This paper proposes genetic algorithms (GAs) for path planning and trajectory planning of an autonomous mobile robot. Our GA-based approach has an advantage of adaptivity such that the GAs work even if an environment is time-varying or unknown. Therefore, it is suitable for both off-line and on-line motion planning. We first presents a GA for path planning in a 2D terrain. Simulation results on the performance and adaptivity of the GA on randomly generated terrains are shown. Then, we discuss an extension of the GA for solving both path planning and trajectory planning simultaneously.

A communication system which attains an efficient amalgamation of base station (BS) type cellular communication and non-BS type direct communication for mobile terminals (MTs), "cellularad-hoc united communication system," is proposed. In this system, whether a cellular system or an ad-hoc communication system is chosen by distance and whether the condition is good or not between communicating MTs. In other words, when two terminals are relatively near each other and are in sufficient conditions to communicate directly, they select the ad-hoc communication, while for other cases they use the cellular system. The proposed system was confirmed to have effective channel usage and low battery consumption from the simulation results. It can be also said that this system is suited especially for MTs which tend to communicate with closer partners such as operational mobile robots and autonomous carrier vehicles.

Presently, mobile robots are navigated by means of a number of methods, using navigating systems such as the sonar-sensing system or the visual-sensing system. These systems each have their strengths and weaknesses. For example, although the visual system enables a rich input of data from the surrounding environment, allowing an accurate perception of the area, processing of the images invariably takes time. The sonar system, on the other hand, though quicker in response, is limited in terms of quality, accuracy and range of data. Therefore, any navigation methods that involves only any one system as the primary source for navigation, will result in the incompetency of the robot to navigate efficiently in a foreign, slightly-more-complicated-than-usual surrounding. Of course, this is not acceptable if robots are to work harmoniously with humans in a normal office/laboratory environment. Thus, to fully utilise the strengths of both the sonar and visual sensing systems, this paper proposes a fusion of navigating methods involving both the sonar and visual systems as primary sources to produce a fast, efficient and reliable obstacle-avoiding and navigating system. Furthermore, to further enhance a better perception of the surroundings and to improve the navigation capabilities of the mobile robot, active sensing modules are also included. The result is an active sensor fusion system for the collision avoiding behaviour of mobile robots. This behaviour can then be incorporated into other purposive behaviours (eg. Goal Seeking, Path Finding, etc. ). The validity of this system is also shown in real robot experiments.

In this paper, we propose the distributed stable marriage problem and apply it to planning for cooperative works of autonomous mobile robots and battery charger stations. We develop and analyze a distributed algorithm to determine the partner by message communication.

We present a navigation system using ultrasonic sensors for unknown and dynamic indoor environments. To achieve the robustness and flexibility of the mobile robot, we develop a behavior-based system architecture, consisting of multi-layered behaviors. Basic behaviors required for the navigation of a mobile robot, such as, avoiding obstacles, moving towards free space, and following targets, are redundantly developed as agents and combined in a behavior-based system architecture. An extended potential filed method is developed to produce the appropriate velocity and steering commands for the behaviors of the robot. We demonstrate the capabilities of our system through real world experiments in unstructured dynamic office environments using an indoor mobile robot.