106 research outputs found

    A Novel Obstacle Avoidance Approach For Nonholonomic Ground Vehicle Autonomy

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    Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2012Thesis (PhD) -- İstanbul Technical University, Institute of Science and Technology, 2012Bu çalışmada, holonom olmayan bir kara taşıtı için, “Boşluğu Takip Et” (BTE) isimli yeni bir engelden kaçma ve çarpışma önleme metodu geliştirilmiştir. Bu metod, probleme yeni bir çözüm getirmektedir ve diğer metodlara göre çeşitli avantajlara sahiptir. Geliştirilen metodun, benzer metodlarla yapılan karşılaştırılmalar sonucunda, daha güvenli güzergahlarla sonuçlandığı gösterilmiştir. Ayrıca BTE, yapay potansiyel alanlar (YPA) metodu ve bu tabanda çalışan diğer tüm metodların ortak problemi olan lokal minimum probleminden bağımsızdır. BTE’nin bir diğer özelliği, aracın holonom olmayan kısıtlarını ve sensörlerin görüş açısı kısıtlarını da göz önünde bulundurabilmesidir. BTE’nin tamamen reaktif yapısı sayesinde, yalnızca durağan engellerden değil, hareketli engellerden de rahatlıkla sıyrıldığı da tez içerisinde gösterilmiştir. Son olarak, sadece bir ayar parametresine sahip olduğu için, kullanımı da oldukça kolaydır. Engelden kaçınmak için, yalnızca aracın yönelim açısının belirlenmesinin yetmeyeceği düşüncesinden hareketle, aracın engelli bir ortamda hız planlaması için de yeni bir metod geliştirilmiştir. İki adet bulanık çıkarım sisteminin (BÇS) tasarlanmasıyla oluşturulan bu yeni yapı, engellerin oluşturduğu risk durumuna ve aracın yönelim açısına bağlı olarak çalışır. Planlanan hızın takip edilmesi için de yine bulanık mantık kullanılarak yeni bir alt seviye hız kontrolörü tasarlanmıştır. Tasarlanan tüm metodlar, literatürdeki bezerleriyle simülasyon ortamında karşılaştırılmış ve sonuçları gösterilmiştir. Geliştirilen her üç yeni metod, tam otonom kara taşıtı (OKT) üzerinde deneysel olarak da test edilerek sonuçların başarılı olduğu gösterilmiştir. Simülasyonlarda kullanılan araç modelleri ve deneysel düzeneğin tasarımı da tez içerisinde ayrı bölümler halinde anlatılmıştır.In this study, a new obstacle avoidance algorithm “Follow the Gap Method” (FGM) is designed for nonholonomic ground vehicle autonomy. The proposed method brings a new solution to the problem and has several advantages compared to previous methods. Fisrstly, the FGM results in safer trajectories than other compared approaches. This new method is free from local minima which is a big problem for Artificial Potential Fields (APF) and similar methods. Taking into consideration the field of view and the nonholonomic constraints of the vehicle is another advantage of the FGM. Through the purely reactive nature of the FGM, it is shown that not only the static but also the dynamic obstacles are avoided. Besides these, it is easy to tune the algorithm with only one tuning parameter. Vehicle speed is as important as the appropriate steering angle for obstacle avoidance. From this view point, a new speed planning method is designed for the vehicle. Two fuzzy inference systems operate depending on the danger level of the obstacles and the steering angle. In order to track the speed commands from the speed planner, a new low level speed controller is designed based on fuzzy rules. All designed methods are simulated and compared with other methods in literature. The designed methods are also tested experimentally using the real unmanned ground vehicle (UGV) platform and it is shown that experimental results are successful too. The used models for the simulations and designed experimental platform are illustrated in separated sections throughout the thesis.DoktoraPh

    DEVELOPMENT OF AUTONOMOUS VEHICLE MOTION PLANNING AND CONTROL ALGORITHM WITH D* PLANNER AND MODEL PREDICTIVE CONTROL IN A DYNAMIC ENVIRONMENT

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    The research in this report incorporates the improvement in the autonomous driving capability of self-driving cars in a dynamic environment. Global and local path planning are implemented using the D* path planning algorithm with a combined Cubic B-Spline trajectory generator, which generates an optimal obstacle free trajectory for the vehicle to follow and avoid collision. Model Predictive Control (MPC) is used for the longitudinal and the lateral control of the vehicle. The presented motion planning and control algorithm is tested using Model-In-the-Loop (MIL) method with the help of MATLAB® Driving Scenario Designer and Unreal Engine® Simulator by Epic Games®. Different traffic scenarios are built, and a camera sensor is configured to simulate the sensory data and feed it to the controller for further processing and vehicle motion planning. Simulation results of vehicle motion control with global and local path planning for dynamic obstacle avoidance are presented. The simulation results show that an autonomous vehicle follows a commanded velocity when the relative distance between the ego vehicle and an obstacle is greater than a calculated safe distance. When the relative distance is close to the safe distance, the ego vehicle maintains the headway. When an obstacle is detected by the ego vehicle and the ego vehicle wants to pass the obstacle, the ego vehicle performs obstacle avoidance maneuver by tracking desired lateral positions

    Development of Fault Diagnosis and Fault Tolerant Control Algorithms with Application to Unmanned Systems

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    Unmanned vehicles have been increasingly employed in real life. They include unmanned air vehicles (UAVs), unmanned ground vehicles (UGVs), unmanned spacecrafts, and unmanned underwater vehicles (UUVs). Unmanned vehicles like any other autonomous systems need controllers to stabilize and control them. On the other hand unmanned systems might subject to different faults. Detecting a fault, finding the location and severity of it, are crucial for unmanned vehicles. Having enough information about a fault, it is needed to redesign controller based on post fault characteristics of the system. The obtained controlled system in this case can tolerate the fault and may have a better performance. The main focus of this thesis is to develop Fault Detection and Diagnosis (FDD) algorithms, and Fault Tolerant Controllers (FTC) to increase performance, safety and reliability of various missions using unmanned systems. In the field of unmanned ground vehicles, a new kinematical control method has been proposed for the trajectory tracking of nonholonomic Wheeled Mobile Robots (MWRs). It has been experimentally tested on an UGV, called Qbot. A stable leader-follower formation controller for time-varying formation configuration of multiple nonholonomic wheeled mobile robots has also been presented and is examined through computer simulation. In the field of unmanned aerial vehicles, Two-Stage Kalman Filter (TSKF), Adaptive Two-Stage Kalman Filter (ATSKF), and Interacting Multiple Model (IMM) filter were proposed for FDD of the quadrotor helicopter testbed in the presence of actuator faults. As for space missions, an FDD algorithm for the attitude control system of the Japan Canada Joint Collaboration Satellite - Formation Flying (JC2Sat-FF) mission has been developed. The FDD scheme was achieved using an IMM-based FDD algorithm. The efficiency of the FDD algorithm has been shown through simulation results in a nonlinear simulator of the JC2Sat-FF. A fault tolerant fuzzy gain-scheduled PID controller has also been designed for a quadrotor unmanned helicopter in the presence of actuator faults. The developed FDD algorithms and fuzzy controller were evaluated through experimental application to a quadrotor helicopter testbed called Qball-X4

    Reinforcement and Curriculum Learning for Off-Road Navigation of an UGV with a 3D LiDAR

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    This paper presents the use of deep Reinforcement Learning (RL) for autonomous navigation of an Unmanned Ground Vehicle (UGV) with an onboard three-dimensional (3D) Light Detection and Ranging (LiDAR) sensor in off-road environments. For training, both the robotic simulator Gazebo and the Curriculum Learning paradigm are applied. Furthermore, an Actor–Critic Neural Network (NN) scheme is chosen with a suitable state and a custom reward function. To employ the 3D LiDAR data as part of the input state of the NNs, a virtual two-dimensional (2D) traversability scanner is developed. The resulting Actor NN has been successfully tested in both real and simulated experiments and favorably compared with a previous reactive navigation approach on the same UGV.Partial funding for open access charge: Universidad de Málag

    Mobile Robots Navigation

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    Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

    Design and Development of an Automated Mobile Manipulator for Industrial Applications

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    This thesis presents the modeling, control and coordination of an automated mobile manipulator. A mobile manipulator in this investigation consists of a robotic manipulator and a mobile platform resulting in a hybrid mechanism that includes a mobile platform for locomotion and a manipulator arm for manipulation. The structural complexity of a mobile manipulator is the main challenging issue because it includes several problems like adapting a manipulator and a redundancy mobile platform at non-holonomic constraints. The objective of the thesis is to fabricate an automated mobile manipulator and develop control algorithms that effectively coordinate the arm manipulation and mobility of mobile platform. The research work starts with deriving the motion equations of mobile manipulators. The derivation introduced here makes use of motion equations of robot manipulators and mobile platforms separately, and then integrated them as one entity. The kinematic analysis is performed in two ways namely forward & inverse kinematics. The motion analysis is performed for various WMPs such as, Omnidirectional WMP, Differential three WMP, Three wheeled omni-steer WMP, Tricycle WMP and Two steer WMP. From the obtained motion analysis results, Differential three WMP is chosen as the mobile platform for the developed mobile manipulator. Later motion analysis is carried out for 4-axis articulated arm. Danvit-Hartenberg representation is implemented to perform forward kinematic analysis. Because of this representation, one can easily understand the kinematic equation for a robotic arm. From the obtained arm equation, Inverse kinematic model for the 4-axis robotic manipulator is developed. Motion planning of an intelligent mobile robot is one of the most vital issues in the field of robotics, which includes the generation of optimal collision free trajectories within its work space and finally reaches its target position. For solving this problem, two evolutionary algorithms namely Particle Swarm Optimization (PSO) and Artificial Immune System (AIS) are introduced to move the mobile platform in intelligent manner. The developed algorithms are effective in avoiding obstacles, trap situations and generating optimal paths within its unknown environments. Once the robot reaches its goal (within the work space of the manipulator), the manipulator will generate its trajectories according to task assigned by the user. Simulation analyses are performed using MATLAB-2010 in order to validate the feasibility of the developed methodologies in various unknown environments. Additionally, experiments are carried out on an automated mobile manipulator. ATmega16 Microcontrollers are used to enable the entire robot system movement in desired trajectories by means of robot interface application program. The control program is developed in robot software (Keil) to control the mobile manipulator servomotors via a serial connection through a personal computer. To support the proposed control algorithms both simulation and experimental results are presented. Moreover, validation of the developed methodologies has been made with the ER-400 mobile platform

    Advances in Robot Navigation

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    Robot navigation includes different interrelated activities such as perception - obtaining and interpreting sensory information; exploration - the strategy that guides the robot to select the next direction to go; mapping - the construction of a spatial representation by using the sensory information perceived; localization - the strategy to estimate the robot position within the spatial map; path planning - the strategy to find a path towards a goal location being optimal or not; and path execution, where motor actions are determined and adapted to environmental changes. This book integrates results from the research work of authors all over the world, addressing the abovementioned activities and analyzing the critical implications of dealing with dynamic environments. Different solutions providing adaptive navigation are taken from nature inspiration, and diverse applications are described in the context of an important field of study: social robotics

    Next generation main battle tank. Part II: Converting old MBTS into unmanned MBTS (UMBT)

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    Modern MBTs (Main Battle Tank) are extremely expensive. Many outdated MBTs and other armored vehicles, often lacking the required armor protection, are still kept in depots. It is now convenient to upgrade them to optionally unmanned weapons by adding a humanoid driver, and a robotic arm as a loader. Sensors, an optional automatic driving system, a control and communication suite would complete the transformation. The main armament and secondary armament may be also changed or upgraded. The off-the-shelf huge electronic equipment can be installed wireless inside the hull. The old crew compartment may be spoiled of all the human related parts. Only the driver seat may be kept in order to leave the capability to remove the humanoid, robotized driver and reinstate the human one. This upgrade should also include a diagnostic system for the vehicle, the sensors and the additional systems to reduce the maintenance burden. An additional, specialized, lightweight armor suite should be focused to protect the mobilization system, the robots, the control and the communication system. This second part of the paper introduces a few options to convert the Leopard 1 MBT to an optionally piloted UMBT (Unmanned Main Battle Tank). A first, minimal step, is just the automation of the original tank. In a second step, the weight is reduced by installing a smaller 60mm cannon with a lighter, but more numerous ammunition storage. A third step increases the firepower by installing on the main turret an automated turret with a 12.7 or 30mm cannon with an optional additional 7.62 machinegun. It is also highly advisable to add an APU (Auxiliary Power Unit) and a battery to reduce IR (infrared) signature, improve main engine life and reduce maintenance
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