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

Knjiga daje pregled upotrebe programskog paketa Matlab u rjesavanju tehnickih problema. Opisan je nacin modeliranja tehnickih sustava te njihovo simuliranje primjenom programskog alata Simulink unutar Matlaba. Osim toga prikazan je nacin matematickog modeliranja tehnickih sustava te metode grafickog prikaza rezultata pomocu Matlaba.

Application of the Matlab software package for technical problems solving

A concept of a water supply plant control system is proposed that ensures output pressure stabilisation under conditions of variable water consumption and variable water resourses. The control system consists of inner control loops which control the flow of each pump station and of superimposed control loop that controls output pressure of the water supply plant. The pressure controller calculates the cumulative reference value of the water flow for all controlled pumps and from this value an algorithm for the flow distribution calculates the reference flow for each inner loop, taking into account the level of water accumulation in each water well. Control system performances were investigated by simulations on the water supply plant Mala Mlaka near Zagreb. It has been shown that the system exhibits more robust behaviour if a fuzzy logic controller of output pressure is used instead of a conventional PI controller.

Fuzzy Control of a Water Supply System

In most applications, a mobile robot must be able to determine its position and orientation in the environment using only own sensors. Orientation estimation accuracy greatly influences the position estimation accuracy and is therefore crucial for a reliable mobile robot pose tracking. Our approach to orientation estimation is based on angle histograms matching. Angle histograms are obtained indirectly via Hough transformation combined with a non-iterative algorithm for determination of the end points and length of straight-line parts contained in obtained histograms. Sensors used for local occupancy grid generation are sonars. Test results with mobile robot Pioneer 2DX simulator show the capacity of this method.

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Sonar-based self-orientation of indoor mobile robots

Reliable operation in inclement weather is essential to the deployment of safe autonomous vehicles (AVs). Robustness and reliability can be achieved by fusing data from the standard AV sensor suite (i.e., lidars, cameras) with weather robust sensors, such as millimetre-wavelength radar. Critically, accurate sensor data fusion requires knowledge of the rigid-body transform between sensor pairs, which can be determined through the process of extrinsic calibration. A number of extrinsic calibration algorithms have been designed for 2D (planar) radar sensors - however, recently-developed, low-cost 3D millimetre-wavelength radars are set to displace their 2D counterparts in many applications. In this paper, we present a continuous-time 3D radar-to-camera extrinsic calibration algorithm that utilizes radar velocity measurements and, unlike the majority of existing techniques, does not require specialized radar retroreflectors to be present in the environment. We derive the observability properties of our formulation and demonstrate the efficacy of our algorithm through synthetic and real-world experiments.

Ivan Petrović

Papers

Application of the Matlab software package for technical problems solving

Fuzzy Control of a Water Supply System

Sonar-based self-orientation of indoor mobile robots

A Continuous-Time Approach for 3D Radar-to-Camera Extrinsic Calibration

Editorial: Special issue ECMR 2009