To keep autonomous mobile robot in continuously working condition, power supply is an important issue. Typically, rechargeable batteries may provide few hours of peak usage. Recharging is necessary before the power of the batteries has exhausted. In this paper, the docking station with an auto-recharging device and robot docking mechanism are implemented. We propose a docking control strategy and automatic recharging device for the security robot recharging. We also propose a power prediction algorithm to determine when the robot needs to navigate back to docking station for recharging. An artificial landmark is detected and recognized by the proposed image processing system. Then the geometrical relationship between the robot and the docking station (the depth and orientation) is estimated. The robot will move directly right in front of the docking station. Finally, the robot approaches to the docking station based on the proposed virtual spring model. In this model, we assume that the robot and the docking station are connected by a virtual spring. The compliant forces act both in the direction of the translation deformation and bending. The motion control parameters, which are velocities of the two wheels, can be derived from the model. Experimental results show that the proposed methods successfully perform the docking and automatic recharging.

The security of home, laboratory, office and factory is essential to human daily life. A danger event is often caused by the negligence of humans. Potential hazards may injure our life. Therefore, it motivates us to develop an intelligent multi-sensor based Security Robot system. It is expected to be widely employed in our daily life. Security robot can detect dangerous situation and provide timely alert us. The structure of the security robot contains eight parts. Including remote surveillance and control system, image system, obstacle avoidance system, software system, auto-dialing, driver system, sensor system and motion planning system. In this paper, we discuss the opportunity to use multi-agent technology in the sensory system and the expected improvements. The sensory system has seven-variety detection and diagnosis agent (local agent) and one sensor agent (auxiliary agent). Finally, we use multi-processor architecture to implement the multi-agent based sensory system for the security robot application.

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.

The objective of the paper is to develop multilevel multisensor based decision fusion principles for an intelligent animal robot. The paper presents the design and implementation of a four-legged animal robot. The animal robot's head, body, and legs were constructed using aluminum material. The control/decision unit, driver unit and sensory unit were designed for the control of the animal robot. In addition, we use hierarchically organized control hardware and multilevel multisensor based fusion techniques to obtain a fused decision. Weight function and rule based algorithms are employed to fuse and integrate multisensor data. Preliminary experimental results indicate that the proposed methods can effectively fuse decisions for the animal robot.

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.