Geofencing Motion Planning for Unmanned Aerial Vehicles Using an Anticipatory Range Control Algorithm

© 2023 The Author(s). Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/This paper presents a range control approach for implementing hard geofencing for unmanned air vehicles (UAVs), and especially remotely piloted versions (RPVs), via a proposed anticipatory range calculator. The approach employs turning circle intersection tests that anticipate the fence perimeter on approach. This ensures the vehicle turns before penetrating the geofence and remains inside the allowable operational airspace by accounting for the vehicles’ turning dynamics. Allowance is made for general geozone shapes and locations, including those located at the problematic poles and meridians where nonlinear angle mapping is dealt with, concave geozones, narrow corners with acute internal angles, and transient turn dynamics. The algorithm is shown to prevent any excursions using a high-fidelity simulation of a small remotely piloted vehicle. The algorithm relies on a single tuning parameter which can be determined from the closed-loop rise time in the aircraft’s roll command tracking.Peer reviewe

A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning

Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off

A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning

Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off.Peer reviewe

Podešavanje pulsno-širinskog pulsno-frekvencijskog modulatora korištenjem optimizacije rojevima čestica: Inženjerski pristup dizajnu regulatora orijentacije letjelice

: In this paper, a new technique for fine tuning of spacecraft autopilots based on pulse-width pulse-frequency (PWPF) modulators is presented. PWPF is one of the most commonly used approaches to control signal modulation. Its main application is found in spacecraft controllers to produce discontinuous on-off control signals for two situational actuators. The main reasons for the popularity of this method are the reduced energy consumption and the quasi linear operation with high degrees of freedom in adjustment. But, due to multiplicity and nonlinear relationship between parameters, fine tuning of PWPF is known to be an engineering problem. Similar complexity is observable in adjusting the incorporated controller parameters. These involvements regarding the industrial and academic background of PWPF are not properly explored. The paper shows how particle swarm optimization (PSO) can be invoked to set both controller and PWPF parameters. Several spacecraft autopilots have been designed to show effectiveness of the proposed method.U ovom radu prikazana je nova metoda za fino podešavanje autopilota letjelice zasnovana na pulsno-širinsko pulsno-frekvencijskoj modulaciji (PWPF). PWPF je jedan od najčešće korištenih pristupa u upravljanju modulacijom signala. Njegova glavna primjena nalazi se u regulatorima letjelica koji proizvode diskontinuirane on-off upravljačke signale za dva aktuatora. Glavni razlozi za popularnost ove metode je smanjena potrošnja energije i kvazi linearno ponašanje s velikim stupnjem slobode kod podešavanja. Međutim, zbog višestrukosti i nelinernih odnosa među parametrima, fino podešavanje PWPF-a je zahtjevno. Slična složenost može se primijetiti i kod podešavanja parametara regulatora. Ovi problemi kod primjene PWPF-a nisu dovoljno istraženi. U ovom radu prikazano je kako se može iskoristiti optimizacija rojevima čestica za podešavanje parametara regulatora i modulatora. Dizajnirano je nekoliko autopilota letjelica kako bi se pokazala učinkovitost predložene metode

Optimal gear ratio selection of linear primary permanent magnet vernier machines for wave energy applications

Linear permanent magnet vernier generators offer a high capability of force density, making them appealing configurations for wave energy harvesting systems. In absolute terms, the performance of these machines is significantly influenced by the selection of slot/pole combinations based on the magnetic gearing effect. For the first time, this paper aims to investigate the impact of different gear ratios on a wide array of linear primary permanent magnet vernier machines (LPPMVMs) with different slot/pole combinations based on fair criteria to offer a more comprehensive understanding of gear ratio selection. To find the optimal number of slots and poles, the response surface methodology is adopted to obtain a robust design and make a fair comparison among LPPMVMs with optimum design characteristics using a cost-effective approach for the fast and reliable optimisation process. The higher gear ratios result in higher thrust force capability. This will help establishing a new route toward faster develpment of advanced LPPMVMs. The power loss models of LPPMVMs are studied to predict their steady-state and transient thermal behaviours, verifying their stability and safety, while a simple external forced convection method can be utilised. To verify the model, finite element analysis is exploited to confirm the electromagnetic and thermal analysis results and provide a more exhaustive investigation

Optimal gear ratio selection of linear primary permanent magnet vernier machines for wave energy applications

© 2023 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Linear permanent magnet vernier generators offer a high capability of force density, making them appealing configurations for wave energy harvesting systems. In absolute terms, the performance of these machines is significantly influenced by the selection of slot/pole combinations based on the magnetic gearing effect. For the first time, this paper aims to investigate the impact of different gear ratios on a wide array of linear primary permanent magnet vernier machines (LPPMVMs) with different slot/pole combinations based on fair criteria to offer a more comprehensive understanding of gear ratio selection. To find the optimal number of slots and poles, the response surface methodology is adopted to obtain a robust design and make a fair comparison among LPPMVMs with optimum design characteristics using a cost‐effective approach for the fast and reliable optimisation process. The higher gear ratios result in higher thrust force capability. This will help establishing a new route toward faster develpment of advanced LPPMVMs. The power loss models of LPPMVMs are studied to predict their steady‐state and transient thermal behaviours, verifying their stability and safety, while a simple external forced convection method can be utilised. To verify the model, finite element analysis is exploited to confirm the electromagnetic and thermal analysis results and provide a more exhaustive investigation.Peer reviewe

Deep reinforcement learning for adaptive path planning and control of an autonomous underwater vehicle

© 2022 Elsevier Ltd. All rights reserved. This is the accepted manuscript version of article which has been published in final form at https://doi.org/10.1016/j.apor.2022.103326Research into intelligent motion planning methods has been driven by the growing autonomy of autonomous underwater vehicles (AUV) in complex unknown environments. Deep reinforcement learning (DRL) algorithms with actor-critic structures are optimal adaptive solutions that render online solutions for completely unknown systems. The present study proposes an adaptive motion planning and obstacle avoidance technique based on deep reinforcement learning for an AUV. The research employs a twin-delayed deep deterministic policy algorithm, which is suitable for Markov processes with continuous actions. Environmental observations are the vehicle's sensor navigation information. Motion planning is carried out without having any knowledge of the environment. A comprehensive reward function has been developed for control purposes. The proposed system is robust to the disturbances caused by ocean currents. The simulation results show that the motion planning system can precisely guide an AUV with six-degrees-of-freedom dynamics towards the target. In addition, the intelligent agent has appropriate generalization power.Peer reviewe

An Integrated Risk Assessment and Collision Avoidance Methodology for an Autonomous Catamaran with Fuzzy Weighting Functions

Collision avoidance and risk assessment are open problems to be practically addressed in maritime transportation. In high-speed vessels this problem becomes more challenging due to manoeuvring and reaction time constraints. Here, a reactive collision avoidance and risk assessment technique with fuzzy weighting functions are proposed for a relatively high-speed autonomous catamaran. To follow paths between predefined waypoints, a Line of Sight (LOS) technique with Cross Tracking Error (CTE) is utilised. Besides, a new collision risk index is introduced based on fuzzy weighting functions. To perform formal maritime decision making, the standard marine COLlision REGulations (COLREGs) are incorporated into the algorithm. Furthermore, a simplified Closest Point of Approach (CPA) formulation is presented. The proposed framework is simulated on a realistic model of a vessel including input and non-holonomic constraints and disturbances. Simulation results for various encounter scenarios demonstrate the merits of the proposed method

On the Application of Agile Project Management Techniques, V-Model and Recent Software Tools in Postgraduate Theses Supervision

© 2023 The Author(s). This is an open access article under the CC BY-NC-ND licence, https://creativecommons.org/licenses/by-nc-nd/4.0/Abstract: Due to the nature of most postgraduate theses in control engineering and their similarities to industrial and software engineering projects, invoking novel project control techniques could be effective. In recent decades, agile techniques have attracted popularity thanks to their attributes in delivering successful projects. Hence exploiting those methods in education and thesis supervision of engineering topics can facilitate the process. On the other hand, because of the limitations imposed by the CoVid19 pandemic, the integration of well-established online tools in collaborative education is noteworthy. This paper proposes an application of the agile project management method for the supervision of postgraduate students’ theses in the general field of engineering. The study extends a Scrum technique combined with approved systems engineering and team working tools such as Jira Software, Microsoft Teams, and Git version control (Github website). A custom designed V-model to nail an outstanding thesis is presented. The overall blended method is beneficial to provide feedback and self-assessment aid for the students and the supervisors. Employing this technique has shown promising progress in easing the supervision of students whilst helping them to manage their projects