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 …

I and i

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.

Coverage control for mobile sensing networks

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.

Adaptive Control

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.

A Review of Motion Planning Techniques for Automated Vehicles

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%.

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

This paper considers the application of a neural network for the performance improvement of the sliding mode controller for a class of linear systems with unmatched uncertainties/disturbances. A neural network is employed for the online estimation of the uncertainties using the simple gradient descent learning algorithm. The combination of the sliding mode and backstepping-like recursive control design is used to achieve the desired tracking performance. The algorithm is verified through computer simulations.

Neural network based sliding mode controller for a class of linear systems with unmatched uncertainties

In this paper an optimal load-frequency controller for nonlinear power system is proposed. The mathematical model of power system consists of one area with several power plants, a few concentrated loads and transmission network, along with simplified models of neighbor areas. Firstly, a substitute linear model is derived, with its parameters being identified from the responses of the nonlinear model. That model is used for load-frequency control (LFC) algorithm synthesis, which is based on discrete-time sliding mode control. Due to non-minimum phase behavior of hydro power plants, full-state feedback sliding mode controller must be used. Therefore, an estimation method based on fast output sampling is proposed for estimating unmeasured system state and disturbance. Finally, the controller's parameters are optimized using genetic algorithm. Simulation results show that the proposed control algorithm with the proposed estimation technique can be used for LFC in nonlinear power system.

/pdf/applying-optimal-sliding-mode-based-load-frequency-control-1z694pu20t.pdf

Applying optimal sliding mode based load-frequency control in power systems with controllable hydro power plants

Platforms with holonomic drives are particularly interesting due to their maneuvering capabilities. Robots used for transportation tasks usually have a non-circular footprint. In this work, we present a navigation strategy for a holonomic mobile robot with anyshape footprint. Our technique introduces an efficient navigation method based on a strategy that makes use of discrete and continuous techniques. We introduce compact discrete intervals to represent the free space for computing fast-to-update plans. Based on these, we provide a continuous motion generation approach to generate smooth motions that are fast to compute. We evaluated our approach by running simulated experiments and by using a real holonomic L-shaped robot. Our experiments demonstrate that our technique can be carried out online and is able to smoothly drive the robot to its goal locations even in dynamic environments.

http://ais.informatik.uni-freiburg.de/publications/papers/dakulovic13iros.pdf

Efficient navigation for anyshape holonomic mobile robots in dynamic environments

Stereo matching is a powerful method of image segmentation for applications such as moving objects tracking. The result of a stereo matching algorithm is a disparity image which shows the difference of the object location in the images produced by the left and right camera. In this paper, we present a tracking algorithm which is based on the CAMSHIFT (Continuously Adaptive Mean Shift) algorithm. Our algorithm operates on the stereo images, and the disparity image. Extending the CAMSHIFT algorithm with the scene depth information from the disparity image we are able to increase the tracking quality with acceptable losses in the execution time. The algorithm is implemented and evaluated in a people tracking system, where the experimental results show that our algorithm outperforms the conventional CAMSHIFT algorithm in tracking accuracy.

An improved CamShift algorithm using stereo vision for object tracking

https://bib.irb.hr/datoteka/635933.ASOC_paper.pdf

Ivan Petrović

Papers

Neural network based sliding mode controller for a class of linear systems with unmatched uncertainties

Applying optimal sliding mode based load-frequency control in power systems with controllable hydro power plants

Efficient navigation for anyshape holonomic mobile robots in dynamic environments

An improved CamShift algorithm using stereo vision for object tracking

Partial Mutual Information Based Input Variable Selection for Supervised Learning Approaches to Voice Activity Detection