Ivan Petrović

Bio: Ivan Petrović is an academic researcher from University of Zagreb. The author has contributed to research in topics: Mobile robot & Motion planning. The author has an hindex of 28, co-authored 248 publications receiving 3002 citations. Previous affiliations of Ivan Petrović include Czech Technical University in Prague & University of Toronto.

A Petri Net Approach of Deadlock Prevention in Marine Traffic System

Abstract: This paper deals with the traffic control of vessels moving through the marine system of canals and basins. Dangerous vessel deadlock situations may occur in case of vessels’ irregular moving through the system. To avoid this, the vessel traffic is supervised and controlled by traffic lights. A supervisor is responsible for vessels’ stopping only in the case of dangerous situation and until this situation elapses. This paper shows a formal method of calculating a maximal permissible deadlock prevention supervisor by use of Petri net and iterative siphon control method. The functionality of calculated deadlock prevention supervisor is verified using a computer simulation of vessels’ movements.

An Efficient Newton-type learning Algorithm for MLP Neural Networks

Abstract:  Multilayer perceptrons (MLP) are the most often used neural networks in function approximation applications. They learn by modifying the strength of interconnections between neurons, according to some specified rule called learning algorithm. Many different learning algorithms have been reported in the literature. The majority of them are based on gradient numerical optimization methods such as the steepest descent, conjugate gradient, quasi-Newton and Newton methods. In this paper we have proposed a new Newton-type learning algorithm which is a modification of the popular Levenberg-Marquardt learning algorithm. The algorithm has been compared with the original Levenberg-Marquardt algorithm regarding the convergence speed and the computation complexity on four nonlinear test functions. Also the effects of the data sets size and extremely high accuracy requirements on the efficiency of the algorithms have been analyzed. To provide the algorithms comparison as objective as possible, both algorithms were implemented on the same manner and the network weights were initialized equally for both of them. The proposed algorithm exhibited better performances in all test cases.

Robot arm teleoperation via RGBD sensor palm tracking

Abstract: In this paper we present a simple and intuitive approach to teleoperating a 3 or higher degrees-of-freedom (DOF) robotic arm in Cartesian space. Using an RGBD camera, we retrieve the position of the user's palm. This position is then translated into the desired robotic arm position, which is then used as an input to a control loop. The entire system is implemented in the Robotic Operating System, enabling simple functionality transfer to any compatible robotic arm. The system was tested on the Kinova Jaco 6DOF robotic arm with the aim of using it for object manipulation. We use the inverse kinematics for calculating the joint rotation velocities required for following the Cartesian path of the human hand. The resulting joint velocities are then sent to the robotic arm control interface which then passes commands to the pertaining API. Results corroborate the validity of the proposed approach for robotic arm teleoperation, opening the possibility for many potential applications.

Application of the RBF neural networks for tire-road friction force estimation

Abstract: This paper deals with the problem of the robust tire-road friction force estimation. Good information about friction force generated in contact between wheel and road has significant importance in many active safety systems in modern vehicles (anti-lock brake systems, traction control, vehicle dynamic systems, etc). Since state estimators are usually based on exact model of process, they are therefore limited by the model accuracy. A new estimation scheme based on RBF neural networks is proposed in this paper. The neural network is added to the estimator to compensate the effects of the friction model uncertainties to the estimation quality. An adaptation law for the neural network parameters is derived using Lyapunov stability analysis. The proposed state estimator provides accurate estimation of the tire-road friction force when fiction characteristic is only approximately known or even completely unknown. Quality of the estimation is examined through simulation using one wheel friction model. Simulation results suggest very fast compensation of the changes of the model parameters (< 150 ms) even when they vary in a wide range (changes of 100% and more). Possible drawback of proposed estimation scheme is the fact that neural network does not give the information what particular parameter has changed.

Moving Objects Tracking on the Unit Sphere Using a Multiple-Camera System on a Mobile Robot

Abstract: Detection and tracking of moving objects with camera systems mounted on a mobile robot presents a formidable problem since the ego-motion of the robot and the moving objects jointly form a challengingly discernible motion in the image. In this paper, we are concerned with multiple-camera systems, namely the Ladybug\(^{\textregistered }2\) camera, whose perspective images were used to detect motion and subsequently perform the tracking of multiple objects on the sphere. This enabled us to account for the continuity of the scene which is achieved by the sensor in an image stitching process on the sphere. The objects are tracked on the sphere with a Bayesian filter based on the von Mises–Fisher distribution and the data association is achieved by the global nearest neighbor method, for which the distance matrix is constructed by deriving the Renyi \(\alpha \)-divergence for the von Mises–Fisher distribution. The prospects of the method are tested on a synthetic and real-world data experiments.

I and i

Abstract: 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 …

Coverage control for mobile sensing networks

Abstract: 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.

Adaptive Control

Abstract: 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.

A Review of Motion Planning Techniques for Automated Vehicles

Abstract: 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.

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

Abstract: 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%.