Ren C. Luo

Multisensor integration and fusion in intelligent systems

The issues involved in integrating multiple sensors into the operation of a system are presented in the context of the type of information these sensors can uniquely provide. A survey is provided of the variety of approaches to the problem of multisensor integration and fusion that have appeared in the literature in recent years ranging from general paradigms, frameworks, and methods for integrating and fusing multisensory information to existing multisensor systems used in different areas of application. General multisensor fusion methods, sensor selection strategies, and world models are examined, along with approaches to the integration and fusion of information from combinations of different types of sensors. Short descriptions of the role of multisensor integration and fusion in the operation of a number of existing mobile robots are provided, together with proposed high-level multisensory representations suitable for mobile robot navigation and control. Existing multisensor systems for industrial and other applications are considered. >

Multisensor fusion and integration: approaches, applications, and future research directions

Multisensor fusion and integration is a rapidly evolving research area and requires interdisciplinary knowledge in control theory, signal processing, artificial intelligence, probability and statistics, etc. The advantages gained through the use of redundant, complementary, or more timely information in a system can provide more reliable and accurate information. This paper provides an overview of current sensor technologies and describes the paradigm of multisensor fusion and integration as well as fusion techniques at different fusion levels. Applications of multisensor fusion in robotics, biomedical system, equipment monitoring, remote sensing, and transportation system are also discussed. Finally, future research directions of multisensor fusion technology including microsensors, smart sensors, and adaptive fusion techniques are presented.

Development of a multi-behavior based mobile robot for remote supervisory control through the Internet

We review the networked mobile robot systems and suggest taxonomy based on the three levels of control commands. The performance analysis result shows that direct control has potential difficulty for implementation due to the unpredicted transmission delay of the network. To tackle this problem, we have suggested the behavior-programming control concept to avoid disturbances of the Internet latency. For this purpose, primitive local intelligence of the mobile robot is grouped into motion planner, motion executor, and motion assistant, where each of a group is treated as an agent. They are integrated by centralized control architecture based on multi-agent concept, communicated through a center information memory. The event-driven concept is applied on the robot to switch the behaviors to accommodate the unpredicted mission autonomously. We have successfully demonstrated experimentally the feasibility and reliability for system through a performance comparison with direct remote control.

Mobile Sensor Node Deployment and Asynchronous Power Management for Wireless Sensor Networks

Mobile sensor node deployment and power management are important issues in the wireless sensor network system. This study designs a mobile sensor node platform to achieve a highly accurate localization mechanism by using ultrasonic, dead reckoning, and radio frequency information which is processed through a particle filter algorithm. Mobile sensor node with accurate localization ability is of great interest to basic research works and applications, such as sensor deployment, coverage management, dynamic power management, etc. In this paper, we propose an efficient mobile sensor node deployment method, grid deployment, where the map is divided into multiple individual grids and the weight of each grid is determined by environmental factors such as predeployed nodes, boundaries, and obstacles. The grid with minimum values is the goal of the mobile node. We also design an asynchronous power management strategy in our sensor node to reduce power consumption of the sensor network. Several factors such as probability of event generation, battery status, coverage issues, and communication situations have also been taken into consideration. In network communication, we propose an asynchronous awakening scheme so that each node is free to switch on or off its components according to observed event statistics and make a tradeoff between communication and power consumption. The deepest sleep state period is determined by the residual power. By combining these methods, the power consumption of the sensor node can be reduced.

Networked intelligent robots through the Internet: issues and opportunities

Intelligent robotic systems have been extensively applied in factory automation, space exploration, intelligent buildings, surgery, military service, and also in our daily life. Various remote control methods have been performed for intelligent robotic systems, such as radio, microwave, computer networks, etc. Nowadays, the computer network services have broadly used in our daily life, such as FTP, Telnet, the World Wide Web, e-mail, etc. Consequently, it is very convenient to use the Internet to control intelligent robot, and the users will increase in the future. In the past few years, many researchers have been using the Internet as a command transmission medium which can control the intelligent robot and obtain feedback signals. Although the Internet has many advantages in a variety of fields, using the Internet to control intelligent robots also has some limitations, such as the uncertain time-delay problem, the uncertain data-loss problem, and the data-transmission security problem. In the literature, many experts proposed various methods to solve these problems. This paper will discuss these methods and analyze the effects on the remote control systems caused by these problems. The intelligent robot can simultaneously present low-level navigational capabilities, medium-level self-positioning capabilities, high-level motion-planning capabilities, and the ability to be controlled through the Internet. The issues for controlling intelligent robots through the Internet will be discussed in terms of direct control, behavior programming control, supervisory control, and learning control. Finally, we enumerate some opportunities for the application of network-based intelligent robots, and present some successful examples of networked intelligent robots in our laboratory. Future trends and concluding remarks appear at the end of this paper.

Design and implementation of capacitive proximity sensor using microelectromechanical systems technology

This paper presents an innovative proximity sensor using microelectromechanical systems (MEMS) technology. The proximity sensor works on the principle of fringe capacitance. The target object does not need to be part of the measuring system and could be either a conductor or nonconductor. Modeling of the proximity sensor is performed and closed-form analytical solution is obtained for a ring-shaped sensing pattern. The proximity sensors could be batch fabricated using MEMS technology, and the fabrication process is relatively simple. Measurement of the prototype sensors revealed promising results. The size of the proximity sensor could vary from a few hundred micrometers to the size of the substrate. The flexibility on sensor size, sensing patterns, and sensing pattern geometrical parameters makes the sensor very versatile and capable of precision measurement of proximity in the range from micrometers to centimeters. The small size of the sensor makes it possible to surface mount the sensor in many space-constrained places. This advantage is vital in many areas, such as MEMS, microrobotics, precision engineering, machine automation, inspection tools, and many other applications. The ability of the proximity sensor in measuring relative permittivity of materials also finds the sensor useful applications in biomedical and tissue engineering. In addition, this micro proximity sensor is an ideal building block for many other types of sensors, such as force, tactile, and flow sensors.

A tutorial on multisensor integration and fusion

A tutorial introduction to the subject of multisensor integration and fusion is presented. The role of multisensor integration and fusion in the operation of intelligent systems is defined in terms of the unique type of information multiple sensors can provide. Multisensor integration is discussed in terms of basic integration functions and multisensor fusion in terms of the different levels at which fusion can take place. Numerical examples are given to illustrate a variety of different fusion methods. Speculations concerning possible research future directions and a guide to survey and review papers in the area of multisensor integration and fusion are presented.<<ETX>>

Multisensor Fusion and Integration: Theories, Applications, and its Perspectives

The decision-making processes in an autonomous mechatronic system rely on data coming from multiple sensors. An optimal fusion of information from distributed multiple sensors requires robust fusion approaches. The science of multisensor fusion and integration (MFI) is formed to treat the information merging requirements. MFI aims to provide the system a more accurate perception enabling an optimal decision to be made. The wide application spectrum of MFI in mechatronic systems includes industrial automation, the development of intelligent robots, military applications, biomedical applications, and microelectromechanical systems (MEMS)/nanoelectromechanical systems (NEMS). This paper reviews the theories and approaches of MFI with its applications. Furthermore, sensor fusion methods at different levels, namely, estimation methods, classification methods and inference methods, are the most frequently used algorithms. Future perspectives of MFI deployment are included in the concluding remarks.

Target tracking using a hierarchical grey-fuzzy motion decision-making method

This paper presents a hierarchical grey-fuzzy motion decision-making (HGFMD) algorithm, which is capable of integrating multiple sequential data for decision making and for the design of the control kernel of the target tracking system. The algorithm combines multiple grey prediction modules, each of which can estimate a suitable model from sequential sensory information for approximating the observed dynamic system for future-trend prediction and for decision making through a multilayer fuzzy logic inference engine. We have designed the HGFMD controller for a target tracking system and implemented it in our autonomous mobile robot. The HGFMD is compared with the conventional fuzzy logic controller, multilayer fuzzy controller, and the original grey-fuzzy controller developed previously in various target-tracking experiments. We demonstrated the high reliability of the HGFMD controller and tracking system even when encountering the uncertain status of slow sensory response time and the nonlinear motion behaviors of the target.

Desktop rapid prototyping system with supervisory control and monitoring through Internet

Rapid prototyping (RP) can enhance design and manufacturing productivity by taking advantage of the Internet. Via the Internet, most small and medium size companies should be able to share the access of the RP machine remotely without owning the expensive machines. This study combines RP preprocessing, RP machine, and the Internet into a telecontrolled manufacturing system. Also presented is the software and hardware for a new LCD-based photosensitive resin RP machine that uses only visible light. This visible light can expose and solidify an entire layer at once, layer-by-layer, until the whole part is finished. The user sends a three-dimensional (3-D) CAD model (STL file) via the Internet to a telecontrol server, which transforms the CAD model into a RP machine LCD photomask display. The user can then direct the RP machine to build the RP part while watching a live image of the part via the World Wide Web. An online visual system allows inspection of the RP part quality during manufacturing. Part building is monitored by a pattern matching algorithm which compares a grabbed image with the photomask. If the grabbed image is not adequately similar to the photomask, the program stops manufacture and notifies the user. The experimental results show that RP using the Internet is promised, but the surface roughness should be further improved.