scispace - formally typeset
Search or ask a question
Author

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.


Papers
More filters
Journal ArticleDOI
TL;DR: The overall approach to map indoor environments with thermal data in 3D enabling one to detect all sources of wasted energy and to modify buildings to reach these savings is presented.

40 citations

Journal ArticleDOI
TL;DR: This work introduces a novel path planning and replanning algorithm - the two-way D^* (TWD^*) algorithm - based on a two-dimensional occupancy grid map of the environment that can find optimal paths in weighted occupancy grid maps.

40 citations

Proceedings ArticleDOI
26 Dec 2007
TL;DR: Estimation of the mobile robot pose as a stochastic variable is done by correspondences of image lines, extracted using Random Window Randomized Hough Transform line detection algorithm, and model lines, predicted using odometry readings and 3D environment model.
Abstract: In this paper, we consider the problem of mobile robot pose estimation using only visual information from a single camera and odometry readings. A focus is on building complex environmental models, fast online rendering and real-time complex and noisy image segmentation. The 3D model of the robot's environment is built using a professional freeware computer graphics tool named Blender and pre-stored in the memory of the robot's on-board computer. Estimation of the mobile robot pose as a stochastic variable is done by correspondences of image lines, extracted using Random Window Randomized Hough Transform line detection algorithm, and model lines, predicted using odometry readings and 3D environment model. The camera model and ray tracing algorithm are also described. Developed algorithms are experimentally tested using a Pioneer 2DX mobile robot.

39 citations

Journal ArticleDOI
TL;DR: A new online path-smoothing algorithm for smoothing a path that consists of straight line segments and is primarily intended for differential drive mobile robots and allows non-zero initial path curvature, which is important for replanning.

37 citations

Journal ArticleDOI
TL;DR: A receding horizon control (RHC) algorithm for convergent navigation of a differential drive mobile robot is proposed, which produces faster motion to the goal with significantly lower computational costs and it does not need any controller tuning to cope with diverse obstacle configurations.
Abstract: A receding horizon control (RHC) algorithm for convergent navigation of a differential drive mobile robot is proposed. Its objective function utilizes a local-minima-free navigation function to measure the cost-to-goal over the robot trajectory. The navigation function is derived from the path-search algorithm over a discretized 2-D search space. The proposed RHC navigation algorithm includes a systematic procedure for the generation of feasible control sequences. The optimal value of the objective function is employed as a Lyapunov function to prove a finite-time convergence of the discrete-time nonlinear closed-loop system to the goal state. The developed RHC navigation algorithm inherits fast replanning capability from the $D$ * search algorithm, which is experimentally verified in changing indoor environments. The performance of the developed RHC navigation algorithm is compared with the state-of-the-art sample-based motion planning algorithm based on lattice graphs, which is combined with a trajectory tracking controller. The RHC navigation algorithm produces faster motion to the goal with significantly lower computational costs and it does not need any controller tuning to cope with diverse obstacle configurations.

37 citations


Cited by
More filters
Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
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 …

33,785 citations

Posted Content
TL;DR: This paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies which are adaptive, distributed, asynchronous, and verifiably correct.
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.

2,198 citations

Journal Article
TL;DR: In this paper, two major figures in adaptive control provide a wealth of material for researchers, practitioners, and students 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.
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.

1,814 citations

Journal ArticleDOI
TL;DR: A review of motion planning techniques implemented in the intelligent vehicles literature, with a description of the technique used by research teams, their contributions in motion planning, and a comparison among these techniques is presented.
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.

1,162 citations

Journal Article
TL;DR: A new approach to visual navigation under changing conditions dubbed SeqSLAM, which 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.
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%.

686 citations