This thesis presents a detailed design, calibration, and control of a modular and reconfigurable robot (MRR) system. A MRR system not only includes modular mechanical hardware, but also modular electrical hardware, control algorithms and software. Also, those modular components can be easily constructed into various manipulator configurations to accomplish a wider range of tasks. MRRs represent the next generation of industrial manipulators that cope with the transition from mass to customer-oriented production. The main contributions of this thesis are: 1) mechanical design and calibration of multi-input multi-output (MIMO) joint modules of MRR, and 2) control design to handle multiple configuration and overcome disturbance due to dynamics uncertainty. From the mechanical design point of view, this thesis presents two main topics: 1) each joint is not only modularly designed, but also has multiple-input multiple-output (MIMO) physical connection ports, which contributes to the concept of reconfigurability. Strictly speaking, single-input single-output (SISO) modular joint falls into the category of modular manipulator, and the robot reconfiguration is achieved by integrating different types of modules. For example, with single revolute MIMO joint module, both rotary and pivotal joint can be generated. On the other hand, if you would like to switch from rotary movement to pivotal movement with a SISO joint module, using another pivotal joint module is the only way to achieve this goal, and 2) for precise automation application, joints and links should be accurately connected and oriented when reconfigured. Our proposed modular joint has four connection ports which can be configured as either a rotary joint or a pivotal joint. In addition, key and keyway connection mechanism provides high accuracy in positioning the link onto the joint. Therefore, this structure reduces or eliminates MRRs system calibration time when reconfigured. Furthermore, zero link offset when used as a pivotal joint increases the robot dexterity, maximizes the reachability, and results in kinematics simplicity. The main challenge in the control of an MRR system with harmonic drives (HD) is the significant uncertainties due to friction, unmodelled dynamics, varying payload, gravitation, dynamic coupling between motions of joints, and the configuration changes. In order to compensate all unpredictable effects, we proposed a decentralized saturation-type robust control scheme based on direct-Lyapunov method and backstepping techniques. To better understand the system dynamics behavior, the HD flexspline compliance and friction calibration and results are also provided. The results are used for controller design. The proposed controller is verified through both computer simulation and experimental analysis.

Online computation is a model for formulating decision making under uncertainty. In an online problem, the algorithm does not know the entire input from the beginning; the input is revealed in a sequence of steps. At each step, the algorithm should make its decisions based on the past and without any knowledge about the future. Many important real-life problems such as robot navigation are intrinsically online and thus the design and analysis of online algorithms is one of the main research areas in theoretical computer science. Competitive analysis is the standard measure for analysis of online algorithms. It has been applied to many online problems in diverse areas ranging from robot navigation, to network routing, to scheduling, to online graph coloring. In this thesis, we first survey three classic online problems, namely the cow-path problem, the Processor-Allocation problem and the Robots-Search-Rays problem and highlight connections between them. Second, the main result is for the One-Robot-Searches-Two-Rays problem for which we consider the weighted scenario, in which the robot is located on a ray with a preferential probability p. We term the One-Robot-Searches-Two-Rays-And-Weighted problem as 1-STRAW (and in general k-STRAW for k searchers). In the 1-STRAW problem, we propose a search strategy which is optimal among weighted geometric states. In addition, we prove a tight lower bound of the worst case competitive ratio and conjecture a lower bound of the average case competitive ratio for the 1-STRAW problem. Additionally, we compare our search strategy and its performance with the doubling strategy and the SmartCow algorithm.

This thesis proposes and designs an autonomous transportation robot. It can provide elders and disabled people to cross the crosswalk safely with sensor information fusion for real time decision-making and control by fuzzy inference and the information of image, radar, and encoder etc. In this study, the vision system makes feedback of position offset, and declination angle through the fuzzy controller, the robot could modify the attitude error to cross the crosswalk completely. This thesis considers obstacles in the forward path. Using the vision feedback information to design the fuzzy controller, it can supply obstacle avoidance to make the transportation smoothly. Besides, we also address the robot guide at a crosswalk interface point. The detection of special purpose road for the blinds on the vision system can guide the robot to make a right turn or left turn to the next intersection. In cooperate with the intersection-agent system for collision avoidance, the robot could pass through the next crosswalk safely in order to finish the whole safely intersection system.

The main purpose of this thesis is to detect pedestrian crossing by static and dynamic image processing. This technique can help the blind and the disabled people to find the pedestrian crossing and walk through it safely. Until now there is no research about detecting pedestrian crossing in Taiwan. Therefore, this article applies the algorithm of Bipolarity feature in an image-based technique. In this thesis, the Bipolarity is regarded as the main feature in detecting pedestrian crossing. At first, it uses the features of pedestrian crossing, the black road surface is painted with constant-width periodic white stripes. After computation, the analysis and comparison in an image of intensity distribution is obtained. And the background will be eliminated. Secondly, Connected Component Labeling is used to extract the most similar region, and the marked region will be detected by the image. Finally, this thesis will detect whether the crossing exist or not in the marked region and measure the length of crossing. In dynamic model, the real-time image processing technique combines with wheelchair robot in order to walk through pedestrian crossing automatically, and image processing technique provides real-time offset and angle of displacement for the wheelchair robot to control and reach the destination. In this thesis, the image processing is in PC-base, and it receives the information by using a digital camera to record the real scene of pedestrian crossing. Keywords:pedestrian crossing , image processing, wheelchair robot

This thesis sets up terrain parameters and locomotion strategies of a hexapod robot walking on variable and discontinuous terrain. Walking on this kind of terrain is the greatest advantage of legged robots compared with wheeled robots. First, establish a randomly distributed parameterized terrain. Second, set up morphological parameters and dimension parameters of the robot. Third, build kinematic model and generate continuous gaits of the robot, including crab gaits and turning gaits. The locomotion strategy can determine every AEP ,PEP and stride depending on terrain. Finally, verify the strategy through computer programming and find shorter path by calculating if foothold is available in advance. Because of applying randomly distributed parameterized terrain, in addition to describing the terrain more comprehensively, the terrain parameters can be adjusted easily according to different needs. This research will bring about more applications and developments of legged robots.

The objective of the thesis is utilizing body sensing technology to develop a more intuitive and convenient way to control robots. The idea is to build a body sensing control system based on Kinect framework. Through Kinect, users from different age groups can achieve the desired purposes through motion demonstration without complicated programming. The system can accurately calculate angle change from users¡¦ gestures in a motion and identify key-postures which can compose an emulation motion similar to the presenting one. In other words, from analyzing these key postures, the demonstrated behaviors are able to be represented internally. Therefore, the system, consisting of a kinematics computational module and a representation algorithm, not only provides the function of behavior emulation but also behavior representation. By representation algorithm, the system extracts the features of combined behaviors. Besides, with the modular programming methodology, different behaviors can be reorganized to generate new behaviors based on the set of key poses represented by the extracted features. The application implemented in this system is within the OpenNI and NITE environment. OpenNI is used to retrieve information that the Kinect captured. NITE is used to track the user skeleton. The system is demonstrated by a play of ¡§Tai-Ji-Advancer¡¨ and at http://www.youtube.com/watch?v=cSYS49JKVAA.

Constructing and maintaining a formation is critical in applications of cooperative control of multi-agent systems. In this research we address the formation control problem of generating a formation for a group of nonholonomic mobile agents. The formation control scheme proposed in this work is based on a fusion of leader-follower and virtual reference approaches. This scheme gives a formation constraint representation that is independent of the number of agents in the formation and the resulting control algorithm is scalable. One of the important desired features in controller design is that the formation errors defined by formation constraints should be stabilized globally and exponentially by the controller. The proposed controller is based on feedback linearization, and formation errors are shown to be globally exponentially stable in the sense of Lyapunov. Since formation errors are stabilized globally, the proposed controller is applicable to both formation keeping and formation construction problems. As a possible application, the proposed algorithm is implemented in a cooperative ground moving target surveillance scenario. The proposed algorithm enables the determination of the minimal number of agents required for surveillance of a moving target. The number of agents returned by this scheme is not optimal and hence is a conservative solution. However, this is justified by the computational savings the scheme offers.

Internet has become the most extensible media of data exchange and communication in the world because computer science and technology are more and more popular. The Geographic Information Systems¡]GIS¡^are also developed on the Internet. However, using existing mechanism of data searching on the Internet cannot search data in Web GIS. We can only browse data but not access. This situation makes Web GIS as an isolated island. Users fail to know where and what kinds of data are provided, and these data also cannot be shared. The most important objective of the research is to build an effective mechanism of searching and clearinghouse on the Internet. This mechanism can help computer overcome difficulties in reading and understanding geographic data that are composed of maps and images, and then geographic data can be searched and shared easily as text data. The research will try to create metadata by XML that are complied with FGDC standard. By using two of the XML characteristics, i.e. creating tags and describing data, the computer can retrieve information automatically from metadata on the Internet. Lastly, the geographic search engine and clearinghouse that the research built will collect and integrate geographic metadata to systematically facilitate users finding geographic data they need through Internet, and achieve the objectives of geographic data search and clearinghouse on the Internet.

The locomotion robots have wheeled, biped, quadruped and so on. Walking robot may not move faster or more popular than wheeled robot. But walking robot is a good assistant to pass through the rough roadway and to explore unknown landforms. The advantages of walking robot have: mobility, walking in danger environment, across obstacles, up stairs and down stairs and nimbleness. These difficulties environment are the obstacles for the wheeled robot. Today, many robots are designed to make up for human body and ability. Application in explore the outer space, to relieve the victims of a disaster, to move work, to offer greater convenience to the people, housekeeper, to substitute for handicapped limbs and so on. In the thesis, we analyze the gait of biped robot. Biped robot arrive a destination rapidly in the limit environment. Biped can use turning motion gait to bypass obstacles. We purpose to maintain motion velocity of biped robot and come out the speed and stride distance of the biped robot. The studies have: biped robot turning design, planning humanoid motion gait and experiment motion gait.

This thesis presents an imitation-based methodology, also a simple and easy way, for a service robot to learn the behaviors demonstrated by the user. With this proposed method, a robot can learn human behavior through observation. Inspired by the concept of biological learning, this learning model is initiated when facing a new learning event. A series of experiments are conducted to use a humanoid robot as a platform to implement the proposed algorithm. Discussions are made of how the robot generates a complete behavior sequences performed by its demonstrator. Because it is time consuming for a robot to go through the whole process of learning, we thus propose a decomposed learning method to enhance the learning performance, that is, based on the past learning information, the robot can skip learning again the behaviors already known. For simple robot behaviors, a hierarchical evolutionary mechanism is developed to evolve the complete behavior trajectories. For complex behaviors sequences, different ways are used to tackle the scalability problem, including decomposing the overall task into several sub-tasks, exploiting behavior information recorded previously, and constructing a new strategy to maintain population diversity. To verify our approach, a different series of experiments have been conducted. The results show that our imitation-based approach is a natural way to teach the robot new behaviors. This evolutionary mechanism successfully enables a humanoid robot to perform the behavior sequences it learns.