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.

Experimental investigations of a direct adaptive neurofuzzy controller

Abstract: The control of dynamical systems with inherent non-linear characteristics has motivated research in non-linear control theory. Two main approaches to dealing with uncertainties in control systems design are adaptive and robust control. In this paper, discrete direct adaptive control of unknown non-linear SISO systems is considered. The controller is implemented using a fuzzy neural network. The control concept is tested on a laboratory pilot plant and compared to a standard discrete PID Takahashi controller

Endemic goiter in Croatia

Abstract: In the early nineties, 40 years after the introduction of iodine prophylaxis in Croatia (10 mg KI/kg salt), a nationwide study was initiated with the aim to determine the real prevalence of goiter in the country. A total of 2436 schoolchildren of both sexes, aged 7-15 years, were included in the study. Investigations were designed in a way to cover most of geographical regions in Croatia and subjects were randomly selected. The prevalence of goiter in schoolchildren was assessed by palpation and in part by ultrasonography of the neck. At the same time urinary iodine excretion was measured and potassium iodide content in salt samples was determined. The results revealed the persistence of mild endemic goiter in the inland parts of Croatia with the prevalence of 6-29% in the age group 7-11 years and 10-43% among the age group 12-15 years. The overall goiter prevalence in schoolchildren in Croatia fluctuates from 8% to 35%. The urinary iodine excretion in Zagreb varied from 1.6 to 24.4 micrograms/dL with a median of 9.0, while in 14% it was below 5 micrograms/dL. The urinary iodine excretion in Samobor varied between 0.5 and 19 micrograms/dL with a median of 7.4 micrograms/dL, while in 30% it was below 5 micrograms/dL. Examination of salt from all three salt plants in Croatia showed iodine contents between 0.0 and 12.73 mg/kg with the average value of 5.39 mg/kg. Such prevalence, most probably due to less than optimum iodine intake, is unlikely to change until potassium iodide content of salt is increased from its present level of 10 mg of KI per kg of salt, with concomitant stricter observance of legal regulations.

Short-Term Map Based Detection and Tracking of Moving Objects with 3D Laser on a Vehicle

Abstract: Detection and tracking of moving objects is an essential problem in situational awareness context and hence crucial for many robotic applications. Here we propose a method for the detection of moving objects with a 3D laser range sensor and a variation of the method for tracking multiple detected objects. The detection procedure starts with the ground extraction using random sample consensus approach for model parameter estimation. The resulting point cloud is then downsampled using voxel grid approach and filtered using a radius outlier rejection method. Within the approach, we have utilized a procedure for building short-term maps of the environment by using the octree data structure. This data structure enables an efficient comparison of the current scan and the short-term local map, thus detecting dynamic parts of scene. The ego-motion of the mobile platform is compensated using the available odometry information, which is rather imperfect, and hence is refined using the iterative closest point registration technique. Furthermore, due to sensor characteristics, the iterative closest point is carried out in 2D between the short-term map and the current, where the non-ground filtered scans are projected onto 2D. The tracking task is based on the joint probabilistic data association filter and Kalman filtering with variable process and measurement noise which take into account velocity and position of the tracked objects. Since this data association approach assumes a constant and known number of objects, we have utilized a specific entropy based track management. The experiments performed using Velodyne HDL-32E laser sensor mounted on top of a mobile platform demonstrate the suitability and efficiency of the proposed method.

Speculative trading strategy of buying crude oil for refinery on spot market

Abstract: This paper examines buying of the crude oil for a refinery on spot market based on a speculative trading strategy. The goal is to assure convenience yield and speculative gain by reducing the cost of crude oil. An Artificial Neural Network (ANN) was designed as a part of Decision Support System (DSS). Because of difficulties in forecasting the crude oil spot price, an innovative approach was applied in modelling the ANN. ANN output variables are not future spot prices, but decisions that represent small, mid or large quantity of crude oil to be bought. The ANN was trained on decisions during the five years time period (1995-2000) and was validated during the three years time period (2001-2003). Input variables to the ANN were heuristically derived from the historical spot prices of crude oil. About 3 % speculative gain has been obtained. Designed methodology presented in this work could be implemented on any application where the energy or raw material is being bought on spot market in order to be stored for consuming within a short time period of up to 3 months.

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%.