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

Extended information filter on matrix Lie groups

In this paper we present an algorithm for detection, extraction and tracking of moving objects using a 3D laser range sensor. First, ground extraction is performed using random sample consensus for model parameter estimation. Afterwards, to downsample the point cloud, a voxel grid filtering is executed and octree data structure is used. This data structure enables an efficient detection of differences between two consecutive point clouds, based on which clustering of dynamic parts of the cloud is performed. The obtained clusters are then expanded over the set of static voxels in order to cover entire objects. In order to account for ego-motion an iterative closest point registration technique with an initial transformation guess obtained by odometry of the platform is used. As the final step, we present a tracking algorithm based on joint probabilistic data association (JPDA) filter with variable process and measurement noise taking into account velocity and position of the tracked objects. However, JPDA filter assumes a constant and known number of objects in the scene, and therefore we use track management based on entropy. Experiments are performed using a setup consisting of a Velodyne HDL-32E mounted on top of a mobile platform in order to verify the developed algorithms.

Detection and tracking of dynamic objects using 3D laser range sensor on a mobile platform

This paper is concerned with the problem of finding a time-optimal velocity profile along the predefined path for static formations of mobile robots in order to traverse the path in shortest time and to satisfy, for each mobile robot in the formation, velocity, acceleration, tip over and wheel slip prevention constraints. Time-optimal velocity planning is achieved using so called bang-bang control where minimum and maximum accelerations of the formation are alternating. The developed trajectory planning algorithm is demonstrated on the formation of differential drive mobile robots.

Time-optimal velocity planning along predefined path for static formations of mobile robots

In the development of mobile robot control algorithms the researchers are often faced with the problem of ensuring safety operation of used mobile robot during the real mobile robot experiment phase. For this reason implemented control algorithms have to be firstly tested in various simulation scenarios. Appropriate simulation tools have to enable easy creation of different simulation setups, usage of different sensors, collection and evaluation of sensor measurements, examination of noise influence, etc. This paper describes a developed simulation tool for such a purpose – AMORsim (Autonomous MObile Robots simulator), a mobile robot simulator that’ s able to simulate a three-wheeled user-defined mobile robot in a two-dimensional environment. It’ s written in Matlab, which is a common, and well-known simulation environment.

/pdf/amorsim-a-mobile-robot-simulator-for-matlab-44e71mkj8f.pdf

Ivan Petrović

Papers

Extended information filter on matrix Lie groups

Detection and tracking of dynamic objects using 3D laser range sensor on a mobile platform

Time-optimal velocity planning along predefined path for static formations of mobile robots

AMORsim - A Mobile Robot Simulator for Matlab

Neural network based sliding mode control of electronic throttle