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.

Sonar-based Pose Tracking of Indoor Mobile Robots

Abstract: In order to perform useful tasks the mobile robot's current pose must be accurately known. Problem of finding and tracking the mobile robot's pose is called localization, and can be global or local. In this paper we address the problem of mobile robot's local localization or pose tracking with prerequisites of known starting pose, robot kinematics and world model. Pose tracking is mostly based on odometry, which has the problem of accumulating errors in an unbounded fashion. To overcome this problem sensor fusion is commonly used. This paper describes a simple odometry calibration method and compares two fusion methods of calibrated odometry data and sonars' range data based on Kalman Filter theory. One fusion method is based on standard Extended Kalman Filter and another one, proposed in this paper, on the Unscented Kalman Filter. Occupancy grid map is used as the world model, which is beneficial because only sonars' range measurement uncertainty has to be considered. If a feature based map is used as the world model then an additional uncertainty regardingthe feature/range reading assignment must be also considered. Experimental results obtained with the Pioneer 2DX mobile robot (manufacturer ActivMedia Robotics) show that better accuracy of pose estimation and smoother robot motion can be obtained with Unscented Kalman Filter.

Application of extended Kalman filter for road condition estimation

Abstract: High quality estimation of tire-road friction forces has important role in many automotive control systems like anti-lock brake systems (ABS), traction control systems etc. For this purpose an extended Kalman filter augmented with integral term has been employed. A procedure for selecting appropriate integral gain has been proposed. The proposed estimator has been compared to the well-known passivity based state estimator.

Teleoperation Of Collaborative Mobile Robots With Force Feedback Over Internet

Abstract: A teleoperation system has been developed that enables two human operators to safely control two collaborative mobile robots in unknown and dynamic environments from any two PCs connected to the Internet by installing developed client program on them and by using simple force feedback joysticks. On the graphical user interfaces, the operators receive images forwarded by the cameras mounted on the robots, and on the the joysticks they feel forces forwarded by developed obstacle prevention algorithm based on the dynamic window approach. The amount and direction of the forces they feel on their hands depend on the distance and direction to the robot’ s closest obstacle, which can also be the collaborating robot. To overcome the instability caused by the unknown and varying time delay an event-based teleoperation method is employed to synchronize actions of each robot with commands from its operator. Through experimental investigation it is confirmed that developed teleoperation system enables the operators to successfully accomplish collaborative tasks in complex environments.

Current status of endemic goiter in Croatia: The results of a nationwide study (1995)

Abstract: In the beginning of the nineties, 40 years after introduction of iodine prophylaxis in Croatia, on a basis of a frequent reports coming from general practitioners about the presence of a rather high prevalence of goiter among schoolchildren, a nationwide study was initiated with the aim to determine the real prevalence of goiter in the country. A total of 2856 schoolchildren of both sexes, aged 7-15 years, were included into 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 iodine content in salt samples was determined. The results have revealed the persistence of mild endemic goiter in inland parts of Croatia with the prevalence of 6-29% in the age group 7-11 years and those of 10-43% among the age group 12-15 years. The overall goiter prevalence in schoolchildren in Croatia fluctuates from 8% to 35%. Such prevalence, most probably due to less than optimum iodine intake, is unlikely to change until iodine content of the salt is increased from its present level of 10 mg of Kl per kg of salt.

On wrapping the Kalman filter and estimating with the SO(2) group

Abstract: This paper analyzes directional tracking in 2D with the extended Kalman filter on Lie groups (LG-EKF). The study stems from the problem of tracking objects moving in 2D Euclidean space, with the observer measuring direction only, thus rendering the measurement space and object position on the circle—a non-Euclidean geometry. The problem is further inconvenienced if we need to include higher-order dynamics in the state space, like angular velocity which is a Euclidean variables. The LG-EKF offers a solution to this issue by modeling the state space as a Lie group or combination thereof, e.g., SO(2) or its combinations with ℝn. In the present paper, we first derive the LG-EKF on SO(2) and subsequently show that this derivation, based on the mathematically grounded framework of filtering on Lie groups, yields the same result as heuristically wrapping the angular variable within the EKF framework. This result applies only to the SO(2) and SO(2) × ℝn LG-EKFs and is not intended to be extended to other Lie groups or combinations thereof. In the end, we showcase the SO(2) × ℝ2 LG-EKF, as an example of a constant angular acceleration model, on the problem of speaker tracking with a microphone array for which real-world experiments are conducted and accuracy is evaluated with ground truth data obtained by a motion capture system.

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%.