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Mobile robot motion planning in unstructured dynamic environments is a challenging task. Thus, often suboptimal methods are employed which perform global path planning and local obstacle avoidance separately. This work introduces a holistic planning algorithm which is based on the concept of state

obstacle avoidance --- Pfadplanung --- motion planning --- autonomes Fahren --- HindernisvermeidungMobile robots --- path planning --- Mobile Roboter --- Bewegungsplanung --- autonomous driving

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This book presents the results of the successful Sensors Special Issue on Intelligent Vehicles that received submissions between March 2019 and May 2020. The Guest Editors of this Special Issue are Dr. David Fernández-Llorca, Dr. Ignacio Parra-Alonso, Dr. Iván García-Daza and Dr. Noelia Parra-Alonso, all from the Computer Engineering Department at the University of Alcalá (Madrid, Spain). A total of 32 manuscripts were finally accepted between 2019 and 2020, presented by top researchers from all over the world. The reader will find a well-representative set of current research and developments related to sensors and sensing for intelligent vehicles. The topics of the published manuscripts can be grouped into seven main categories: (1) assistance systems and automatic vehicle operation, (2) vehicle positioning and localization, (3) fault diagnosis and fail-x systems, (4) perception and scene understanding, (5) smart regenerative braking systems for electric vehicles, (6) driver behavior modeling and (7) intelligent sensing. We, the Guest Editors, hope that the readers will find this book to contain interesting papers for their research, papers that they will enjoy reading as much as we have enjoyed organizing this Special Issue

tracking-by-detection --- multi-vehicle tracking --- Siamese network --- data association --- Markov decision process --- driving behavior --- real-time monitoring --- driver distraction --- mobile application --- portable system --- simulation test --- dynamic driving behavior --- traffic scene augmentation --- corridor model --- IMU --- vision --- classification networks --- Hough transform --- lane markings detection --- semantic segmentation --- transfer learning --- autonomous --- off-road driving --- tire-road forces estimation --- slip angle estimation --- gauge sensors --- fuzzy logic system --- load transfer estimation --- simulation results --- normalization --- lateral force empirical model --- driver monitor --- lane departure --- statistical process control --- fault detection --- sensor fault --- signal restoration --- intelligent vehicle --- autonomous vehicle --- kinematic model --- visual SLAM --- sparse direct method --- photometric calibration --- corner detection and filtering --- loop closure detection --- road friction coefficient --- tire model --- nonlinear observer --- self-aligning torque --- lateral displacement --- Lyapunov method --- automatic parking system (APS) --- end-to-end parking --- reinforcement learning --- parking slot tracking --- deceleration planning --- multi-layer perceptron --- smart regenerative braking --- electric vehicles --- vehicle speed prediction --- driver behavior modeling --- electric vehicle control --- driver characteristics online learning --- objects’ edge detection --- stixel histograms accumulate --- point cloud segmentation --- autonomous vehicles --- scene understanding --- occlusion reasoning --- road detection --- advanced driver assistance system --- trajectory prediction --- risk assessment --- collision warning --- connected vehicles --- vehicular communications --- vulnerable road users --- fail-operational systems --- fall-back strategy --- automated driving --- advanced driving assistance systems --- illumination --- shadow detection --- shadow edge --- image processing --- traffic light detection --- intelligent transportation system --- lane-changing --- merging maneuvers --- game theory --- decision-making --- intelligent vehicles --- model predictive controller --- automatic train operation --- softness factor --- fusion velocity --- online obtaining --- hardware-in-the-loop simulation --- driving assistant --- driving diagnosis --- accident risk maps --- driving safety --- intelligent driving --- virtual test environment --- millimeter wave radar --- lane-change decision --- risk perception --- mixed traffic --- minimum safe deceleration --- automated driving system (ADS) --- sensor fusion --- multi-lane detection --- particle filter --- self-driving car --- unscented Kalman filter --- vehicle model --- Monte Carlo localization --- millimeter-wave radar --- square-root cubature Kalman filter --- Sage-Husa algorithm --- target tracking --- stationary and moving object classification --- localization --- LiDAR --- GNSS --- Global Positioning System (GPS) --- monte carlo --- autonomous driving --- robot motion --- path planning --- piecewise linear approximation --- multiple-target path planning --- autonomous mobile robot --- homotopy based path planning --- LiDAR signal processing --- sensor and information fusion --- advanced driver assistance systems --- autonomous racing --- high-speed camera --- real-time systems --- LiDAR odometry --- fail-aware --- sensors --- sensing --- percepction --- object detection and tracking --- scene segmentation --- vehicle positioning --- fail-x systems --- driver behavior modelling --- automatic operation

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The biennial Congress of the Italian Society of Oral Pathology and Medicine (SIPMO) is an International meeting dedicated to the growing diagnostic challenges in the oral pathology and medicine field. The III International and XV National edition will be a chance to discuss clinical conditions which are unusual, rare, or difficult to define. Many consolidated national and international research groups will be involved in the debate and discussion through special guest lecturers, academic dissertations, single clinical case presentations, posters, and degree thesis discussions. The SIPMO Congress took place from the 17th to the 19th of October 2019 in Bari (Italy), and the enclosed copy of Proceedings is a non-exhaustive collection of abstracts from the SIPMO 2019 contributions.

modeling --- underwater vehicle --- gesture-based language --- text classification --- navigation and control --- motion constraints --- autonomy --- dynamics --- marine robotics --- unmanned surface vehicle --- field trials --- actuator constraints --- robust control --- fault detection and isolation --- remotely operated vehicle --- underwater manipulator --- intelligent control --- object obstacle avoidance --- submersible vehicles --- overcome strong sea current --- underwater robot --- maneuverability identification --- ROV --- Lyapunov stability --- VGI --- ocean research --- two-ray --- path loss --- obstacle avoidance --- parallel control --- approximated optimal control --- sliding mode control --- automation systems --- fault-tolerant control --- numerical calculation --- backstepping control --- deep learning --- unmanned underwater vehicle (UUV) --- underwater human–robot interaction --- aerial underwater vehicle --- thruster fault --- airmax --- position control --- cross-medium --- free space --- second path planning --- flow sensing --- underwater vehicle-manipulator system --- marine systems --- low-level control --- dynamic modelling --- kinematics --- vehicle dynamics --- WLAN --- viscous hydrodynamics --- fault accommodation --- RSSI --- nonlinear systems --- guidance --- simulation model --- artificial lateral system --- autonomous underwater vehicle --- typhoon disaster --- force control

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The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences.

Technology: general issues --- History of engineering & technology --- neuron PID --- Kalman filtering --- omnidirectional mobile robot --- implementations --- anti-windup --- fault tolerance --- reconfigurable control --- Maglev --- neural networks --- artificial intelligence --- unmanned tracked vehicle --- inertial parameters --- vehicle-terrain interaction --- identification --- recursive least square with exponential forgetting --- generalized Newton–Raphson --- Unscented Kalman Filter --- lane keeping control (LKC) --- non-smooth finite-time control --- previewed tracking --- error weight superposition --- electric vehicle (EV) --- ODD-based AD function design --- path tracking --- path planning --- software architecture --- interface design --- autonomous vehicle --- advanced driver-assistance system --- LPV approach --- robust control --- cruise control --- semi-active suspension control --- passenger comfort --- automated vehicles --- ADAS/AD functions --- C-ITS --- IVIM --- infrastructure assistance --- routing recommendations --- autonomous driving --- active learning --- formal methods --- model-based engineering --- automata learning --- unmanned vehicle --- nonlinear model prediction controller --- trajectory tracking --- outdoor field test --- vehicle following --- path following --- splines --- spline approximation --- n/a --- generalized Newton-Raphson

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Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens.

star image --- image denoising --- reinforcement learning --- maximum likelihood estimation --- mixed Poisson–Gaussian likelihood --- machine learning-based classification --- non-uniform foundation --- stochastic analysis --- vehicle–pavement–foundation interaction --- forest growing stem volume --- coniferous plantations --- variable selection --- texture feature --- random forest --- red-edge band --- on-shelf availability --- semi-supervised learning --- deep learning --- image classification --- machine learning --- explainable artificial intelligence --- wildfire --- risk assessment --- Naïve bayes --- transmission-line corridors --- image encryption --- compressive sensing --- plaintext related --- chaotic system --- convolutional neural network --- color prior model --- object detection --- piston error detection --- segmented telescope --- BP artificial neural network --- modulation transfer function --- computer vision --- intelligent vehicles --- extrinsic camera calibration --- structure from motion --- convex optimization --- temperature estimation --- BLDC --- electric machine protection --- touchscreen --- capacitive --- display --- SNR --- stylus --- laser cutting --- quality monitoring --- artificial neural network --- burr formation --- cut interruption --- fiber laser --- semi-supervised --- fuzzy --- noisy --- real-world --- plankton --- marine --- activity recognition --- wearable sensors --- imbalanced activities --- sampling methods --- path planning --- Q-learning --- neural network --- YOLO algorithm --- robot arm --- target reaching --- obstacle avoidance