Guest Editorial: Computational Intelligence in Robotics and Automation

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<ul><li><p>Autonomous Robots 9, 56, 2000c 2000 Kluwer Academic Publishers. Manufactured in The Netherlands.</p><p>Guest Editorial: Computational Intelligence in Robotics and Automation</p><p>The field of computational intelligence has been maturing and growing quickly. The use of artificial neural networks,fuzzy logic, evolutionary computation, and other related disciplines in artificial intelligence, together form thefoundation of computational intelligence. One of the major application areas of computational intelligence can befound in robotics and automation, where these computational algorithms are often combined with sensory inputfrom the environment and actuators with which to effect changes in the environment. The application areas ofrobotics and automation seem to thrive on computational intelligence.</p><p>In response to the need of bringing together researchers and practitioners working on different aspects of com-putational intelligence as they relate to robotics, the International Symposium of Computational Intelligence inRobotics and Automation (CIRA) was initiated. The first International Symposium on Computational Intelligenceon Robotics and Automation was organized in 1997 by the IEEE Robotics and Automation Society together withthe IEEE Neural Network Council in Monterey, CA. This special issue brings together representative papers fromthe CIRA 1999 symposium selected by the reviewers and the program chairs. Many papers were deemed to beacceptable for this special issue; we tried to choose ones that represented a cross section of the techniques andapplications presented.</p><p>As a community, we are thinking more about higher level robot cognition and reasoning as well as the interactionof multiple robot behaviors within a single platform and cooperation of multiple robots. This is reflected in the papersthat were presented at the conference. This year we had sessions devoted to arbitration and behavior coordination,multi-agent systems, high-level planning, and robot interactions with living things. Control was still importantwith sessions on grasping, manipulation, motion control, and nonholonomic control. The traditional areas ofposition estimation, path planning, and exploration and mapping were also well represented. Vision research waspresented in sessions on object recognition, active vision and attention control. Several papers addressed robotprogramming and design issues. As in the past two symposia, many papers addressed aspects of learning in roboticsand automation.</p><p>Of the 98 papers that were submitted, 59 papers were accepted after a thorough review process. This is a 60%acceptance rate and reflects the high quality of this symposium. We were very pleased with the quality of thepublished papers.</p><p>This year, papers were assigned to tracks based on application area instead of computational technique. Thismeans that researchers were able to see different approaches to solving similar problems. We hope that this willhelp to foster cross-disciplinary approaches.</p><p>Hyams et al. present a technique for position estimation and cooperative navigation of a micro rover as a largerrobot tracks it through large spaces using color segmentation. Rosenblatt presents a new means of action selectionbased on maximizing expected utility. Yang and Meng present a neural network approach to real-time collision-freepath planning of robot manipulators in a non-stationary environment. Anglani et al. present a solution to manip-ulation control using Q-learning. In Howard and Bekey, a learning technique is used to address the problem ofrobotic grasping for 3-D deformable objects. Ramirez et al. present a novel modeling and control synthesis techniquefor flexible manufacturing workcells that allows part-routing flexibility. In Pollack and McCarthy, rationale-basedmonitoring is applied to a mobile robot environment for improving planning performance. Finally, Horswill presents</p></li><li><p>6 Schultz and Nourbakhsh</p><p>a functional programming language in which most of the features of the popular behavior-based robot architecturescan be concisely written as reusable software abstractions.</p><p>Alan C. SchultzNavy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory,Washington, DC 20375-5337Email:</p><p>Illah R. NourbakhshThe Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213Email:</p><p>Alan C. Schultz is a computer scientist in the Adaptive Systems Group at the Navy Center for Applied Research in Artificial Intelligence, part ofthe Naval Research Laboratory in Washington, DC, where he leads several research projects. His research is in the area of evolutionary robotics,machine learning and evolutionary computation. Mr. Schultz earned his Masters of Science degree in computer science at George MasonUniversity in 1988, and a B.A. in Communications from American University in 1979. Mr. Schultz is a member of IEEE, IEEE ComputerSociety, AAAI, and ACM.</p><p>Illah R. Nourbakhsh is an Assistant Professor of Robotics in The Robotics Institute at Carnegie Mellon University. He received his Ph.D. incomputer science from Stanford University in 1966. He is co-founder of the Toy Robots Initiative at The Robotics Institute. His current researchprojects include electric wheelchair sensing devices, robot learning, theoretical robot architecture, believable robot personality, visual navigationand robot locomotion. His past research has included protein structure prediction under the GENOME project, software reuse, interleavingplanning and execution and planning and scheduling algorithms. At the Jet Propulsion Laboratory he was a member of the New MilleniumRapid Prototyping Team for the design of autonomous spacecraft. He is a founder and chief scientist of Blue Pumpkin Software, Inc. andMobot, Inc. He is also chief scientist of Hyperbot Inc., and leads robot autonomy for Probotics, Inc.</p></li></ul>