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.

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 them can online estimate the suitable model from sequential sensory information to approximate the observed dynamic system model for future-trend prediction, and for decision making through a multilayered fuzzy logic inference engine. We designed the HGFMD controller for target tracking system and implemented in our autonomous mobile robot. The HGFMD is compared with the conventional fuzzy logic controller, multilayered fuzzy controller, and the original grey-fuzzy controller we have developed previously in various target-tracking experiments. The results obtained show the high reliability of the HGFMD controller and tracking system.

Target tracking is useful in many robotics applications, such as assembly, autonomous navigation, etc. Conventional position based tracking strategies usually define the dynamics models for either motion control on tracker or position prediction on target, making the system inflexible and difficult to implement. This paper presents a new target tracking strategy, in which the dynamics model parameters are online updated according to the limited observation of the target and tracker relation. The grey theory is used in the position prediction of the target, which predicts the future trend based on the previous sensory measurements. We also use the look-ahead fuzzy logic for motion control of the tracker. Experimental results for tracking different moving targets are accomplished by the "Chung Cheng-1" mobile robot used in our laboratory. The results demonstrate the robustness and flexibility of the system.

Many potential accidents due to direct control of electrical wheelchair may be occurred by unreasonable control in various internal or external environmental conditions, such as the uncertainties of the front caster wheels and the unbalanced friction condition between wheels and floor. This paper presents a grey-fuzzy decision-making (GFD) algorithm based on grey prediction theory and fuzzy logic theory. The GFD is able to estimate optimal parameters approximating to the real system dynamics mode according to the observed information. The application of intelligent assistant control of an electrical wheelchair investigated. We conduct the experiments by multisensor based wheelchairs "Luoson#1" and "Luoson#3". The GFD controller is presented to integrate the electronic compass and dead reckoning measurements for the reduction of the influence caused by the uncertainties. The results have demonstrated the system capability for the increased certainty and safety to operate the electrical wheelchairs.

The development of intelligent wheelchairs can bring a good solution to assist severely handicapped people who are unable to operate the classical electrical wheelchair by themselves in their daily activities. In this paper we have developed a new concept on automatic guided intelligent wheelchair system, through which the wheelchair users can extend their activity area from a priori known environment as many autonomous approaches addressed. We describe a hierarchical look-ahead grey-fuzzy decision motion algorithm for the tracking control of the wheelchair to follow a guiding service robot in an unknown environment. We have developed a multisensor based intelligent wheelchair prototype called Luoson III to work with the previous built autonomous guiding service robot Chung-Cheng 1. The experimental results have demonstrated the success for the motion decision algorithm and the whole concept.

A force reflective feedback control system for the operation of intelligent wheelchairs is presented. A multisensor based differential-driver intelligent wheelchair "Luoson III" has been developed to conduct the experiments. We have used a Microsoft Force Feedback Pro joystick which has the force feedback and programmable capability. The joystick not only can receive the use input but also can simulate the effects of wall, shake, constant force and reflect back to the user. We have demonstrated the success of this system through the prototype wheelchair operated in an open office environment.

A multiagent with event driven obstacle avoidance system (MEDOAS) has been developed, which is capable of avoiding stationary and moving obstacles and achieve the goal position. The multiagent design enables the mobile robot to perform sensing, steering and driving control in parallel. Event driven motion control of the system enables the robot to cope with various difficulties of the given assignment in a dynamic changing task environment. The MEDOAS is implemented by multi-thread programming on a time sharing operating system running on the main control system on our mobile robot system in the laboratory. The performance of MEDOAS is experimentally tested on the mobile robot. Experimental results demonstrate the resultant MEDOAS system capable of navigating a mobile robot through the cluttered environment efficiently.

The components for providing autonomous capabilities of a mobile robot are grouped into few basic modules, namely motion planner, motion executor, motion assistant, and behavior arbitrator. The primitive motion executors such as obstacle avoidance, goal following, wall following, docking, and path tracking for mobile robot navigation are developed in this paper. They are integrated with motion planner, motion assistants, and behavior arbitrator together based on decentralized control architecture with a hierarchical shared information memory. Event driven concept is used on switching the motion behaviors. The resultant intelligence for mobile robot navigation is capable of efficiently performing motion behavior and detecting environmental event in parallel to adapt dynamically changed environment. It also allows the human to program the motion behaviors in high level to complete a task. Experimental results on a real mobile robot have demonstrated the robustness of the motion executors and the overall system.

Obstacle avoidance and other functional behaviors for autonomous mobile robot navigation usually play an independent role in a robot system. The cooperation between two or more functional behavior modules is loose relatively. As the navigation system becomes more complicated, the codes for various functional behaviors may become immense and redundant. In this paper, we propose a multiagent with event driven obstacle avoidance system (MEDOAS) for mobile robot navigation. The MEDOAS decomposes a behavior module, such as obstacle avoidance, into several primitive processing agents. The agent cooperates with others by communicating through a shared information memory. A central controller switches the agent activity by event driving rules. Based on MEDOAS more behavior modules can be achieved by adding new primitive agent to cooperate with the existing agents. Experimental results demonstrate the MEDOAS system capable of navigating a mobile robot through the cluttered environment efficiently.

A remote supervisory control architecture which combines computer network and an autonomous mobile robot is constructed. The main feature of this architecture is that users just need a general purpose computer and a World Wide Web browser, they can command the mobile robot in a remote location through Internet and Home Page. Hardware configuration of this architecture includes a mobile robot, as server workstation, and other computers, each of them is able to communicate with others via computer network. Software design includes assignment of the server, communication between the server and robot, and the user interface. The architecture has extensive capabilities for teleoperation, telerobotics operations, and other multimedia services. Based on this architecture we have implemented multilevel operations for the user on various remote applications. At present, the system allows users to operate our mobile robot via the Internet and a friendly user interface (via our Home Page) using supervisory and direct control modules and they can receive live images captured by a CCD camera which is mounted on the robot and other data from sensor measurement.