scholarly journals Perspectives of Computational Intelligence in Robotics and Automation

Author(s):  
Max Q-H Meng ◽  
◽  
Hong Zhang ◽  

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.

1990 ◽  
Vol 36 (9) ◽  
pp. 1544-1550 ◽  
Author(s):  
W S Lob

Abstract Mobile robots perform fetch-and-carry tasks autonomously. An intelligent, sensor-equipped mobile robot does not require dedicated pathways or extensive facility modification. In the hospital, mobile robots can be used to carry specimens, pharmaceuticals, meals, etc. between supply centers, patient areas, and laboratories. The HelpMate (Transitions Research Corp.) mobile robot was developed specifically for hospital environments. To reach a desired destination, Help-Mate navigates with an on-board computer that continuously polls a suite of sensors, matches the sensor data against a pre-programmed map of the environment, and issues drive commands and path corrections. A sender operates the robot with a user-friendly menu that prompts for payload insertion and desired destination(s). Upon arrival at its selected destination, the robot prompts the recipient for a security code or physical key and awaits acknowledgement of payload removal. In the future, the integration of HelpMate with robot manipulators, test equipment, and central institutional information systems will open new applications in more localized areas and should help overcome difficulties in filling transport staff positions.


1991 ◽  
Vol 3 (5) ◽  
pp. 379-386
Author(s):  
Hesin Sai ◽  
◽  
Yoshikuni Okawa

As part of a guidance system for mobile robots operating on a wide and flat floor, such as an ordinary factory or a gymnasium, we have proposed a special-purpose sign. It consists of a cylinder, with four slits, and a fluorescent light, which is placed on the axis of the cylinder. Two of the slits are parallel to each other, and the other two are angled. A robot obtains an image of the sign with a TV camera. After thresholding, we have four bright sets of pixels which correspond to the four slits of the cylinder. We compute by measuring the relative distances between the four points, the distance and the angle to the direction of the sign can be computed using simple geometrical equations. Using a personal computer with an image processing capability, we have investigated the accuracy of the proposed position identification method and compared the experimental results against the theoretical analysis of measured error. The data shows good coincidence between the analysis and the experiments. Finally, we have built a movable robot, which has three microprocessors and a TV camera, and performed several control experiments for trajectory following.


2008 ◽  
Vol 20 (2) ◽  
pp. 213-220 ◽  
Author(s):  
Kimitoshi Yamazaki ◽  
◽  
Takashi Tsubouchi ◽  
Masahiro Tomono ◽  
◽  
...  

In this paper, a modeling method to handle furniture is proposed. Real-life environments are crowded with objects such as drawers and cabinets that, while easily dealt with by people, present mobile robots with problems. While it is to be hoped that robots will assist in multiple daily tasks such as putting objects in into drawers, the major problems lies in providing robots with knowledge about the environment efficiently and, if possible, autonomously.If mobile robots can handle these furniture autonomously, it is expected that multiple daily jobs, for example, storing a small object in a drawer, can be performed by the robots. However, it is a perplexing process to give several pieces of knowledge about the furniture to the robots manually. In our approach, by utilizing sensor data from a camera and a laser range finder which are combined with direct teaching, a handling model can be created not only how to handle the furniture but also an appearance and 3D shape. Experimental results show the effectiveness of our methods.


1999 ◽  
Vol 11 (1) ◽  
pp. 1-1
Author(s):  
Kiyoshi Komoriya ◽  

Mobility, or locomotion, is as important a function for robots as manipulation. A robot can enlarge its work space by locomotion. It can also recognize its environment well with its sensors by moving around and by observing its surroundings from various directions. Much researches has been done on mobile robots and the research appears to be mature. Research activity on robot mobility is still very active; for example, 22% of the sessions at ICRA'98 - the International Conference on Robotics and Automation - and 24% of the sessions at IROS'98 - the International Conference on Intelligent Robots and Systems - dealt with issues directly related to mobile robots. One of the main reasons may be that intelligent mobile robots are thought to be the closest position to autonomous robot applications. This special issue focuses on a variety of mobile robot research from mobile mechanisms, localization, and navigation to remote control through networks. The first paper, entitled ""Control of an Omnidirectional Vehicle with Multiple Modular Steerable Drive Wheels,"" by M. Hashimoto et al., deals with locomotion mechanisms. They propose an omnidirectional mobile mechanism consisting of modular steerable drive wheels. The omnidirectional function of mobile mechanisms will be an important part of the human-friendly robot in the near future to realize flexible movements in indoor environments. The next three papers focus on audiovisual sensing to localize and navigate a robot. The second paper, entitled ""High-Speed Measurement of Normal Wall Direction by Ultrasonic Sensor,"" by A. Ohya et al., proposes a method to measure the normal direction of walls by ultrasonic array sensor. The third paper, entitled ""Self-Position Detection System Using a Visual-Sensor for Mobile Robots,"" is written by T. Tanaka et al. In their method, the position of the robot is decided by measuring marks such as name plates and fire alarm lamps by visual sensor. In the fourth paper, entitled ""Development of Ultra-Wide-Angle Laser Range Sensor and Navigation of a Mobile Robot in a Corridor Environment,"" written by Y Ando et al., a very wide view-angle sensor is realized using 5 laser fan beam projectors and 3 CCD cameras. The next three papers discussing navigation problems. The fifth paper, entitled ""Autonomous Navigation of an Intelligent Vehicle Using 1-Dimensional Optical Flow,"" by M. Yamada and K. Nakazawa, discusses navigation based on visual feedback. In this work, navigation is realized by general and qualitative knowledge of the environment. The sixth paper, entitled ""Development of Sensor-Based Navigation for Mobile Robots Using Target Direction Sensor,"" by M. Yamamoto et al., proposes a new sensor-based navigation algorithm in an unknown obstacle environment. The seventh paper, entitled ""Navigation Based on Vision and DGPS Information for Mobile Robots,"" S. Kotani et al., describes a navigation system for an autonomous mobile robot in an outdoor environment. The unique point of their paper is the utilization of landmarks and a differential global positioning system to determine robot position and orientation. The last paper deals with the relationship between the mobile robot and computer networks. The paper, entitled ""Direct Mobile Robot Teleoperation via Internet,"" by K. Kawabata et al., proposes direct teleoperation of a mobile robot via the Internet. Such network-based robotics will be an important field in robotics application. We sincerely thank all of the contributors to this special issue for their cooperation from the planning stage to the review process. Many thanks also go to the reviewers for their excellent work. We will be most happy if this issue aids readers in understanding recent trends in mobile robot research and furthers interest in this research field.


2018 ◽  
Vol 30 (4) ◽  
pp. 540-551 ◽  
Author(s):  
Shingo Nakamura ◽  
◽  
Tadahiro Hasegawa ◽  
Tsubasa Hiraoka ◽  
Yoshinori Ochiai ◽  
...  

The Tsukuba Challenge is a competition, in which autonomous mobile robots run on a route set on a public road under a real environment. Their task includes not only simple running but also finding multiple specific persons at the same time. This study proposes a method that would realize person searching. While many person-searching algorithms use a laser sensor and a camera in combination, our method only uses an omnidirectional camera. The search target is detected using a convolutional neural network (CNN) that performs a classification of the search target. Training a CNN requires a great amount of data for which pseudo images created by composition are used. Our method is implemented in an autonomous mobile robot, and its performance has been verified in the Tsukuba Challenge 2017.


2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Talgat Islamgozhayev ◽  
Maksat Kalimoldayev ◽  
Arman Eleusinov ◽  
Shokan Mazhitov ◽  
Orken Mamyrbayev

Abstract The use of mobile robots is becoming popular in many areas of service because they ensure safety and good performance while working in dangerous or unreachable locations. Areas of application of mobile robots differ from educational research to detection of bombs and their disposal. Based on the mission of the robot they have different configurations and abilities – some of them have additional arms, cranes and other tools, others use sensors and built-in image processing and object recognition systems to perform their missions. The robot that is described in this paper is mobile robot with a turret mounted on top of it. Different approaches have been tested while searching for best method suitable for image processing and template matching goals. Based on the information from image processing unit the system executes appropriate actions for planning motions and trajectory of the mobile robot.


2012 ◽  
Vol 522 ◽  
pp. 618-622
Author(s):  
Ying Xiong ◽  
Shi De Xiao ◽  
Shuang Jiang Lei ◽  
Feng Zha

An intelligent tracking control system based on the micro-control unit (MCU) has been developed to control the motors by sensing the change of black guide lines. After the training of the BP Neural Network, the MCU is able to make decisions quickly and accurately for various situations during robot moving. Using MCU technology to control the motors, the system is compatible for both manual and automatic control. The experiment shows that the mobile robot could follow the change of black guide lines accurately and quickly, and stillness and out-of-orbit were effectively inhibited during moving. The proposed tracking control system based on the BP Neural Network has been verified to have high reliability.


Author(s):  
Francisco García-Córdova ◽  
Antonio Guerrero-González ◽  
Fulgencio Marín-García

Neural networks have been used in a number of robotic applications (Das & Kar, 2006; Fierro & Lewis, 1998), including both manipulators and mobile robots. A typical approach is to use neural networks for nonlinear system modelling, including for instance the learning of forward and inverse models of a plant, noise cancellation, and other forms of nonlinear control (Fierro & Lewis, 1998). An alternative approach is to solve a particular problem by designing a specialized neural network architecture and/or learning rule (Sutton & Barto, 1981). It is clear that biological brains, though exhibiting a certain degree of homogeneity, rely on many specialized circuits designed to solve particular problems. We are interested in understanding how animals are able to solve complex problems such as learning to navigate in an unknown environment, with the aim of applying what is learned of biology to the control of robots (Chang & Gaudiano, 1998; Martínez-Marín, 2007; Montes-González, Santos-Reyes & Ríos- Figueroa, 2006). In particular, this article presents a neural architecture that makes possible the integration of a kinematical adaptive neuro-controller for trajectory tracking and an obstacle avoidance adaptive neuro-controller for nonholonomic mobile robots. The kinematical adaptive neuro-controller is a real-time, unsupervised neural network that learns to control a nonholonomic mobile robot in a nonstationary environment, which is termed Self-Organization Direction Mapping Network (SODMN), and combines associative learning and Vector Associative Map (VAM) learning to generate transformations between spatial and velocity coordinates (García-Córdova, Guerrero-González & García-Marín, 2007). The transformations are learned in an unsupervised training phase, during which the robot moves as a result of randomly selected wheel velocities. The obstacle avoidance adaptive neurocontroller is a neural network that learns to control avoidance behaviours in a mobile robot based on a form of animal learning known as operant conditioning. Learning, which requires no supervision, takes place as the robot moves around a cluttered environment with obstacles. The neural network requires no knowledge of the geometry of the robot or of the quality, number, or configuration of the robot’s sensors. The efficacy of the proposed neural architecture is tested experimentally by a differentially driven mobile robot.


2002 ◽  
Vol 14 (4) ◽  
pp. 323-323
Author(s):  
Takashi Tsubouchi ◽  
◽  
Keiji Nagatani ◽  

Since the dawning of the Robotics age, mobile robots have been important objectives of research and development. Working from such aspects as locomotion mechanisms, path and motion planning algorithms, navigation, map building and localization, and system architecture, researchers are working long and hard. Despite the fact that mobile robotics has a shorter history than conventional mechanical engineering, it has already accumulated a major, innovative, and rich body of R&D work. Rapid progress in modern scientific technology had advanced to where down-sized low-cost electronic devices, especially highperformance computers, can now be built into such mobile robots. Recent trends in ever higher performance and increased downsizing have enabled those working in the field of mobile robotics to make their models increasingly intelligent, versatile, and dexterous. The down-sized computer systems implemented in mobile robots must provide high-speed calculation for complicated motion planning, real-time image processing in image recognition, and sufficient memory for storing the huge amounts of data required for environment mapping. Given the swift progress in electronic devices, new trends are now emerging in mobile robotics. This special issue on ""Modern Trends in Mobile Robotics"" provides a diverse collection of distinguished papers on modern mobile robotics research. In the area of locomotion mechanisms, Huang et al. provide an informative paper on control of a 6-legged walking robot and Fujiwara et al. contribute progressive work on the development of a practical omnidirectional cart. Given the importance of vision systems enabling robots to survey their environments, Doi et al., Tang et al., and Shimizu present papers on cutting-edge vision-based navigation. On the crucial subject of how to equip robots with intelligence, Hashimoto et al. present the latest on sensor fault detection in dead-reckoning, Miura et al. detail the probabilistic modeling of obstacle motion during mobile robot navigation, Hada et al. treat long-term mobile robot activity, and Lee et al. explore mobile robot control in intelligent space. As guest editors, we are sure readers will find these articles both informative and interesting concerning current issues and new perspectives in modern trends in mobile robotics.


2015 ◽  
Vol 27 (4) ◽  
pp. 317-317 ◽  
Author(s):  
Yoshihiro Takita ◽  
Shin’ichi Yuta ◽  
Takashi Tsubouchi ◽  
Koichi Ozaki

The first Tsukuba Challenge started in 2007 as a technological challenge for autonomous mobile robots moving around on city walkways. A task was then added involving the search for certain persons. In these and other ways, the challenge provides a test field for developing positive relationships between mobile robots and human beings. To make progress an autonomous robotic research, this special issue details and clarifies technological problems and solutions found by participants in the challenge. We sincerely thank the authors and reviewers for this chance to work with them in these important areas.


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