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

A text that makes the mathematical underpinnings of robot motion accessible and relates low-level details of implementation to high-level algorithmic concepts. Robot motion planning has become a major focus of robotics. Research findings can be applied not only to robotics but to planning routes on circuit boards, directing digital actors in computer graphics, robot-assisted surgery and medicine, and in novel areas such as drug design and protein folding. This text reflects the great advances that have taken place in the last ten years, including sensor-based planning, probabalistic planning, localization and mapping, and motion planning for dynamic and nonholonomic systems. Its presentation makes the mathematical underpinnings of robot motion accessible to students of computer science and engineering, rleating low-level implementation details to high-level algorithmic concepts.

Principles of Robot Motion: Theory, Algorithms, and Implementations

The open motion planning library (OMPL) is a new library for sampling-based motion planning, which contains implementations of many state-of-the-art planning algorithms. The library is designed in a way that it allows the user to easily solve a variety of complex motion planning problems with minimal input. OMPL facilitates the addition of new motion planning algorithms, and it can be conveniently interfaced with other software components. A simple graphical user interface (GUI) built on top of the library, a number of tutorials, demos, and programming assignments are designed to teach students about sampling-based motion planning. The library is also available for use through Robot Operating System (ROS).

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The Open Motion Planning Library

Traditional robots have rigid underlying structures that limit their ability to interact with their environment. For example, conventional robot manipulators have rigid links and can manipulate objects using only their specialised end effectors. These robots often encounter difficulties operating in unstructured and highly congested environments. A variety of animals and plants exhibit complex movement with soft structures devoid of rigid components. Muscular hydrostats e.g. octopus arms and elephant trunks are almost entirely composed of muscle and connective tissue and plant cells can change shape when pressurised by osmosis. Researchers have been inspired by biology to design and build soft robots. With a soft structure and redundant degrees of freedom, these robots can be used for delicate tasks in cluttered and/or unstructured environments. This paper discusses the novel capabilities of soft robots, describes examples from nature that provide biological inspiration, surveys the state of the art and outlines existing challenges in soft robot design, modelling, fabrication and control.

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Soft robotics: Biological inspiration, state of the art, and future research

Recently, a new class of randomized path planning methods, known as Probabilistic Roadmap Methods (prms) have shown great potential for solving complicated high-dimensional problems. prms use randomization (usually during preprocessing) to construct a graph of representative paths in C-space (a roadmap) whose vertices correspond to collision-free con gurations of the robot and in which two vertices are connected by an edge if a path between the two corresponding con gurations can be found by a local planning method.

OBPRM: an obstacle-based PRM for 3D workspaces

While many methods to simulate flocking behaviors have been proposed, these techniques usually only provide simplistic navigation and planning capabilities because each flock member's behavior depends only on its local environment. In this work, we investigate how the addition of global information in the form of a roadmap of the environment enables more sophisticated flocking behaviors and supports global navigation and planning. In this paper, we propose new techniques for four distinct group behaviors: homing, goal searching, traversing narrow areas and shepherding. Extending ideas from cognitive modeling, we embed behavior rules in individual flock members and in the roadmap. These embedded behaviors enable the creatures to modify their actions based on their current location and state. For example, the flock might move as an unordered group in open regions and in a follow-the-leader fashion through narrow passages. These behaviors exploit global knowledge of the environment and utilize information gathered by all flock members which is communicated by allowing individual flock members to dynamically update the shared roadmap to reflect (un)desirable routes or regions. We present experimental results showing how the judicious use of simple roadmaps of the environment enables complex behaviors to be obtained at minimal cost. Animations can be viewed at http://parasol.tamu.edu.

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Better group behaviors in complex environments using global roadmaps

In this paper, we investigate methods for motion planning for deformable robots. Our framework is based on a probabilistic roadmap planner. As with traditional motion planning, the planner's goal is to find a valid path for the robot. Unlike typical motion planning, the robot is allowed to change its shape (deform) to avoid collisions as it moves along the path. We propose a two-stage approach. First, an 'approximate' path which may contain collisions is found. Next, we attempt to correct any collisions on this path by deforming the robot. We propose and analyze two methods for performing the deformations. Both techniques are inspired by a physically correct behavior, but are more efficient than completely, physically correct methods. Our approach can be applied in several domains, including flexible robots, computer modeling and animation, and biological simulations.

Probabilistic roadmap motion planning for deformable objects

Flocking behavior is very common in nature, and there have been ongoing research efforts to simulate such behavior in computer animations and robotics applications. Generally, such work considers behaviors that can be determined independently by each flock member solely by observing its local environment, e.g., the speed and direction of its neighboring flock members. Since flock members are not assumed to have global information about the environment, only very simple navigation and planning techniques have been considered for such flocks. In this paper, we investigate how the addition of global information in the form of a roadmap of the environment enables more sophisticated flocking behaviors. In particular, we study and propose new techniques for three distinct group behaviors: homing, exploring and shepherding. These behaviors exploit global knowledge of the environment and utilize knowledge gathered by all flock members. This knowledge is communicated by allowing individual flock members to dynamically update the shared roadmap to reflect (un)desirable routes or regions. We present experimental results showing how the judicious use of simple roadmaps of the environment enables more complex behaviors to be obtained at minimal cost.

Roadmap-based flocking for complex environments

Mobile entity navigation in dynamic environments is an essential part of many mission critical applications like search and rescue and fire fighting. The dynamism of the environment necessitates the mobile entity to constantly maintain a high degree of awareness of the changing environment. This criteria makes it difficult to achieve good navigation performance by using just on-board sensors and existing navigation methods and motivates the use of wireless sensor networks (WSNs) to aid navigation. In this paper, we present a novel approach that integrates a roadmap based navigation algorithm with a novel WSN query protocol called Roadmap Query (RQ). RQ enables collection of frequent, up-to-date information about the surrounding environment, thus allowing the mobile entity to make good navigation decisions. Simulation results under realistic fire scenarios show that in highly dynamic environments RQ outperforms existing approaches in both navigation performance and communication cost. We also present a mobile agent based implementation of RQ along with preliminary experimental results, on Mica2 motes.

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O. Burchan Bayazit

Papers

OBPRM: an obstacle-based PRM for 3D workspaces

Better group behaviors in complex environments using global roadmaps

Probabilistic roadmap motion planning for deformable objects

Roadmap-based flocking for complex environments

Roadmap query for sensor network assisted navigation in dynamic environments