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

Comparison of several recursive identification methods

Tracking of multiple moving objects on the unit sphere using a multiple-camera system on a mobile robot

This paper deals with a deadlock prevention method in manufacturing systems based on Petri nets. The manufacturing system is a discrete event system which has the potential deadlock states. The paper shows how it is possible to model manufacturing system by Petri net and how it is possible to synthesize the Petri net supervisor that will prevent deadlock occurrence. Deadlock prevention algorithm proposed in this paper uses reachability tree to detect deadlock state and iterative siphon control method to synthesize the supervisor. Such deadlock prevention supervisor is maximally permissive and consists of minimal number of control places. The algorithm is usable for reversible or partially reversible P-T Petri net, but it can also be applied to ordinary nets. Finally, illustrative example of deadlock prevention of a flexible assembly cell with three robots and conveyors is presented.

Deadlock Prevention in Manufacturing Systems Based on Petri Nets

This contribution presents a modified autotuning algorithm of the PID controller. The motivation for the modification of the basic autotuning algorithm is to enlarge the class of processes to which it can be applied. The basic autotuning algorithm introduced by Astrom and Hagglund is extended by the preliminary identification procedure and through the usage of the dead time compensating controller. These modifications are detailed through the description of the algorithms' functioning. The proposed algorithm has been implemented in the programmable logic controller (PLC) Siemens SIMATIC S7-300. The experimental results confirm the good robustness properties of the proposed algorithm, which were demonstrated in a simulation study.

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Modification and application of autotuning PID controller

In this paper we consider an aerial vehicle transporting a suspended payload and propose an Extended Kalman filter for payload state estimation. The filter is based on derived system dynamics and relies solely on onboard IMU measurements. Effectiveness of the method is verified in numerical simulations and experimentally.

Ivan Petrović

Papers

Comparison of several recursive identification methods

Tracking of multiple moving objects on the unit sphere using a multiple-camera system on a mobile robot

Deadlock Prevention in Manufacturing Systems Based on Petri Nets

Modification and application of autotuning PID controller

Extended Kalman filter for payload state estimation utilizing aircraft inertial sensing