As people attempt to build biomimetic robots and realize automation processes through artificial intelligence, computational intelligence plays a very important role in robotics and automation. This special issue contains several important papers that address various aspects of computational intelligence in robotics and automation. While acknowledging its limited coverage, this special issue offers a range of interesting contributions such as intelligent trajectory planning for flying and land mobile robots, fuzzy decision making, control of rigid and teleoperated robots, modeling of human sensations, and intelligent sensor fusion techniques. Let us scan through these contributions of this special issue. The first paper, "Planar Spline Trajectory Following for an Autonomous Helicopter," by Harbick et al., proposes a technique for planar trajectory following for an autonomous aerial robot. A trajectory is modeled as a planar spline. A behavior-based control system stabilizes the robot and enforces trajectory following of an autonomous helicopter with a reasonable trajectory tracking error on the order of the size of the helicopter (1.8m). In the second paper, "A Biologically Inspired Approach to Collision-Free Path Planning and Tracking Control of a Mobile Robot," by Yang et al., a novel biologically inspired neural network approach is proposed for dynamic collision-free path planning and stable tracking control of a nonholonomic mobile robot in a non-stationary environment, based on shunting equations derived from Hodgkin and Huxley's biological membrane equation. The third paper, "Composite Fuzzy Measure and Its Application to Decision Making," by Kaino and Kaoru, builds a composite fuzzy measure from fuzzy measures defined on fuzzy measurable spaces using composite fuzzy weights by the authors, with a successful application to an automobile factory capital investment decision making problem. In "Intelligent Control of a Miniature Climbing Robot," by Xiao et al., a fuzzy logic based intelligent optimal control system for a miniature climbing robot to achieve precision motion control, minimized power consumption, and versatile behaviors is presented with validation via experimental studies. The fifth paper, "Incorporating Motivation in a Hybrid Robot Architecture," by Stoytchev and Arkin, describes a hybrid mobile robot architecture capable of deliberative planning, reactive control, and motivational drives, which addresses three main challenges for robots living in human-inhabited environments: operating in dynamic and unpredictable environment, dealing with high-level human commands, and engaging human users. Experimental results for a fax delivery mission in a normal office environment are included. In the next paper, "Intelligent Scaling Control for Internet-based Teleoperation," by Liu et al., an adaptive scaling control scheme, with a neural network based time-delay prediction algorithm trained using the maximum entropy principle, is proposed with successful experimental studies on an Internet mobile robot platform. The next paper, "Feature Extraction of Robot Sensor Data Using Factor Analysis for Behavior Learning," by Fung and Liu, discusses important knowledge extraction of sensor data for robot behavior learning using a new approach based on the inter-correlation of sensor data via factor analysis and construction of logical perceptual space by hypothetical latent factors. Experimental results are included to demonstrate the process of logical perceptual space extraction from ultrasonic range data for robot behavior learning. "Trajectory Planning of Mobile Robots Using DNA Computing," by Kiguchi et al., presents an optimal trajectory planning method for mobile robots using Watson-Crick pairing to find the shortest trajectory in the robot working area with the DNA sequences representing the locations of the obstacles removed during the process. The proposed algorithm is especially suitable for computing on a DNA molecular computer. In the ninth paper, "Computational Intelligence for Modeling Human Sensations in Virtual Environments," by Lee and Xu, cascade neural networks with node-decoupled extended Kalman filter training for modeling human sensations in virtual environments are proposed, with a stochastic similarity measure based on hidden Markov models to calculate the relative similarity between model-generated sensations and actual human sensations. A new input selection technique, based on independent component analysis capable of reducing the data size and selecting the stimulus information, is developed and reported. The next paper, "Intelligent Sensor Fusion in Robotic Prosthetic Eye System," by Gu et al., is concerned with the design, sensing and control of a robotic prosthetic eye that moves horizontally in synchronization with the movement of the natural eye. It discusses issues on sensor failure detection and recovery and sensor data fusion techniques using statistical methods and artificial neural network based methods. Simulation and experimental results are included to demonstrate the effectiveness of the results. The final contribution in our collection is a paper by Sun et al., entitled "A Position Control of Direct-Drive Robot Manipulators with PMAC Motors Using Enhanced Fuzzy PD Control." It presents a simple and easy-to-implement position control scheme for direct-drive robot manipulators based on enhanced fuzzy PD control, incorporating two nonlinear tracking differentiators into a conventional PD controller. Experiments on a single-link manipulator directly driven by a permanent magnet AC (PMAC) motor demonstrate the validity of the proposed approach. The Guest Editors would like to thank the contributors and reviewers of this special issue for their time and effort in making this special issue possible. They would also like to express their sincere appreciation to the JACIII editorial board, especially Profs. Kaoru and Fukuda, Editors-in-Chief and Kenta Uchino, Managing Editor, for the opportunity and help they provided for us to put together this special issue.
Neural Behavior Chain Learning of Mobile Robot Actions