4,633 research outputs found
Transfer Learning-Based Crack Detection by Autonomous UAVs
Unmanned Aerial Vehicles (UAVs) have recently shown great performance
collecting visual data through autonomous exploration and mapping in building
inspection. Yet, the number of studies is limited considering the post
processing of the data and its integration with autonomous UAVs. These will
enable huge steps onward into full automation of building inspection. In this
regard, this work presents a decision making tool for revisiting tasks in
visual building inspection by autonomous UAVs. The tool is an implementation of
fine-tuning a pretrained Convolutional Neural Network (CNN) for surface crack
detection. It offers an optional mechanism for task planning of revisiting
pinpoint locations during inspection. It is integrated to a quadrotor UAV
system that can autonomously navigate in GPS-denied environments. The UAV is
equipped with onboard sensors and computers for autonomous localization,
mapping and motion planning. The integrated system is tested through
simulations and real-world experiments. The results show that the system
achieves crack detection and autonomous navigation in GPS-denied environments
for building inspection
A Probabilistic Framework for Imitating Human Race Driver Behavior
Understanding and modeling human driver behavior is crucial for advanced
vehicle development. However, unique driving styles, inconsistent behavior, and
complex decision processes render it a challenging task, and existing
approaches often lack variability or robustness. To approach this problem, we
propose Probabilistic Modeling of Driver behavior (ProMoD), a modular framework
which splits the task of driver behavior modeling into multiple modules. A
global target trajectory distribution is learned with Probabilistic Movement
Primitives, clothoids are utilized for local path generation, and the
corresponding choice of actions is performed by a neural network. Experiments
in a simulated car racing setting show considerable advantages in imitation
accuracy and robustness compared to other imitation learning algorithms. The
modular architecture of the proposed framework facilitates straightforward
extensibility in driving line adaptation and sequencing of multiple movement
primitives for future research.Comment: updated references [17] and [33]; added journal inf
Pathfinder autonomous rendezvous and docking project
Capabilities are being developed and demonstrated to support manned and unmanned vehicle operations in lunar and planetary orbits. In this initial phase, primary emphasis is placed on definition of the system requirements for candidate Pathfinder mission applications and correlation of these system-level requirements with specific requirements. The FY-89 activities detailed are best characterized as foundation building. The majority of the efforts were dedicated to assessing the current state of the art, identifying desired elaborations and expansions to this level of development and charting a course that will realize the desired objectives in the future. Efforts are detailed across all work packages in developing those requirements and tools needed to test, refine, and validate basic autonomous rendezvous and docking elements
Vehicle and Traffic Safety
The book is devoted to contemporary issues regarding the safety of motor vehicles and road traffic. It presents the achievements of scientists, specialists, and industry representatives in the following selected areas of road transport safety and automotive engineering: active and passive vehicle safety, vehicle dynamics and stability, testing of vehicles (and their assemblies), including electric cars as well as autonomous vehicles. Selected issues from the area of accident analysis and reconstruction are discussed. The impact on road safety of aspects such as traffic control systems, road infrastructure, and human factors is also considered
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