handle all missions with a fixed structure, where there may be a need to adjust its parameters for each different case. With these motivations, practical implementation and evaluation of an autonomous controller applied to a quadrotor UAV are proposed in this work. A self-adaptive controller based on a composite control scheme where a combination of sliding mode control (SMC) and evolving neuro-fuzzy control is used. The parameter vector of the neuro-fuzzy controller is updated adaptively based on the sliding surface of the SMC. The autonomous controller possesses a new elastic structure, where the number of fuzzy rules keeps growing or get pruned based on bias and variance balance. The interaction of the UAV is experimentally evaluated in real time considering the ground effect, ceiling effect and flight through a strong fan-generated wind while following time-based trajectories.Comment: 18 page

Numerical and Experimental Investigation of Performance for Very-Low-Head Micro and Pico Kaplan Hydro-Turbines with Rim-Driven Generators

Renewable energy plays a significant role in new power generation worldwide, and hydropower is contributing to 86% of renewable electricity production within all other renewable energy resources. Simultaneously, hydropower shares 83% of U.S. renewable energy capacity and accounts for 77% of actual renewable electricity generation. However, most of the installed hydropower consists of large plants. Much potential hydro generation remains untapped, particularly at lower power and head levels. There is a substantial opportunity worldwide and across the U.S. in specific to add new hydropower generating capabilities at low-head sites such as non-powered dams, canals, and conduits with a water height of less than 30 meters, especially, where the potential of solar and wind is not available. As stated by the U.S. Department of Energy, there is an estimated potential hydropower capacity of 12,000 MW of the existed 80,000 unpowered dams with at least 3 feet of water head available. In this research, investigation of power-efficient micro and pico Kaplan hydro turbines at very-low-head with rim-driven generators to be studied and evaluated, specifically, at heads of less than 3 meters (10 ft). Optimization of performance and design for a 3D-printed conventional -with shaft- 7.6 cm (3-inch) Kaplan turbine to be carried out based on an experimental setup in the Hydro Turbines Laboratory of the University of Wisconsin-Milwaukee in addition to the utilization of Computational Fluid Dynamics (CFD). Then a shaftless rim-driven generator-based turbine (RDT) to be introduced and optimized. Such a new hydro turbine perception will increase the efficiency (of power generation) of hydro turbines in general, and the efficiency of low-head turbines in specific. The design optimization includes; the number of the blades for the turbine’s rotor (runner) and stator, the blade wrap-angle of the rotor, intake and draft tubes angles, lengths and shapes, and the guide vanes. The performance in terms of the power output and the efficiency is evaluated for the conventional turbine by utilizing CFD and by testing a 3D-printed model of the turbine in a custom-built experimental setup at different water heads (from 2.0 m to 2.6 m) and different rotational speeds (0 – 4000 rpm). The CFD setup is based on 3D transient turbulent featuring the Large Eddy Simulation (LES) model, and STAR-CCM+ is the CFD software. In addition, the high-performance computing (HPC) cluster of the University of Wisconsin-Milwaukee is used for solving the complex CFD simulations. To evaluate the advantage of the RDT over the conventional turbines, the rim-driven shaftless turbine is introduced in this research at the same boundary conditions. The RDT is not expected only to increase the efficiency of hydro turbines. It will also contribute saving the environment by allowing debris or fish to pass through the central area of the turbine, especially in the case of run-a-river hydro turbines applications. Furthermore, some applications of the RDT are presented in this study. The utilization of RDT in wastewater treatment plants (WWTPs) is one example where WWTPs usually have low or very-low head between the discharge point of the plant and the water body where the treated water is supposed to be disposed. At the same time, a significant continuous water flow rate is available all over the year for feasible hydro turbine installations. Such utilization will improve the energy efficiency of WWTPs

Design mining interacting wind turbines

© 2016 by the Massachusetts Institute of Technology. An initial study has recently been presented of surrogate-assisted evolutionary algorithms used to design vertical-axis wind turbines wherein candidate prototypes are evaluated under fan-generated wind conditions after being physically instantiated by a 3D printer. Unlike other approaches, such as computational fluid dynamics simulations, no mathematical formulations were used and no model assumptions weremade. This paper extends that work by exploring alternative surrogate modelling and evolutionary techniques. The accuracy of various modelling algorithms used to estimate the fitness of evaluated individuals from the initial experiments is compared. The effect of temporally windowing surrogate model training samples is explored. A surrogateassisted approach based on an enhanced local search is introduced; and alternative coevolution collaboration schemes are examined

Living IoT: A Flying Wireless Platform on Live Insects

Sensor networks with devices capable of moving could enable applications ranging from precision irrigation to environmental sensing. Using mechanical drones to move sensors, however, severely limits operation time since flight time is limited by the energy density of current battery technology. We explore an alternative, biology-based solution: integrate sensing, computing and communication functionalities onto live flying insects to create a mobile IoT platform. Such an approach takes advantage of these tiny, highly efficient biological insects which are ubiquitous in many outdoor ecosystems, to essentially provide mobility for free. Doing so however requires addressing key technical challenges of power, size, weight and self-localization in order for the insects to perform location-dependent sensing operations as they carry our IoT payload through the environment. We develop and deploy our platform on bumblebees which includes backscatter communication, low-power self-localization hardware, sensors, and a power source. We show that our platform is capable of sensing, backscattering data at 1 kbps when the insects are back at the hive, and localizing itself up to distances of 80 m from the access points, all within a total weight budget of 102 mg.Comment: Co-primary authors: Vikram Iyer, Rajalakshmi Nandakumar, Anran Wang, In Proceedings of Mobicom. ACM, New York, NY, USA, 15 pages, 201

Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization

In October 2014, the EU leaders agreed upon three key targets for the year 2030: a reduction by at least 40% in greenhouse gas emissions, savings of at least 27% for renewable energy, and improvements by at least 27% in energy efficiency. The increase in computational power combined with advanced modeling and simulation tools makes it possible to derive new technological solutions that can enhance the energy efficiency of systems and that can reduce the ecological footprint. This book compiles 10 novel research works from a Special Issue that was focused on data-driven approaches, machine learning, or artificial intelligence for the modeling, simulation, and optimization of energy systems

Development of an Indoor Multirotor Testbed for Experimentation on Autonomous Guidance Strategies

Despite the vast popularity of rotary wing unmanned aerial vehicles and research centres that develop their guidance software, there are only a limited number of references that provide an exhaustive description of a step-by-step procedure to build-up a multirotor testbed. In response to such need, the first part of this thesis aims to describe, in detail, the complete procedure to establish and operate an autonomous multirotor unmanned aerial vehicle indoor experimental platform to test and validate guidance, navigation and control strategies. Both hardware and software aspects of the testbed are described to offer a complete understanding of the different aspects. The second part of this thesis focuses on two benchmarks multirotor guidance, navigation and control problems. Initially, the guidance law for an accurate landing manoeuvre is studied. Multirotor usually have a flight time limited to a few minutes. Autonomous landing and docking to a charging station could extend the mission duration of these vehicles. Subsequently, the guidance strategy for the formation flight between two multirotors is considered. In this case, the fundamental goal is an accurate autonomous alignment between two vehicles, each of them behaving as a target and chaser simultaneously. In the last part of this thesis, the problem of minimum energy manoeuvres is tackled. Again, in this case, the motive is to address the limitation in multirotor flight duration. The fundamental objective of this guidance, navigation and control strategy is to determine and implement, in real-time, the minimum energy control histories that transfer the multirotor from its initial point to a given final point. As opposed to conventional guidance strategies, mostly based on proportional-integral-derivative laws, a minimum energy controller allows the vehicle to execute the manoeuvre with a minimum electrical power expenditure

Marine dual fuel engines modelling and optimisation employing : a novel combustion characterisation method

Dual fuel (DF) engines have been an attractive alternative of traditional diesel engines for reducing both the environmental impact and operating cost. The major challenge of DF engine design is to deal with the performance-emissions trade-off via operating settings optimisation. Nevertheless, determining the optimal solution requires large amount of case studies, which could be both time-consuming and costly in cases where methods like engine test or Computational Fluid Dynamics (CFD) simulation are directly used to perform the optimisation. This study aims at developing a novel combustion characterisation method for marine DF engines based on the combined use of three-dimensional (3D) simulation and zero-dimensional/one-dimensional (0D/1D) simulation methods. The 3D model is developed with the CONVERGE software and validated by employing the measured pressure and emissions. Subsequently, the validated 3D model is used to perform a parametric study to explore the engine operating settings that allow simultaneous reduction of the brake specific fuel consumption (BSFC) and NOx emissions at three engine operation conditions (1457 r/min, 1629 r/min and 1800 r/min). Furthermore, the derived heat release rate (HRR) is employed to calibrate the 0D Wiebe combustion model by using Response Surface Methodology (RSM). A linear response model for the Wiebe combustion function parameters is proposed by considering each Wiebe parameter as a function of the pilot injection timing, equivalence ratio and natural gas mass. The 0D/1D model is established in the GT-ISE software and used to optimise the performance-emissions trade-off of the reference engine by employing the Nondominated Sorting Genetic Algorithm II (NSGA II). The obtained results provide a comprehensive insight on the impacts of the involved engine operating settings on in-cylinder combustion characteristics, engine performance and emissions of the investigated marine DF engine. By performing the settings optimisation at three engine operating points, settings that lead to reduced BSFC are identified, whilst the NOx emissions comply with the Tier III NOx emissions regulation. The proposed novel method is expected to support the combustion analysis and enhancement of marine DF engines during the design phase, whilst the derived optimal solution is expected to provide guidelines of DF engine management for reducing operating cost and environmental footprint.Dual fuel (DF) engines have been an attractive alternative of traditional diesel engines for reducing both the environmental impact and operating cost. The major challenge of DF engine design is to deal with the performance-emissions trade-off via operating settings optimisation. Nevertheless, determining the optimal solution requires large amount of case studies, which could be both time-consuming and costly in cases where methods like engine test or Computational Fluid Dynamics (CFD) simulation are directly used to perform the optimisation. This study aims at developing a novel combustion characterisation method for marine DF engines based on the combined use of three-dimensional (3D) simulation and zero-dimensional/one-dimensional (0D/1D) simulation methods. The 3D model is developed with the CONVERGE software and validated by employing the measured pressure and emissions. Subsequently, the validated 3D model is used to perform a parametric study to explore the engine operating settings that allow simultaneous reduction of the brake specific fuel consumption (BSFC) and NOx emissions at three engine operation conditions (1457 r/min, 1629 r/min and 1800 r/min). Furthermore, the derived heat release rate (HRR) is employed to calibrate the 0D Wiebe combustion model by using Response Surface Methodology (RSM). A linear response model for the Wiebe combustion function parameters is proposed by considering each Wiebe parameter as a function of the pilot injection timing, equivalence ratio and natural gas mass. The 0D/1D model is established in the GT-ISE software and used to optimise the performance-emissions trade-off of the reference engine by employing the Nondominated Sorting Genetic Algorithm II (NSGA II). The obtained results provide a comprehensive insight on the impacts of the involved engine operating settings on in-cylinder combustion characteristics, engine performance and emissions of the investigated marine DF engine. By performing the settings optimisation at three engine operating points, settings that lead to reduced BSFC are identified, whilst the NOx emissions comply with the Tier III NOx emissions regulation. The proposed novel method is expected to support the combustion analysis and enhancement of marine DF engines during the design phase, whilst the derived optimal solution is expected to provide guidelines of DF engine management for reducing operating cost and environmental footprint