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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]

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]

Abstract: In this paper, the robust -stability problem of matrix second-order systems with perturbations in the form of a one-parameter family of matrices is investigated. All the system matrices, including the second-order differential coefficient matrices, are assumed to have such perturbations. Based on the Kronecker product, a necessary and sufficient condition for the robust -stability problem is presented by transforming such a problem into checking the nonsingularity of a class of uncertain matrices. Then, a closed form for the maximal perturbation bounds for preserving the -stability is given. Finally, illustrative examples are given to show that our results are effective and less conservative than the results obtained by other researchers. [Copyright &y& Elsevier]

Abstract: This paper introduces a methodology to cluster noisy range images into planar regions acquired in indoor environments. The noisy range images are segmented based on a Gaussian similarity metric, which compares the geometric attributes that satisfy the coplanarity conditions. The algorithm is designed to cluster coplanar noisy range data by means of patch-based sampling from range images. We discuss the advantages of patch-based clustering over point-based clustering of noisy range images that eliminates computational redundancy to accelerate the clustering process while keeping the segmentation error to a minimum. The final output of the algorithm is a set of polygons, where each polygon is defined by a set of boundary points that replaces large number of coplanar data points in a given planar region. The 3D range image is acquired by a rotating 2D range scanner and stored in a 2D array. Each element in the array is explicitly stored as the range distance; the indices of the array implicitly retain neighborhood and angular information. The array is grouped into mutually-exclusive patches of size and the Hessian plane parameters are computed for each patch. We propose a graph-search algorithm that compares the plane parameters of neighboring patches by searching breadth-wise and clusters the coplanar patches into respective planes. We compare the proposed Patch-based Plane Clustering (PPC) algorithm with the point-based Region Growing (RG) algorithm and the RANSAC plane segmentation method to analyze the performance of each of the algorithms in terms of speed and accuracy. Experimental results indicate that the PPC algorithm shows a significant improvement in computational speed when compared with the state-of-the-art segmentation algorithms while maintaining a high accuracy in segmenting noisy range images. [Copyright &y& Elsevier]

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]

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.

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]

More and more unexpected incidents come out currently. Static sensor networks can not satisfy the increasing need for emergency response due to lack of adaptivity and limited coverage, thus mobile sensor networks become the research focus for overcoming above drawbacks. With excellent performances including autonomy, flexibility, stability and low-cost, micro-unmanned air vehicles (MUAVs) play a greater pole in mobile sensor networks. Ranging is one of the most critical sensing functions for mobile sensor networks. In this paper, a flying laser ranging sensor is proposed, essentially, an airborne pulsed time-of-flight (TOF) laser rangefinder (LRF) is realized. Because of the influence from walk error, time jitter, temperature drift and other factors, there is still room to improve the measurement precision of pulsed TOF LRFs. So the more important contribution of the paper is to present a novel interval time amendment approach for higher ranging precision. Based on leading-edge time discrimination, this method first employs wavelet multi-resolution analysis to compute compensation time values, which are computed mainly from local modulus maxima (LMMs) position differences. With approximations signals similarity and measurement relative error (MRE), a fusion evaluation is built to select the best wavelet parameters, and optimal corrected measurement model is obtained. Results show that MRE of the improved model is reduced by 73.41%. Therefore, the performance of flying laser ranging sensor is improved significantly with multi-resolution model correction. [ABSTRACT FROM AUTHOR]

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]

We design a novel 'folded' spherical catadioptric rig (formed by two coaxially-aligned spherical mirrors of distinct radii and a single perspective camera) to recover near-spherical range panoramas (about 360° × 153°) from the fusion of depth given by optical flow and stereoscopy. We observe that for rigid motion that is parallel to a plane, optical flow and stereo generate nearly complementary distributions of depth resolution. While optical flow provides strong depth cues in the periphery and near the poles of the view-sphere, stereo generates reliable depth in a narrow band about the equator instead. We exploit this dual-modality principle by modeling (separately) the depth resolution of optical flow and stereo in order to fuse them later on a probabilistic spherical panorama. We achieve a desired vertical field-of-view and optical resolution by deriving a linearized model of the rig in terms of three parameters (radii of the two mirrors plus axial distance between the mirrors' centers). We analyze the error due to the violation of the single viewpoint constraint and formulate additional constraints on the design to minimize this error. We evaluate our proposed method via a synthetic model and with real-world prototypes by computing dense spherical panoramas of depth from cluttered indoor environments after fusing the two modalities (stereo and optical flow). [ABSTRACT FROM AUTHOR]

This paper presents the advantages of a single-camera stereo omnidirectional system (SOS) in estimating egomotion in real-world environments. The challenge of applying omnidirectional stereo vision via a single camera is what separates our work from others. In practice, dynamic environments, deficient illumination, and poor textured surfaces result in the lack of features to track in the observable scene. As a consequence, this negatively affects the pose estimation of visual odometry systems, regardless of their field of view. We compare the tracking accuracy and stability of the single-camera SOS versus an RGB-D device under various real circumstances. Our quantitative evaluation is performed with respect to 3D ground-truth data obtained from a motion capture system. The datasets and experimental results we provide are unique due to the nature of our catadioptric omnistereo rig, and the situations in which we captured these motion sequences. We have implemented a tracking system with simple rules applicable to both synthetic and real scenes. Our implementation does not make any motion model assumptions, and it maintains a fixed configuration among the compared sensors. Our experimental outcomes confer the robustness in 3D metric visual odometry estimation that the single-camera SOS can achieve under normal and special conditions in which other perspective narrow view systems such as RGB-D cameras would fail. [ABSTRACT FROM AUTHOR]

Semiconductor pulsed time-of-flight (TOF) laser rangefinders (LRFs) have been the main distance measurement components equipped by many radar systems of Micro Unmanned Air Vehicles (MUAVs). A novel approach is proposed to improve the measurement accuracy of pulsed TOF LRFs. This approach adopted traditional single-threshold leading edge detection firstly, then employed continuous wavelet transform (CWT)-based local modulus maxima (LMMs) to detect singularities of emitting pulses and receiving echoes of LRFs, and obtained the compensation time with detected singularities to revise the measurement model. A series of experiments were done in a proposed pulsed TOF LRF prototype, and the measurement model is built by linear fitting. With the compensation values of various wavelets, the optimum revised measurement model is obtained. It is demonstrated that the single-shot precision of the pulsed TOF LRF achieves 195 ps (∼29 mm) with SNR = 7, and the absolute error is reduced from 9.25 to 6.32 mm. Therefore, the proposed approach can better compensate the walk error of the existing pulsed TOF LRFs and improve the performance of TOF LRFs. [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]

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]