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.

Describes an algorithm and experimental results for detecting and isolating sensory failures for a fire detection system. In the fire system, we use two smoke sensors, two flame sensors and two temperature sensors to detect fire events. These sensors can be classified into two groups, and each group has one smoke sensor one flame sensor and one temperature sensor. The sensory failure and isolation techniques described in the paper are based on weight variation of the sensory adaptive fusion method. From the simulation and experimental implementation results, it demonstrates that the method can exactly find out which sensor is faulty and isolate it. That is to say, when a sensory failure occurs, the system can exactly locate the sensory failure.