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In numerous infrastructure health monitoring and inspection applications, swift and precise three-dimensional reconstruction of subsurface objects from ground penetrating radar (GPR) data is of critical importance, particularly given the recent advancements in perception modeling and the emergence of deep learning. Nonetheless, current research on the reconstruction of subsurface infrastructure scenes and objects faces limitations. Owing to the restrictions of conventional GPR data processing, these methodologies are prone to GPR data with noisy backgrounds and struggle to recreate noncylindrical objects. This paper investigates the back-projection (BP) approach for GPR-based three-dimensional (3D) subsurface target reconstruction and presents a learning model that formulates the reconstruction as an implicit BP from 2D to 3D representations, circumventing any preprocessing requirements in contrast to traditional techniques. The proposed learned model ultimately generates an explicit volumetric representation of the subsurface objects. Experimental results show at least a 33% enhancement in the performance of the proposed model compared to meticulously designed baselines. [ABSTRACT FROM AUTHOR]

Human‐made concrete structures require cutting‐edge inspection tools to ensure the quality of the construction to meet the applicable building codes and to maintain the sustainability of the aging infrastructure. This paper introduces a wall‐climbing robot for metric concrete inspection that can reach difficult‐to‐access locations with a close‐up view for visual data collection and real‐time flaws detection and localization. The wall‐climbing robot is able to detect concrete surface flaws (i.e., cracks and spalls) and produce a defect‐highlighted 3D model with extracted location clues and metric measurements. The system encompasses four modules, including a data collection module to capture RGB‐D frames and inertial measurement unit data, a visual–inertial navigation system module to generate pose‐coupled keyframes, a deep neural network module (namely InspectionNet) to classify each pixel into three classes (background, crack, and spall), and a semantic reconstruction module to integrate per‐frame measurement into a global volumetric model with defects highlighted. We found that commercial RGB‐D camera output depth is noisy with holes, and a Gussian‐Bilateral filter for depth completion is introduced to inpaint the depth image. The method achieves the state‐of‐the‐art depth completion accuracy even with large holes. Based on the semantic mesh, we introduce a coherent defect metric evaluation approach to compute the metric measurement of crack and spall area (e.g., length, width, area, and depth). Field experiments on a concrete bridge demonstrate that our wall‐climbing robot is able to operate on a rough surface and can cross over shallow gaps. The robot is capable to detect and measure surface flaws under low illuminated environments and texture‐less environments. Besides the robot system, we create the first publicly accessible concrete structure spalls and cracks data set that includes 820 labeled images and over 10,000 field‐collected images and release it to the research community. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is a very useful nondestructive evaluation (NDE) device for locating and mapping underground assets prior to digging and trenching efforts in construction. This paper presents a novel robotic system to automate the GPR data-collection process, localize underground utilities, and interpret and reconstruct the underground objects for better visualization, allowing regular nonprofessional users to understand the survey results. This system is composed of three modules: (1) an omnidirectional robotic data-collection platform that carries a RGB-D camera with inertial measurement unit (IMU) and a GPR antenna to perform automatic GPR data collection and tag each GPR measurement with visual positioning information at every sampling step, (2) a learning-based migration module to interpret the raw GPR B-scan image into a two-dimensional (2D) cross-section model of objects, and (3) a three-dimensional (3D) reconstruction module, i.e., 30.0% GPRNet, to generate underground utility model represented as fine 3D point cloud. Comparative studies were performed on synthetic data and field GPR raw data with various incompleteness and noise. Experimental results demonstrated that our proposed method achieves a higher GPR imaging accuracy in mean intersection over union (IoU) than the conventional back-projection (BP) migration approach, and 6.9%−7.2% less loss in Chamfer distance (CD) than point cloud model reconstruction baseline methods. The GPR-based robotic inspection provides an effective tool for civil engineers to detect and survey underground utilities before construction. [ABSTRACT FROM AUTHOR]

This paper presents a new holistic vision-based mobile assistive navigation system to help blind and visually impaired people with indoor independent travel. The system detects dynamic obstacles and adjusts path planning in real-time to improve navigation safety. First, we develop an indoor map editor to parse geometric information from architectural models and generate a semantic map consisting of a global 2D traversable grid map layer and context-aware layers. By leveraging the visual positioning service (VPS) within the Google Tango device, we design a map alignment algorithm to bridge the visual area description file (ADF) and semantic map to achieve semantic localization. Using the on-board RGB-D camera, we develop an efficient obstacle detection and avoidance approach based on a time-stamped map Kalman filter (TSM-KF) algorithm. A multi-modal human-machine interface (HMI) is designed with speech-audio interaction and robust haptic interaction through an electronic SmartCane. Finally, field experiments by blindfolded and blind subjects demonstrate that the proposed system provides an effective tool to help blind individuals with indoor navigation and wayfinding. [ABSTRACT FROM AUTHOR]

We present a novel collaborative mapping and autonomous parking system for semi-structured multi-story parking garages, based on cooperative 3-D LiDAR point cloud registration and Bayesian probabilistic updating. First, an inertial-enhanced (IE) generalized iterative closest point (G-ICP) approach is presented to perform high accuracy registration for LiDAR odometry, which is loosely coupled with inertial measurement unit using multi-state extended Kalman filter fusion. Second, the IE G-ICP is utilized to reconstruct the 3-D point cloud model for each vehicle, and then the individual model maps are merged and updated into a global probabilistic 2-D grid map. A collaborative multiple layer semantic map is constructed to support autonomous parking. Finally, we propose a collaborative navigation approach for path planning when there are multiple vehicles in the parking garage through vehicle-to-vehicle communication. A global path planner is designed to explore the minimum cost path based on the semantic map, and local motion planning is performed using a random exploring algorithm for obstacle avoidance and path smoothing. Our pilot experimental evaluation provides a proof of concept for indoor autonomous parking by collaborative perception, map merging, and updating methodologies. [ABSTRACT FROM PUBLISHER]

Real-time orientation estimation using low-cost inertial sensors is essential for all the applications where size and power consumption are critical constraints. Such applications include robotics, human motion analysis, and mobile devices. This paper presents a linear Kalman filter for magnetic angular rate and gravity sensors that processes angular rate, acceleration, and magnetic field data to obtain an estimation of the orientation in quaternion representation. Acceleration and magnetic field observations are preprocessed through a novel external algorithm, which computes the quaternion orientation as the composition of two algebraic quaternions. The decoupled nature of the two quaternions makes the roll and pitch components of the orientation immune to magnetic disturbances. The external algorithm reduces the complexity of the filter, making the measurement equations linear. Real-time implementation and the test results of the Kalman filter are presented and compared against a typical quaternion-based extended Kalman filter and a constant gain filter based on the gradient-descent algorithm. [ABSTRACT FROM AUTHOR]

Most existing source search algorithms suffer from a high travel cost, and few of them have been analyzed in performance in noisy environments where local basins are presented. In this paper, the theseus gradient search (TGS) is proposed to effectively overcome local basins in search. Analytical performances of TGS and the gradient ascend with correlated random walk (GACRW), which is a variant of correlated random walk, are derived and compared. A gradient field model is proposed as an analytical tool that makes it feasible to analyze the performances. The analytical average searching costs of GACRW and TGS are obtained for the first time for this class of algorithms in the environments with local basins. The costs, expressed as functions of searching space size, local basin size, and local basin number are confirmed by simulation results. The performances of GACRW, TGS, and two chemotaxis algorithms are compared in the gradient field and a scenario of indoor radio source search in a hallway driven by real data of signal strengths. The results illustrate that GACRW and TGS are robust to noisy gradients and are more competitive than the chemotaxis-based algorithms in real applications. Both analytical and simulation results indicate that in the presence of local basins, TGS almost always costs the lowest. [ABSTRACT FROM AUTHOR]

This paper focuses on the automatic generation of forward kinematics of a kind of modular reconfigurable robot. Based on the modularized division of robots and the communication between the host and the modules, a configuration recognition method is proposed. By using the graph theory, the method of topological analysis is proposed, and the assembly incidence matrix (AIM) and path matrix are derived. Subsequently, based on the results of topological analysis and the definition of module frames, the initial poses and twists of a robot are obtained. To deal with the multi-chain robots, the entries of the path matrix are employed to enable dyads to appear or not to appear in the kinematic equations. Then, the forward kinematics of the multi-chain robot is derived. An illustrative example and an experiment are presented. The results show that the method is valid and suitable for both single-open-chain robots and multi-chain robots. [ABSTRACT FROM AUTHOR]

SUMMARY This paper investigates the quadratic stability and stabilization of a class of matrix second-order time-varying systems. All the system matrices including the second-order differential coefficient matrix are assumed to have the time-varying norm-bounded parameters. Necessary and sufficient conditions for the quadratic stability and stabilization of such time-varying systems are derived. All the results are obtained in terms of linear matrix inequalities. Two illustrative examples are given to show that our results are effective and less conservative than the results obtained by other researchers. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, we present a multirobot exploration algorithm that aims at reducing the exploration time and to minimize the overall traverse distance of the robots by coordinating the movement of the robots performing the exploration. Modeling the environment as a tree, we consider a coordination model that restricts the number of robots allowed to traverse an edge and to enter a vertex during each step. This coordination is achieved in a decentralized manner by the robots using a set of active landmarks that are dropped by them at explored vertices. We mathematically analyze the algorithm on trees, obtaining its main properties and specifying its bounds on the exploration time. We also define three metrics of performance for multirobot algorithms. We simulate and compare the performance of this new algorithm with those of our multirobot depth first search (MR-DFS) approach presented in our recent paper and classic single-robot DFS. [ABSTRACT FROM AUTHOR]

A four-beam slot array antenna, which has a cylinder conformal structure based on substrate integrated waveguide (SIW) technology, is designed and fabricated for the first time. Through the commercial software Microwave Studio Office CST optimization of the flat structure parameters, which are calculated using the planar SIW slots array antenna and Butler Matrix design method, the parameters of conformal structure are obtained. The fabricated phase array offers four beams with 16 dB gain at Ku band. The sweep range is around 160 degrees over azimuth direction. Meanwhile, the level of side lobes is reduced as low as -12 dB for all beam in E-plane. The usable bandwidth of the antenna is 3.5% at 11.5 GHz. [ABSTRACT FROM AUTHOR]

In this paper, we present an algorithm for the exploration of an unknown graph by multiple robots, which is never worse than depth-first search with a single robot. On trees, we prove that the algorithm is optimal for two robots. For k robots, the algorithm has an optimal dependence on the size of the tree but not on its radius. We believe that the algorithm performs well on any tree, and this is substantiated by simulations. For trees with e edges and radius r, the exploration time is less than 2e/k+(1+(k/r))^k-1(2/k!)r^k-1 =(2e/k) + O((k+r)^k-1) (for r>k, \le (2e/k) + 2r^k-1), thereby improving a recent method with time O((e/\log k) + r) refid="ref2"/, and almost reaching the lower bound max ((2e/k), 2r). The model underlying undirected-graph exploration is a set of rooms connected by opaque passages; thus, the algorithm is appropriate for scenarios like indoor navigation or cave exploration. In this framework, communication can be realized by bookkeeping devices being dropped by the robots at explored vertices, the states of which are read and changed by further visiting robots. Simulations have been performed in both tree and graph explorations to corroborate the mathematical results. [ABSTRACT FROM AUTHOR]

The City-Climber robot is a novel wall-climbing robot developed at The City College of New York that has the capability to move on floors, climb walls, walk on ceilings and transit between them. In this paper, we first develop the dynamic model of the City-Climber robot when it travel on different surfaces, i.e., floors, walls and ceilings, respectively. Then, we present a path planning method for the City-Climber robot using mixed integer linear programming (MILP) in three-dimensional (3-D) building environments that consist of objects with primitive geometrical shapes. MILP provides an optimization framework that can directly incorporate dynamic constraints with logical constraints such as obstacle avoidance and waypoint selection. In order to use MILP to solve the obstacle avoidance problem, we simplify and decouple the robot dynamic model into a linear system by introducing a restricting admissible controller. The decoupled model and obstacle can be rewritten as a linear program with mixed-integer linear constraints that account for the collision avoidance. A key benefit of this approach is that the path optimization can be readily solved using the AMPL and CPLEX optimization software with a MATLAB interface. Simulation results show that the framework of MILP is well suited for path planning and obstacle avoidance problems for the wall-climbing robot in 3-D environments. [ABSTRACT FROM AUTHOR]

Abstract: A great variety of biological groups form a self-organized swarming motion at some point during their life spans, which has two prominent collective features: common velocity and constant spacings among members. In this paper, we present a general individual-based motion framework to explain such collective motion of swarms in homogeneous environments. The motion framework utilizes the concept of social interactions that has been widely accepted throughout the literature. We assume that during the motion of the swarm, each member senses and interacts with its neighbors via virtual Attraction/Alignment/Repulsion (A/A/R) forces, while perceiving and following the gradient force of the environment. During the swarm''s motion, the neighborhood and the interaction relations among members may dynamically change. To explicitly consider the effect of such dynamic change on the emergence of swarm''s collective behavior, we use an algebraic graph to model the topology of the interaction and the neighborhood relations among the members. By using mathematical tools of nonsmooth analysis theory and Lyapunov stability theory, we analytically prove that if the A/A/R forces have limited ranges, and the attraction/repulsion forces are balanced at a certain range, the proposed framework leads to a parallel type of collective motion of the swarm. We mathematically show that the velocities of all swarm members asymptotically converge to a common value and the spacings among neighbors remain unchanging. In addition to the mathematical analysis, a few sets of simulation results are included to demonstrate the presented framework. The contributions of this paper are twofold: First, unlike most works in the literature that mainly use computer simulations to study the swarming phenomena, this paper provides an analytical methodology to investigate how the collective group behavior is self-organized by individual motions. Second, the presented motion framework works over a general range of A/A/R interactions. In other words, we analytically prove that the commonly used A/A/R model can lead to a collective motion of the swarm. In addition, we show that the alternative model in the literature that uses only attraction/repulsion (A/R) interactions is in fact a special case of the A/A/R model. [Copyright &y& Elsevier]

This paper presents a fuzzy control system that incorporates sensing, control and planning to improve the performance of the wall-climbing microrobots in unstructured environments. After introduction of the robot system, a task reference method is proposed which is based on a fuzzy multisensor data fusion scheme. The method provides a novel mechanism to efficiently integrate task scheduling, action planning and motion control in a unified framework. A robot gait generation method is described which switches the robot locomotion between different motion modes with the help of a finite state machine driven by sensory information. A fuzzy motion controller is designed to improve control performance and reduce power consumption by the suitable selection of fuzzy sets and inference methods, as well as the definition of corresponding membership functions and control rule bases. A fuzzy logic compensator is developed to compensate the gravitational effects according to different robot configurations and task situations. Experimental results prove the validity of the proposed methods. [ABSTRACT FROM AUTHOR]

Presents information on a study which used a backpropagation training algorithm with a scheme for the online tuning of a neural network tracking control system. Importance of robust stability and convergence to neural network systems; Methodology; Results; Conclusions.

We describe the design and 3D sensing performance of an omnidirectional stereo (omnistereo) vision system applied to Micro Aerial Vehicles (MAVs). The proposed omnistereo sensor employs a monocular camera that is co-axially aligned with a pair of hyperboloidal mirrors (a vertically-folded catadioptric configuration). We show that this arrangement provides a compact solution for omnidirectional 3D perception while mounted on top of propeller-based MAVs (not capable of large payloads). The theoretical single viewpoint (SVP) constraint helps us derive analytical solutions for the sensor's projective geometry and generate SVP-compliant panoramic images to compute 3D information from stereo correspondences (in a truly synchronous fashion). We perform an extensive analysis on various system characteristics such as its size, catadioptric spatial resolution, field-of-view. In addition, we pose a probabilistic model for the uncertainty estimation of 3D information from triangulation of back-projected rays. We validate the projection error of the design using both synthetic and real-life images against ground-truth data. Qualitatively, we show 3D point clouds (dense and sparse) resulting out of a single image captured from a real-life experiment. We expect the reproducibility of our sensor as its model parameters can be optimized to satisfy other catadioptric-based omnistereo vision under different circumstances.

Orientation estimation using low cost sensors is an important task for Micro Aerial Vehicles (MAVs) in order to obtain a good feedback for the attitude controller. The challenges come from the low accuracy and noisy data of the MicroElectroMechanical System (MEMS) technology, which is the basis of modern, miniaturized inertial sensors. In this article, we describe a novel approach to obtain an estimation of the orientation in quaternion form from the observations of gravity and magnetic field. Our approach provides a quaternion estimation as the algebraic solution of a system from inertial/magnetic observations. We separate the problems of finding the "tilt" quaternion and the heading quaternion in two sub-parts of our system. This procedure is the key for avoiding the impact of the magnetic disturbances on the roll and pitch components of the orientation when the sensor is surrounded by unwanted magnetic flux. We demonstrate the validity of our method first analytically and then empirically using simulated data. We propose a novel complementary filter for MAVs that fuses together gyroscope data with accelerometer and magnetic field readings. The correction part of the filter is based on the method described above and works for both IMU (Inertial Measurement Unit) and MARG (Magnetic, Angular Rate, and Gravity) sensors. We evaluate the effectiveness of the filter and show that it significantly outperforms other common methods, using publicly available datasets with ground-truth data recorded during a real flight experiment of a micro quadrotor helicopter.