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.

Motion planning plays an important role in the field of autonomous mobile robots. We propose a hybrid intelligent system including the object-oriented knowledge base and adaptive motion planning (AMP) algorithm. There are four major procedures in the intelligent system: a priori environment building, on-line topology map generation, candidate path searching, and behavior commands generation. The adaptive motion planning (AMP) algorithm acquires the a priori defined map and the latest or dynamic information. The algorithm then determines path and generates a sequence of motion behavior description commands for navigation. The experimental results indicate that the object-oriented knowledge base can be updated easily. The AMP algorithm can produce the suitable path and behavior commands, and fuse these behaviors to navigate the mobile robot in the dynamic environment. The proposed method is implemented on the "Chung Cheng I" autonomous mobile robot to demonstrate the reliability and flexibility.