An introduction to the techniques and algorithms of the newest field in robotics. Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

A text that makes the mathematical underpinnings of robot motion accessible and relates low-level details of implementation to high-level algorithmic concepts.

The International Conference on Intelligent Autonomous Systems (IAS) conference brings together leading researchers interested in all aspects of autonomy and adaptivity of artificial systems. This book contains the proceedings of the tenth IAS in Baden Baden, Germany.

For many years, Arti?cial Intelligence technology has served in a great variety of successful applications. AI researchand researchershave contributed much to the vision of the so-called Information Society. As early as the 1980s, some of us imagined distributed knowledge bases containing the explicable knowledge of a company or any other organization. Today, such systems are becoming reality. In the process, other technologies have had to be developed and AI-technology has blended with them, and companies are now sensitive to this topic. TheInternetandWWWhaveprovidedtheglobalinfrastructure,whileatthe same time companies have become global in nearly every aspect of enterprise. This process has just started, a little experience has been gained, and therefore it is tempting to re?ect and try to forecast, what the next steps may be. This has given us one of the two main topics of the 23rd Annual German Conference on Arti?cial Intelligence (KI-99)held at the University of Bonn: The Knowledge Society. Two of our invited speakers, Helmut Willke, Bielefeld, and Hans-Peter Kriegel, Munich, dwell on di?erent aspects with di?erent perspectives. Helmut Willke deals with the concept of virtual organizations, while Hans-Peter Kriegel applies data mining concepts to pattern recognitiontasks.The three application forums are also part of the Knowledge Society topic: “IT-based innovation for environment and development”, “Knowledge management in enterprises”, and “Knowledgemanagementinvillageandcityplanningoftheinformationsociety”.

学生や研究者、エンジニアなど、ロボットの実装を行う全ての人に向けて書かれた学術書です。ベイズ則とその拡張を数学的背景に、『確率ロボティクス』の主要なアルゴリズムを、擬似コードによる実装例を交えてボットシステムを実現するためのステップとなる一冊。

「確率ロボティクス」は、環境やシステムが持つ「不確実さ」(予測不可能な事象)に確率・統計を駆使して対処するのを特徴とする分野です。人の作業空間により近い位置への進出を期待される次世代のロボットでは、この分野のアルゴリズム設計は有力な枠組みの1つとして注目を集めています。また、ロボットのみでなく、自動車の自動走行などへも応用可能な分野です。本書は学生、研究者、エンジニアなど、ロボットの実装を行う全ての人に向けて書かれています。「確率ロボティクス」の主要なアルゴリズムを、ベイズ則とその拡張を数学的背景に、擬似コードによる実装例や実験結果を交えて詳細に解説・議論しています。確率の基本的な法則などの基礎的な解説もされており、また、高度な数学の使用は極力避けられているため、技術的背景の異なる人にとっても理解しやすい内容になっています。確率ロボティクスを学ぶには必須の1冊。Sebastian Thrun,Wolfram Burgard,Dieter Foxらによる「Probabilistic Robotics」の日本語版、待望の復刊です。

Abstract: "This paper addresses the problem of building large- scale geometric maps if [sic] indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximum- likelihood estimation problem. It then devises a practical algorithm for generating the most likely map from data, along with the most likely path taken by the robot. Experimental results in cyclic environments of size up to 80 by 25 meter illustrate the appropriateness of the approach."

Abstract: "This article describes the software architecture of an autonomous, interactive tour-guide robot. It presents a modular, distributed software architecture, which integrates localization, mapping, collision avoidance, planning, and various modules concerned with user interaction. The approach does not require any modifications to the environment. To cope with the various challenges in dynamic and ill-structured environments, the software relies on probabilistic computation, on-line learning, any-time algorithms, and distributed control. Special emphasis has been placed on the design of interactive capabilities that appeal to people's intuition. In mid-1997, the robot was successfully deployed in a densely populated museum, demonstrating reliable operation in hazardous public environments, and raising the museum's attendance by more than 50%. In addition, people all over the world controlled the robot through the Web."

This primer describes particle filters and relevant applications in the context of robot navigation and illustrates that these filters are powerful tools that can robustly estimate the state of the robot and its environment.