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The ballbot is a dynamically stable mobile robot designed to balance on a single ball, whose dynamic stability enables improved navigability in narrow, crowded, and dynamic environments. Through its single contact point with the ground, ballbot is omnidirectional and exceptionally agile, maneuverable, and organic in motion compared to other ground vehicles. For dealing with the challenging and imperative issues about ballbot, such as balancing, yaw, position control algorithms as well as the mathematical kinematic model, a novel model employing the Lagrange Equation is derived and control algorithm based on the similar principle as the second-order inverted pendulum is proposed, which is used for tackling the balancing, yaw, and position control. A cascade fuzzy proportional derivative (PD) controller and another controller with proportional integral (PI) control and PD feedback are designed for position and speed, respectively. Yaw control is presented, including head-hold mode and head-free mode. Some experiments are carried out to validate the effectiveness of the mathematical model and control algorithm.

This paper describes the development of a novel measurement system of calculating the three-dimensional (3D) coordinates of a target with image information taken from stereo fisheye cameras. The system has the characteristics of high precision, wide of view, long-distance control, dynamic angles compensation and high frame rate measurement. In order to improve the robustness and precision of the system, a detailed uncertainty analysis of the spatial positioning was made. Moreover, a novel method named Dynamic Angles Compensation Method (DACM) is proposed for first time to complete spatial position measurement in the dynamic environment by angles compensation. This method uses a tilt angle sensor that is installed in the birocular measurement system horizontally to calculate the pitch and roll angles of the measurement system, which is the foundation for calculating the conversion equation to compensate the measurement coordinates error caused by the dynamic environment. Using real data acquired by equipment, we performed experiments covering all the necessary stages to obtain a high-performance stereo fisheye vision system. The validity and accuracy of the proposed method are demonstrated with the statistical analyses of the experimental results.

Abstract Marine monitoring systems have the requirements of a large field of view, low power consumption, real-time viewing, and economical and automatic functionality. This paper establishes an omnidirectional vision system used in marine buoys that meets these requirements. We present a framework for image stabilization, which is achieved by omnidirectional sea-skyline detection in a marine environment. We propose an optimal edge estimation method to calculate the sea-skyline ellipsis according to the sea-skyline characteristics in panoramic images. We construct a compact panoramic image stabilization model based on the sea-skyline and propose a reconstruction method for the invalid regions using the key frame. The experimental results and analysis show that the proposed approach is capable of acquiring stable video in real-time marine monitoring tasks and that the target detection is sufficiently effective, efficient, and accurate for a real-time ship target detection application.

This paper denotes to obtain an accuracy blur kernel and a shape image. An efficient method that blind deconvolution for image deblurring based on edge enhancement and noise suppression is proposed. First, we exploited an edge detection method to extract the strong edge portion of blurred image. Then, the blurred image was divided into weak edge portion and strong edge portion. At this time, we apply a trilateral filter method to suppress the noise in the weak edge portion. Through mathematical operations for weak edge portion and strong edge portion, we can obtain the new blurred image which as the input of blur kernel estimation. At the phase of the kernel estimation, the problem can be solved via alternate between $x$ and $k$ updating. In addition, we utilize improved fast iterative shrinkage thresholding algorithm method to solve the optimization problem. Finally, non-blind deconvolution was employed at the phase of image recovery. A comprehensive evaluation shows that our approach achieves state-of-the-art results for uniformly blurred images.

Abstract Omnidirectional stereo vision systems have been widely used as primary vision sensors in intelligent robot 3D measurement tasks, which require stereo calibration and rectification. Current stereo calibration and rectification methods suffer from complex calculations or a lack of accuracy. This paper establishes a simple and effective equivalency between an omnidirectional stereo vision system and a perspective vision system by studying stereo calibration and rectification methods. First, we improved the stereo calibration method. By applying the essential matrix, the complicated calibration process of the original method is simplified. By using a manual extraction method to extract corner points, noise error is eliminated and high precision is ensured. Second, we propose a new rectification method. By using the proposed simple rectification model and calibration data, the baseline length and an accurate column-aligned image pair are easily obtained, which reduces the computation time. The proposed stereo calibration and rectification method can simply and effectively obtain two key parameters of the triangulation formula for 3D measurement tasks: baseline length and parallax. Using real data captured by equipment, we performed experiments covering all the necessary stages to obtain a high-performance omnidirectional stereo vision system. Statistical analyses of the experimental results demonstrate the effectiveness of the proposed method.

Warping is an effective visual homing method for robot local navigation. However, the performance of the warping method can be greatly influenced by the changes of the environment in a real scene, thus resulting in lower accuracy. In order to solve the above problem and to get higher homing precision, a novel robot visual homing algorithm is proposed by combining SIFT (scale-invariant feature transform) features with the warping method. The algorithm is novel in using SIFT features as landmarks instead of the pixels in the horizon region of the panoramic image. In addition, to further improve the matching accuracy of landmarks in the homing algorithm, a novel mismatching elimination algorithm, based on the distribution characteristics of landmarks in the catadioptric panoramic image, is proposed. Experiments on image databases and on a real scene confirm the effectiveness of the proposed method.

This paper introduces a feature optimization method for robot long-range feature-based visual homing in changing environments. To cope with the changing environmental appearance, the optimization procedure is introduced to distinguish the most relevant features for feature-based visual homing, including the spatial distribution, selection and updating. In the previous research on feature-based visual homing, less effort has been spent on the way to improve the feature distribution to get uniformly distributed features, which are closely related to homing performance. This paper presents a modified feature extraction algorithm to decrease the influence of anisotropic feature distribution. In addition, the feature selection and updating mechanisms, which have hardly drawn any attention in the domain of feature-based visual homing, are crucial in improving homing accuracy and in maintaining the representation of changing environments. To verify the feasibility of the proposal, several comprehensive evaluations are conducted. The results indicate that the feature optimization method can find optimal feature sets for feature-based visual homing, and adapt the appearance representation to the changing environments as well.

The collision avoidance and path planning technology is an important issue for submarine research field. It is essential and crucial for submarine to navigate underwater safely. There are lots of drawbacks for ensuring this important mission. For handling with the challenging and imperative issue, a novel and efficient collision risk model is proposed. A serial of motions of the submarine to be processed for fulfilling the collision avoidance based on this approach. The concepts of detection domain and fuzzy logic are adopted for modeling the degree of collision risk. The detection domain is used as the submarine safety scope when navigating underwater while the fuzzy logic is used as the mathematical implement for the analysis and synthesis of relations between obstacles or other submarines that are met in the navigation environment. The collision risk is regarded as one effective evaluation function that reflects which exact action should be done when the threshold of the risk degree is met. In order to verify the performance of proposed method, multisubmarines simulation experiments are concluded in different scenarios.

By combining the advantages of 360-degree field of view cameras and the high resolution of conventional cameras, the hybrid stereo vision system could be widely used in surveillance. As the relative position of the two cameras is not constant over time, its automatic rectification is highly desirable when adopting a hybrid stereo vision system for practical use. In this work, we provide a method for rectifying the dynamic hybrid stereo vision system automatically. A perspective projection model is proposed to reduce the computation complexity of the hybrid stereoscopic 3D reconstruction. The rectification transformation is calculated by solving a nonlinear constrained optimization problem for a given set of corresponding point pairs. The experimental results demonstrate the accuracy and effectiveness of the proposed method.