In this article, sensor fusion for positioning a four wheeled Autonomous Vehicle, using indirect feed forward Kalman filter algorithm is simulated. Sensors such as accelerometer, tachometer, compass and potentiometer are used. At first, Vehicle and its dynamic equations are introduced then system identification is done in order to find parameters of Vehicle that is performed in two phases: Vehicle parameters identification and motor parameters identification. Also, accelerometer noise and random bias reduction is done using direct and indirect Kalman filter. In order to remove the sensors error, indirect Kalman filter is utilized. The governing equations of error model are linear and time varying. Observability test indicates that these equations are not observable and Kalman filter implementation in such systems leads to non optimal result. A new approach for solving this problem based on finding the observable subsystem of the main system is introduced and Kalman filter is used for estimating the observable states. Simulation results show efficiency of the proposed method. Also, a new approach to find process and measurements noise covariance matrices using reverse shaping filter is presented.

Abstract Modern economic growth has been based on mass industrial production and consumption, which have heavily relied on fossil fuels and energy waste since the 18th century. Hence, current socio-technical systems are unsustainable in meeting humans’ basic needs, such as energy and mobility. Fossil energy resources are non-renewable and, on the one hand, contribute to emissions that cause unreliable harm to the environment. In this research, the prime theory of Transformational Change illuminates how to use science and technology policy to meet social needs sustainably and inclusively in societies. This article answers questions regarding the essential policies and governance measures that states need to implement for the transition to electric Autonomous Vehicles (AVs) in the socio-technical system. Using meta-synthesis, followed by a case study and interviews with experts in the electric AV field, the article identifies state policies and governance measures to facilitate the transition of the sociotechnical system into electric AVs. The conceptualization of these roles determines that the state’s role is influenced by policy, governance, and legal decisions, which are ultimately implemented through specific combinations of policies.

In this study, the roll, yaw and depth fuzzy control of an Autonomous Underwater Vehicle (AUV) are addressed. Yaw and roll angles are regulated only using their errors and rates, but due to the complexity of depth dynamic channel, additional pitch rate quantity is used to improve the depth loop performance. The discussed AUV has four aps at the rear of the Vehicle as actuators. Two rule bases and membership functions based on Mamdani type and Sugeno type fuzzy rule have been chosen in each loop. By invoking the normalized steepest descent optimization method, the optimum values for the membership function parameters are found. Though the AUV is a highly nonlinear system, the simulation of the designed fuzzy logic control system based on the equations of motion shows desirable behavior of the AUV specially when the parameters of the fuzzy membership functions are optimized.

Hydrodynamic design of the exterior hull shape is a main step of body shape design. The effective parameters in this process include the length of the nose and tail، the blunt sections of nose and tail as well as the profiles of the nose and the tail. In the present study to investigate the effect of each of these parameters on the drag coefficient of the body، Design of Experiments (DOE) method is used. For this purpose، by introducing the Hydrolab family profiles، the results of numerical simulation of flow around the Hydrolab500 body was applied to design of experiments. In the first phase، a sample experiments in water tunnel was conducted to validate the pressure profile around Hydrolab500 body. Comparing experimental and numerical results shows the validity of the employed numerical methods. The results of the current work shows that by presented method the drag coefficient of an AUV in the final design can be estimated with high accuracy.

This paper introduces a trajectory planning algorithm for long-term freeway driving for Autonomous Vehicles including different modes of motion. In the Autonomous driving in a freeway, different maneuvers are needed, including free flow, distance adaption, speed adaption, lane change and overtaking. This paper introduces an algorithm that provides all of these driving scenarios in the trajectory planning for an Autonomous Vehicle. All maneuvers are classified and proper formulation for each driving mode formulated. Then, an algorithm is introduced to show the procedure of decision making and switching between all driving modes. The relative distances and velocities of the other peripheral and front Vehicle from Autonomous Vehicle are considered as the main factors for decision making during the travelling in the freeway. By the developed simulation programming, validity and effectiveness of the algorithm are verified, and pseudo code and flowchart for the simulation programming are introduced. Later in two simulation studies, different driving conditions are generated and results have been discussed and analyzed by detail.

In this paper, a new approach to optimize an Autonomous Underwater Vehicle (AUV) hull geometry is presented. Using this methode, the nose and tail of an underwater Vehicle are designed, such that their length constraints due to the arrangement of different components in the AUV body are properly addressed. In the current study, an optimal design for the body profile of a torpedo-shaped AUV is conducted, and a multi-objective optimization scheme based on the optimization algorithm Nondominated Sorting Genetic Algorithm-II (NSGA-II), as an evolutionary algorithm is employed. In addition, predefined geometrical constraints were considered so that equipment with the specific dimensions can be placed inside the AUV space without any effect on the AUV volume and the wetted surface. By optimizing the parameters of the newly presented profile, in addition to maximizing the volume and minimizing the wetted surface area, more diversed shapes can be achieved than with the ‘ Myring’ profile. A CFD analysis of the final optimal design indicates that with the help of the proposed profile, the hydrodynamic parameters for the AUV hull were effectively improved.

Today’s semi-Autonomous Vehicles are gradually moving towards full autonomy. This transition requires developing effective control algorithms for handing complex Autonomous tasks. Driving as a group of Vehicles, referred to as a convoy, on automated highways is a highly important and challenging task that Autonomous driving systems must deal with. This paper considers the control problem of a Vehicle convoy modeled with linear dynamics. The convoy formation requirement is presented in terms of a quadratic performance index to minimize. The convoy formation control is formulated as a receding horizon linear-quadratic (LQ) optimal control problem. The receding horizon control law is innovatively defined via the solution to the algebraic Riccati equation. The solution matrix and therefore the receding horizon control law are obtained in the closed-form. A control architecture consisting of four algorithms is proposed to handle formation size/shape switching. The closed-form control law is at the core of these algorithms. Simulation results are provided to justify the models, solutions, and proposed algorithms.

Nowadays, one of the main challenges in the maritime is the control and monitoring of ports in different weather conditions. To achieve this, Autonomous Surface Vehicles (ASV) are used. In this research, as part of the Morvarid research project, a stereo vision system was implemented on an ASV to detect, localize, and track static and dynamic objects. In order to examine the proposed obstacle detection algorithm, two sets of experiments have been designed; first, the ASV moves toward stationary objects to localize all static and dynamic objects. Second, the ASV tracks a target boat, which equipped with an RTK-GPS and estimates relative and absolute positions and movement vector. The results showed that the proposed algorithm successfully estimated 3D size and position of the surrounding objects. The accuracy of the tracking algorithm was evaluated in the digital elevation map, with the lowest RMSE of 1. 575 (m), and 0. 6621 (m) using raw and EKF data, respectively.

Nowadays Autonomous Underwater Vehicles (AUVs) are an unavoidable part of marine industries. One of the most important parts of any Autonomous Vehicle is the control issue to achieve the desired performance. This paper is concerned with speed control of an AUV model respecting the state and control constraints. According to the Newton-Euler method, the 6 DOF kinematic and dynamic models of the AUV are established. A well-defined performance index and constrained finite horizon optimization program in the form of Model Predictive Control (MPC) strategy is proposed to regulate the horizontal speed of AUV to its desired value while the constraints on the states like depth and control signals are considered in finite time horizon optimization program to be satisfied. The main problem for such a situation is the interaction between speed control and depth deviation then quadratic programing technique managed responses to avoid state and control signal constraints. Simulation results show a reliable performance of proposed MPC strategy to control the horizontal speed of AUV while all the constraints on state, control signal and also the variation of the control signal are satisfied.

Due to the limited resources of fossil fuels and the pollution caused by them, the use of clean fuels seems necessary. In this regard, the development of electric or hybrid Vehicles has great importance. In recent years, research areas related to Autonomous electric Vehicles have attracted the attention of automotive researchers and industrialists. These cars have different categories in terms of capabilities, including intelligent Vehicle speed control. In this paper, dynamic modeling and longitudinal speed control of a level-one Autonomous electric Vehicle using GT-SUITE and MATLAB are investigated. Also, the model created in GT-SUITE software is validated with the help of data obtained from open-loop experimental tests. Then, after connecting the two software, the proportional-integral controller is designed to control the longitudinal speed of the Vehicle and its parameters are adjusted. Finally, the performance of the designed controller in controlling the Vehicle speed in the presence of various conditions (road slope, wind blowing, and different velocity reference inputs) is evaluated.