There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

Intelligent vehicles have increased their capabilities for highly and, even fully, automated driving under controlled environments. Scene information is received using onboard sensors and communication network systems, i.e., infrastructure and other vehicles. Considering the available information, different motion planning and control techniques have been implemented to autonomously driving on complex environments. The main goal is focused on executing strategies to improve safety, comfort, and energy optimization. However, research challenges such as navigation in urban dynamic environments with obstacle avoidance capabilities, i.e., vulnerable road users (VRU) and vehicles, and cooperative maneuvers among automated and semi-automated vehicles still need further efforts for a real environment implementation. This paper presents a review of motion planning techniques implemented in the intelligent vehicles literature. A description of the technique used by research teams, their contributions in motion planning, and a comparison among these techniques is also presented. Relevant works in the overtaking and obstacle avoidance maneuvers are presented, allowing the understanding of the gaps and challenges to be addressed in the next years. Finally, an overview of future research direction and applications is given.

A Review of Motion Planning Techniques for Automated Vehicles

Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.

SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights

An approach for registration of sparse feature sets detected in stereo images taken from two different views is proposed. The approach is based on the RANSAC paradigm which ensures robustness to outliers. The strategy proposed in this work is to apply a multi-step procedure in the hypothesis generation stage of RANSAC using approximate information about the relative camera pose between the two views obtained e.g. by odometry. The data is taken from the input data set one by one and used to pose a geometric constraint for selection of the next data. This strategy reduces the number of false hypothesis in the RANSAC procedure. The presented technique is evaluated using both synthetic data and real data obtained by a stereo camera system mounted on a mobile robot.

Stereo Image Registration Based on RANSAC with Selective Hypothesis Generation

This paper describes the use of Kalman filtering in distance keeping for underwater vehicles. The vision-based distance keeping module has been mounted on a micro-ROV equipped with a camera. Distance from a plane-like obstacle is determined on the basis of the laser dot projections within the frame. Since these measurements are not reliable a Kalman filter is designed - unknown dynamic model parameters are determined using the self-oscillation experiments which prove to be simple and time preserving. Finally, an I-PD controller is designed based on the Kalman filter estimates. The main task of the controller is to keep the vehicle perpendicular to the surface at a desired distance.

/pdf/distance-keeping-for-underwater-vehicles-tuning-kalman-2tlvoilhns.pdf

Distance keeping for underwater vehicles - tuning Kalman filters using self-oscillations

This paper investigates the feasibility of integrating legacy software processes and tools into the paradigm of model-based development of industrial real-time embedded systems. Research has been conducted on the example of using legacy assembly code for automatic code generation scheme inside MATLAB/Simulink environment. A sample Simulink model has been presented, code has been generated from it and its correctness has been validated by back-to-back comparison with the simulation results.

/pdf/introducing-model-based-techniques-into-development-of-real-luh0vn022d.pdf

Introducing Model-Based Techniques into Development of Real-Time Embedded Applications

Efficient Reconfiguration for Lattice-Based Modular Robots

Modern logistic solutions for large warehouses consist of a fleet of robots that transfer goods, move racks, and perform other physically difficult and repetitive tasks. The shopfloor is usually enclosed with a safety fence and if a human needs to enter the warehouse all the robots are stopped, as opposed to only the ones in the most immediate vicinity of the human, thus significantly limiting the warehouse efficiency. To tackle this challenge, an integrated safety system is needed with human localization as one of its essential components. In this paper, we propose a novel human localization method for robotized warehouses that is based on a suite of wearable visual sensors installed on a vest worn by humans. The proposed method does not require any modifications of the warehouse environment and relies on the already existing infrastructure. Specifically, we estimate the human location by fusing stereo visual-inertial odometry data and distances to the known absolute poses of the detected ground-markers which robots use for their localization. Fusion is performed by building a pose graph, where we treat estimated human poses relative to markers as graph nodes and odometry estimates as graph edges. We conducted extensive laboratory and warehouse facility experiments, where we tested the reliability and accuracy of the proposed method and compared its performance to a state-of-the-art visual SLAM solution, namely ORB-SLAM2. The results indicate that our method can track absolute position in real-time and has competitive accuracy with respect to ORB-SLAM2, while ensuring higher localization reliability when faced with structural changes in the environment. Furthermore, we provide publicly the experimental datasets to the research community.

Ivan Petrović

Papers

Stereo Image Registration Based on RANSAC with Selective Hypothesis Generation

Distance keeping for underwater vehicles - tuning Kalman filters using self-oscillations

Introducing Model-Based Techniques into Development of Real-Time Embedded Applications

Efficient Reconfiguration for Lattice-Based Modular Robots

Human localization in robotized warehouses based on stereo odometry and ground-marker fusion