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.

Identification of Wind Turbine Model for Controller Design

Abstract: Wind power increases rapidly with increase of wind speed. In order to keep wind turbin running even at strong winds wind power capture has to be constrained. Efficient way for constraining wind power capture is the use of pitchable blades. For pitch controller design a suitable wind turbine model is needed. Finding mathematical model that would be a good representation of wind turbine system and at the same time suitable for controller design is very difficult task since wind turbine system is strongly nonlinear. Description of wind turbine system using many linear models identified at particular operating points is explored in this paper. Effects occurring when switching between models are examined and their repercussions on controller design are discussed. Use of fuzzy logic is proposed as possible solution for avoiding negative effects caused by switching between models.

A Control Method for Stable and Smooth Path Following of Mobile Robots

Abstract: This paper addresses the problem of mobile robot navigation in indoor cluttered environments. A new algorithm for both longitudinal and lateral real-time control of wheel-based mobile robots has been proposed. Its main characteristic is smooth and stable following of the on-line replanned path. Our control method is actually extension of the virtual vehicle method proposed by M. Egerstedt et al., which is based on the introduction of a look-ahead point on the path that serves as reference point for the control algorithm. While the original virtual vehicle method uses only error feedback for reference point movement along the path, our method uses also a feedforward component based on path curve characteristics between the robot and the reference point. In this way stable robot movement is achieved also in the presence of obstacles that are critical with respect to the path following error. Experimental verification is provided for a differential drive robot within an on-line global path planning framework.

Machine vision based control of the ball and beam

Abstract: The use of vision systems in motion control applications puts hard real-time constrains on image processing. However, constantly increasing performances and decreasing prices of vision hardware make vision measuring systems concurrent to other measuring systems in these applications, where vision systems can be used to precisely measure number of variables such as length, angle, position, orientation, etc. The main advantage of a vision measuring system in these applications is its noncontact measurement principle, which is important in cases when it is difficult to implement contact measurements. Apart from real-time constraints, the biggest problem that limits the applications of a machine vision system is its robustness to the noise present in the image as well as to the scene disturbances caused by background and foreground objects and to the nonideal illumination conditions. A simple, computationally efficient and robust machine vision measuring system is described, which is suitable for real-time angle measurement. Its behavior is experimentally tested on a ball and beam benchmark process, where high quality measurement of the beam angle is achieved even in nonideal lighting condition and also when no white background is used. The implemented vision system is used for feedback control of the ball and beam.

Human motion estimation on Lie groups using IMU measurements

Abstract: This paper proposes a new algorithm for human motion estimation using inertial measurement unit (IMU) measurements. We model the joints by matrix Lie groups, namely the special orthogonal groups SO(2) and SO(3), representing rotations in 2D and 3D space, respectively. The state space is defined by the Cartesian product of the rotation groups and their velocities and accelerations, given a kinematic model of the articulated body. In order to estimate the state, we propose the Lie Group Extended Kalman Filter (LG-EKF), thus explicitly accounting for the non-Euclidean geometry of the state space, and we derive the LG-EKF recursion for articulated motion estimation based on IMU measurements. The performance of the proposed algorithm is compared to the EKF based on Euler angle parametrization in both simulation and real-world experiments. The results show that for motion near gimbal lock regions, which is common for shoulder movement, the proposed filter is a significant improvement over the Euler angles EKF.

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%.