Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

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

We propose a semi-direct monocular visual odometry algorithm that is precise, robust, and faster than current state-of-the-art methods. The semi-direct approach eliminates the need of costly feature extraction and robust matching techniques for motion estimation. Our algorithm operates directly on pixel intensities, which results in subpixel precision at high frame-rates. A probabilistic mapping method that explicitly models outlier measurements is used to estimate 3D points, which results in fewer outliers and more reliable points. Precise and high frame-rate motion estimation brings increased robustness in scenes of little, repetitive, and high-frequency texture. The algorithm is applied to micro-aerial-vehicle state-estimation in GPS-denied environments and runs at 55 frames per second on the onboard embedded computer and at more than 300 frames per second on a consumer laptop. We call our approach SVO (Semi-direct Visual Odometry) and release our implementation as open-source software.

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SVO: Fast semi-direct monocular visual odometry

Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the point of view of swarm engineering: we focus mainly on ideas and concepts that contribute to the advancement of swarm robotics as an engineering field and that could be relevant to tackle real-world applications. Swarm engineering is an emerging discipline that aims at defining systematic and well founded procedures for modeling, designing, realizing, verifying, validating, operating, and maintaining a swarm robotics system. We propose two taxonomies: in the first taxonomy, we classify works that deal with design and analysis methods; in the second taxonomy, we classify works according to the collective behavior studied. We conclude with a discussion of the current limits of swarm robotics as an engineering discipline and with suggestions for future research directions.

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Swarm robotics: a review from the swarm engineering perspective

Guest editorial advances in multirobot systems

As research progresses in distributed robotic sys- tems, more and more aspects of multi-robot systems are being explored. This special issue on Multi-Robot Systems provides a broad sampling of the research that is currently ongoing in the field of distributed mobile robot systems. To help catego- rize this research, we have identified seven primary research top- ics within multi-robot systems — biological inspirations, commu- nication, architectures, localization/mapping/exploration, object transport and manipulation, motion coordination, and reconfig- urable robots. This editorial examines these research areas and discusses the special issue articles in this context. We conclude by identifying several additional open research issues in distributed mobile robotic systems.

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Editorial: Advances in Multi-Robot Systems

This paper presents the localization of a mobile robot while simultaneously mapping the position of the nodes of aWireless Sensor Network using only range measurements. The robot can estimate the distance to nearby nodes of the Wireless Sensor Network by measuring the Received Signal Strength Indicator (RSSI) of the received radio messages. The RSSI measure is very noisy, especially in an indoor environment due to interference and reflections of the radio signals. We adopted an Extended Kalman Filter SLAM algorithm to integrate RSSI measurements from the different nodes over time, while the robot moves in the environment. A simple pre-processing filter helps in reducing the RSSI variations due to interference and reflections. Successful experiments are reported in which an average localization error less than 1 m is obtained when the SLAM algorithm has no a priori knowledge on the wireless node positions, while a localization error less than 0.5 m can be achieved when the position of the node is initialized close to the their actual position. These results are obtained using a generic path loss model for the trasmission channel. Moreover, no internode communication is necessary in the WSN. This can save energy and enables to apply the proposed system also to fully disconnected networks

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Range-only SLAM with a mobile robot and a Wireless Sensor Networks

http://psfmr.univpm.it/2005/slide/Pagello/2-Second%20Lecture/Our%20paper%20on%20DVS/RAS%20Journal.pdf

Enrico Pagello

Papers

Guest editorial advances in multirobot systems

Editorial: Advances in Multi-Robot Systems

Range-only SLAM with a mobile robot and a Wireless Sensor Networks

Image-based Monte Carlo localisation with omnidirectional images

Multirobot motion coordination in space and time