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

In order to use an Ethernet communication network for signal transmission in a feedback control loop, variable transport delay introduced by the network must be considered. In this paper a stochastic model of transport delay is proposed that can be used as the base for synthesis of a control loop.

Modeling of transport delay on Ethernet communication networks

The rapid growth of e-commerce increases the need for larger warehouses and their automation, thus using robots as assistants to human workers becomes a priority. In order to operate efficiently and safely, robot assistants or the supervising system should recognize human intentions. Theory of mind (ToM) is an intuitive conception of other agents’ mental state, i.e., beliefs and desires, and how they cause behavior. In this paper we present a ToM-based algorithm for human intention recognition in flexible robotized warehouses. We have placed the warehouse worker in a simulated 2D environment with three potential goals. We observe agent’s actions and validate them with respect to the goal locations using a Markov decision process framework. Those observations are then processed by the proposed hidden Markov model framework which estimated agent’s desires. We demonstrate that the proposed framework predicts human warehouse worker’s desires in an intuitive manner and in the end we discuss the simulation results.

Human Intention Recognition in Flexible Robotized Warehouses based on Markov Decision Processes

When tracking people or other moving objects with a mobile robot, detection is the first and most critical step. At first most researchers focused on the tracking algorithms, but recently AdaBoost (supervised machine learning technique) was used for people legs detection in 2D range data. The results are promising, but it is unclear if the obtained classifier could be used on the data from another sensor. As it would be a huge inconvenience having to train a classifier for every sensor (setup), we set out to find if, and when is a classifier trained on one sensor setup transferable to another sensor setup. We tested two sensors in five different setups. In total, we acquired 2455 range scans. Experiments showed that the classifier trained on noisier sensor data performed better at classification of data coming from other sensor setups. Classifiers trained on less noisy data were shown to be overconfident, and performed poorly on noisy data. Furthermore, experiments showed that classifiers learned on ten times smaller datasets performed as good as classifiers trained on larger datasets. Since AdaBoost is a supervised learning technique, obtaining same classifier efficiency with significantly smaller dataset means less hand labeling of the data for the same results.

https://bib.irb.hr/datoteka/474496.45_raad2010.pdf

Experimental comparison of AdaBoost algorithms applied on leg detection with different range sensor setups

In this paper we address one of the major problems of mobile robots navigation, the creation of a map from local sensor data collected as the robot moves around an unknown environment. Map building is the problem of generating models of robot environments from sensor data and can be often referred as a concurrent mapping and localization problem. That is to build a consistent map, the mobile robot has to know its pose. We present here three approaches to create occupancy grid maps from sonar's data and suggest a simple solution to improve the mapping quality in cases of irregular disposition of the sonars. The proposed solution has been tested on the mobile robot Pioneer 2DX.

Improvements of Occupancy Grid Maps by Sonar Data Corrections

This paper presents a global vision system for tracking of multiple mobile robots as a part of intelligent space, with special emphasis on the following requirements: (i) the system must have the ability to track unlimited number of robots using images from one or more cameras, (ii) the system must have acceptable price, and (iii) the system must operate in real time, so algorithms with lower computational cost are preferred. In order to fulfill these requirements we used an IEEE 1394 camera with 80 fps, which delivers the image in so called Bayer format. We also designed a specific robot identification mark which enables measurement of robot locations directly in Bayer format image. In this way computational complexity was significantly reduced. Experimental testing demonstrated robust behavior of developed global vision system.

Ivan Petrović

Papers

Modeling of transport delay on Ethernet communication networks

Human Intention Recognition in Flexible Robotized Warehouses based on Markov Decision Processes

Experimental comparison of AdaBoost algorithms applied on leg detection with different range sensor setups

Improvements of Occupancy Grid Maps by Sonar Data Corrections

Global Vision Based Tracking of Multiple Mobile Robots