David Kortenkamp, R. Peter Bonasso and Robin Murphy, Artificial Intelligence and Mobile Robots, AAAI...
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Book reviews / Automatica 41 (2005) 731–738 737 About the reviewer M. Sami Fadali earned a BS in Electrical Engineering from Cairo Uni- versity in 1974, an MS from the Control Systems Center, UMIST, Eng- land, in 1977 and a Ph.D. from the University of Wyoming in 1980. He was an Assistant Professor of Electrical Engineering at the University of King Abdul Aziz, Jeddah, Saudi Arabia, 1981–1983. From 1983–1985, he was a Post Doctoral Fellow at Colorado State University. In 1985, he joined the Electrical Engineering Department at the University of Nevada, Reno, where he is currently Professor of Electrical Engineering. In 1994, he was a visiting professor at Oakland University and GM Research and Development Labs. He spent the summer of 2000 as a Senior Engineer at TRW, San Bernardino. His research interests are in the areas of robust control, robust stability, fault detection and fuzzy logic control. M. Sami Fadali is a senior member of the IEEE. doi:10.1016/j.automatica.2004.10.020 David Kortenkamp, R. Peter Bonasso and Robin Mur- phy, Artificial Intelligence and Mobile Robots, AAAI Press/The MIT Press, USA, ISBN: 0-262-61137-6, 1998 (390pp.). The book under review shows the application of artifi- cial intelligence (AI) to mobile robots by presenting 13 case studies of successful mobile robot systems. These intelli- gent robots perform different tasks, from giving tours to col- lecting trash, and have been tested in natural and unknown environments like offices, outdoors on land or under water, where they have demonstrated their robustness. The cases are organized in three fundamental parts: Navigation and mapping. The navigation of an au- tonomous mobile robot in an environment includes four interrelated activities: exploration, which is the strategy that guides the robot to select the next direction to go; mapping, which implies the construction of a spatial representation of the environment; localization, which is the strategy for determining the robot position in the map; and path plan- ning, which is the strategy the robot follows to find a path to the goal place, where that path could be optimal or not. The case studies in this part of the book use different approaches to represent spatial information. In the 1980s and early 1990s, the field of robotic mapping was divided into metric and topological approaches. A metric map cap- tures the geometric properties of the environment, while a topological map describes connectivity between differ- ent places. The topological approach has been addressed by many authors; e.g., Duckett, Marsland, and Shapiro (2002); Franz, Schölkopf, Mallot, and Bülthoff (1998), Kortenkamp and Weymouth (1994), Kuipers and Byun (1991), Mataric (1990), Owen and Nehmzow (1998), Pierce and Kuipers (1994), Thrun et al. (1998), and Wong and MacDonald (2003). In general, they apply their own algorithms to build the topological map, in some of which they include metric information of the environment, or they combine the con- struction of a topological map with a metric map. Four case studies are included in this part. The case by Thrun and his colleagues describes their robotic mapping ap- proach to integrate the metric and the topological mapping. The case by Kortenkamp and his colleagues describes a robot system which is able to navigate successfully in a 22 ×22 m arena, avoid static and dynamic obstacles, search for distinctive objects and return home by combining high- speed sonar-based obstacle avoidance with task-directed vision. The chapter by Nourbakhsh describes a robot system which navigates autonomously in real-world office build- ings, demonstrating that AI techniques can enable a robot to navigate reliably with less information than would other- wise be necessary. The last chapter in this part, by Koenig and Simmons, presents a navigation architecture that uses partially ob- servable Markov decision process models (POMDPs) for autonomous indoor navigation, considering office environ- ments specifically. Computer vision. Most of the case studies presented in this book use vision selectively and tailored to a specific task or problem. The chapters in this section address how behavior-based vision is being incorporated into programming robots. They concentrate on the architectural issues of integrating vision into behavioral-based robots. The chapter by Hor- swill describes a minimalist system in which simple visual processes produce very sophisticated behavior. The chap- ter by Murphy describes how mobile robots can control multiple sensors in both indoor and outdoor environments. Finally, the chapter by Schiller and colleagues provides a look at how vision can be used on an outdoor mobile robot. Architectures. In the context of the book, robot architec- tures refer to the arrangement of control software for the robot. The first chapter in this section, by Gat, describes the three layer architecture, which consists of three com- ponents: a reactive feedback control mechanism, a reactive plan-execution mechanism, and a mechanism for perform- ing time-consuming deliberative computations. The chapter by Konolige and Myers describes an architec- ture based on three fundamental concepts: coordination of behavior, coherence of modeling, and communication with other agents. The chapter by Firby and colleagues discusses a specific instance of using the first two layers of the “three layer’’ architecture to integrate robot motion and vision control in one framework. The chapter by Arkin and Balch presents their approach to cooperative multiagent robotics in the context of two real- world systems. The chapter by Huosheng Hu and Michael Brady de- scribes a layered control architecture inherently distributed, which was motivated by a real-world production environ- ment. Finally, the chapter by Brutzman and his colleagues de- scribes software architectures for an autonomous underwater
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Transcript of David Kortenkamp, R. Peter Bonasso and Robin Murphy, Artificial Intelligence and Mobile Robots, AAAI...
Book reviews / Automatica 41 (2005) 731–738 737
About the reviewerM. Sami Fadali earned a BS in Electrical Engineering from Cairo Uni-versity in 1974, an MS from the Control Systems Center, UMIST, Eng-land, in 1977 and a Ph.D. from the University of Wyoming in 1980. Hewas an Assistant Professor of Electrical Engineering at the University ofKing Abdul Aziz, Jeddah, Saudi Arabia, 1981–1983. From 1983–1985,he was a Post Doctoral Fellow at Colorado State University. In 1985,
he joined the Electrical Engineering Department at the University ofNevada, Reno, where he is currently Professor of Electrical Engineering.In 1994, he was a visiting professor at Oakland University and GMResearch and Development Labs. He spent the summer of 2000 as aSenior Engineer at TRW, San Bernardino. His research interests are inthe areas of robust control, robust stability, fault detection and fuzzy logiccontrol. M. Sami Fadali is a senior member of the IEEE.
doi:10.1016/j.automatica.2004.10.020
David Kortenkamp, R. Peter Bonasso and Robin Mur-phy, Artificial Intelligence and Mobile Robots, AAAIPress/The MIT Press, USA, ISBN: 0-262-61137-6, 1998(390pp.).
The book under review shows the application of artifi-cial intelligence (AI) to mobile robots by presenting 13 casestudies of successful mobile robot systems. These intelli-gent robots perform different tasks, from giving tours to col-lecting trash, and have been tested in natural and unknownenvironments like offices, outdoors on land or under water,where they have demonstrated their robustness.The cases are organized in three fundamental parts:Navigation and mapping. The navigation of an au-
tonomous mobile robot in an environment includes fourinterrelated activities: exploration, which is the strategy thatguides the robot to select the next direction to go; mapping,which implies the construction of a spatial representationof the environment; localization, which is the strategy fordetermining the robot position in the map; and path plan-ning, which is the strategy the robot follows to find a pathto the goal place, where that path could be optimal or not.The case studies in this part of the book use different
approaches to represent spatial information. In the 1980sand early 1990s, the field of robotic mapping was dividedinto metric and topological approaches. A metric map cap-tures the geometric properties of the environment, whilea topological map describes connectivity between differ-ent places. The topological approach has been addressed bymany authors; e.g.,Duckett, Marsland, and Shapiro (2002);Franz, Schölkopf, Mallot, and Bülthoff (1998), Kortenkampand Weymouth (1994), Kuipers and Byun (1991), Mataric(1990), Owen and Nehmzow (1998), Pierce and Kuipers(1994), Thrun et al. (1998), and Wong and MacDonald(2003). In general, they apply their own algorithms to buildthe topological map, in some of which they include metricinformation of the environment, or they combine the con-struction of a topological map with a metric map.Four case studies are included in this part. The case by
Thrun and his colleagues describes their robotic mapping ap-proach to integrate the metric and the topological mapping.The case by Kortenkamp and his colleagues describes
a robot system which is able to navigate successfully in a22×22m arena, avoid static and dynamic obstacles, searchfor distinctive objects and return home by combining high-speed sonar-based obstacle avoidance with task-directedvision.
The chapter by Nourbakhsh describes a robot systemwhich navigates autonomously in real-world office build-ings, demonstrating that AI techniques can enable a robotto navigate reliably with less information than would other-wise be necessary.The last chapter in this part, by Koenig and Simmons,
presents a navigation architecture that uses partially ob-servable Markov decision process models (POMDPs) forautonomous indoor navigation, considering office environ-ments specifically.Computer vision. Most of the case studies presented in
this book use vision selectively and tailored to a specifictask or problem.The chapters in this section address how behavior-based
vision is being incorporated into programming robots.They concentrate on the architectural issues of integratingvision into behavioral-based robots. The chapter by Hor-swill describes a minimalist system in which simple visualprocesses produce very sophisticated behavior. The chap-ter by Murphy describes how mobile robots can controlmultiple sensors in both indoor and outdoor environments.Finally, the chapter by Schiller and colleagues providesa look at how vision can be used on an outdoor mobilerobot.Architectures. In the context of the book, robot architec-
tures refer to the arrangement of control software for therobot. The first chapter in this section, by Gat, describesthe three layer architecture, which consists of three com-ponents: a reactive feedback control mechanism, a reactiveplan-execution mechanism, and a mechanism for perform-ing time-consuming deliberative computations.The chapter by Konolige and Myers describes an architec-
ture based on three fundamental concepts: coordination ofbehavior, coherence of modeling, and communication withother agents.The chapter by Firby and colleagues discusses a specific
instance of using the first two layers of the “three layer’’architecture to integrate robot motion and vision control inone framework.The chapter by Arkin and Balch presents their approach
to cooperative multiagent robotics in the context of two real-world systems.The chapter by Huosheng Hu and Michael Brady de-
scribes a layered control architecture inherently distributed,which was motivated by a real-world production environ-ment.Finally, the chapter by Brutzman and his colleagues de-
scribes software architectures for an autonomous underwater
738 Book reviews / Automatica 41 (2005) 731–738
robot and for the corresponding underwater virtual world,demonstrating how flexible the architectures must be to dealwith the real world, especially in an underwater environ-ment, which is extremely challenging for robots.The successful case studies included in this book demon-
strate the progress of the application of AI to robotics, andpresent an overview of the state of the art of robotic nav-igation and mapping, computer vision, and robotic archi-tectures. The book may be a good starting point to acquireknowledge of intelligent robotics.
References
Duckett, T., Marsland, S., & Shapiro, J. F. (2002). On-line learning ofglobally consistent maps.Autonomous Robots, 12, 287–300.
Franz, M. O., Schölkopf, B., Mallot, H. A., & Bülthoff, H. H. (1998).Learning view graphs for robot navigation.Autonomous Robots, 5,111–125.
Kortenkamp, D., & Weymouth, T. (1994). Topological mapping for mobilerobots using a combination of sonar and vision sensing. InProceedingsof the twelfth national conference on artificial intelligence. Menlo Park,AAAI, AAAI Press/MIT Press (pp. 979–984).
Kuipers, B., & Byun, Y. (1991). A robot exploration and mapping strategybased on a semantic hierarchy of spatial representations.Journal ofRobotics and Autonomous Systems, 8, 47–63.
Mataric, M. J. (1990).A distributed model for mobile robot environment-learning and navigation. Master’s thesis, Cambridge, MA, MIT.
Owen, C., Nehmzow, U. (1998). Landmark-based navigation for amobile robot. InProceedings of simulation of adaptive behavior ’98.Cambridge, MA: MIT Press.
Pierce, D., & Kuipers, B. (1994). Learning to explore and build maps. InProceedings of the twelfth national conference on artificial intelligence.Menlo Park, AAAI, AAAI Press/MIT Press (pp. 1264–1271).
Thrun, S., Bücken, A., Burgard, W., Fox, D., Fröhlinghaus, T., Hennig,D., Hofmann, T., Krell, M., & Schmidt, T. (1998). Map learning andhigh-speed navigation in RHINO. In: D. Kortenkamp, R.P. Bonasso, &R Murphy (Eds.),AI-based mobile robots: case studies of successfulrobot systemsCambridge, MA: MIT Press.
Wong, S., & MacDonald, B. (2003). A topological coverage algorithmfor mobile robots.Proceedings of the 2003 IEEE/RSJ internationalconference on intelligent robots and systems. Las Vegas, Nevada.
Alejandra BarreraComputer Engineering Department
Instituto Tecnológico Autónomo de MéxicoMexico City, Mexico
E-mail address:[email protected]
About the ReviewerAlejandra Barrera was born in Mexico City, Mexico, in 1972. Shereceived the Computer Engineering degree in 1995 and the Master degreein Information Technology and Management in 1999, both from theInstituto Tecnologico Autonomo de Mexico (ITAM). Since 1997, she hasbeen Titular Professor at ITAM. She is currently studying a doctorate inBiorobotics at Universidad Nacional Autonoma de Mexico (UNAM). Herresearch interests include Robotics, Biorobotics, Artificial Intelligence,Neural networks and Information Technology.
doi:10.1016/j.automatica.2004.11.009
