This dissertation discusses an implementation of a design, control and motion planning for a novel extendable modular redundant robotic manipulator in space constraints, which robots may encounter for completing required tasks in small and constrained environment. The design intent is to facilitate the movement of the proposed robotic manipulator in constrained environments, such as rubble piles. The proposed robotic manipulator with multi Degree of Freedom (m-DOF) links is capable of elongating by 25% of its nominal length. In this context, a design optimization problem with multiple objectives is also considered. In order to identify the benefits of the proposed design strategy, the reachable workspace of the proposed manipulator is compared with that of the Jet Propulsion Laboratory (JPL) serpentine robot. The simulation results show that the proposed manipulator has a relatively efficient reachable workspace, needed in constrained environments. The singularity and manipulability of the designed manipulator are investigated. In this study, we investigate the number of links that produces the optimal design architecture of the proposed robotic manipulator. The total number of links decided by a design optimization can be useful distinction in practice. Also, we have considered a novel robust bio-inspired Sliding Mode Control (SMC) to achieve favorable tracking performance for a class of robotic manipulators with uncertainties. To eliminate the chattering problem of the conventional sliding mode control, we apply the Brain Emotional Learning Based Intelligent Control (BELBIC) to adaptively adjust the control input law in sliding mode control. The on-line computed parameters achieve favorable system robustness in process of parameter uncertainties and external disturbances. The simulation results demonstrate that our control strategy is effective in tracking high speed trajectories with less chattering, as compared to the conventional sliding mode control. The learning process of BLS is shown to enhance the performance of a new robust controller. Lastly, we consider the potential field methodology to generate a desired trajectory in small and constrained environments. Also, Obstacle Collision Avoidance (OCA) is applied to obtain an inverse kinematic solution of a redundant robotic manipulator.

This thesis is concerned with input-output stability theory. Within this framework, it is of interest how inputs map to outputs through an operator that represents a system to be controlled or the controller itself. The Small Gain, Passivity, and Conic Sector Stability Theorems can be used to assess the stability of a negative feedback interconnection involving two systems that each have specific input-output properties. Our first contribution concerns characterization of the input-output properties of linear time-varying (LTV) systems. We present various theorems that ensure that a LTV system has finite gain, is passive, or is conic. We also consider the stability of various negative feedback interconnections. Motivated by the robust nature of passivity-based control, we consider how to overcome passivity violations. This investigation leads to the hybrid conic systems framework whereby systems are described in terms of multiple conic bounds over different operating ranges. A special case relevant to systems that experience a passivity violation is the hybrid passive/finite gain framework. Sufficient conditions are derived that ensure the negative feedback interconnection of two hybrid conic systems is stable. The input-output properties of gain-scheduled systems are also investigated. We show that a gain-scheduled system composed of conic subsystems has conic bounds as well. Using the conic bounds of the subsystems along with the scheduling signal properties, the overall conic bounds of the gain-scheduled system can be calculated. We also show that when hybrid very strictly passive/finite gain (VSP/finite gain) subsystems are gain-scheduled, the overall map is also hybrid VSP/finite gain. Being concerned with the control of aerospace systems, we use the theory developed in this thesis to control two interesting plants. We consider passivity-based control of a spacecraft endowed with magnetic torque rods and reaction wheels. In particular, we synthesize a LTV input strictly passive controller. Using hybrid theory we control single- and two-link flexible manipulators. We present two controller synthesis schemes, each of which employs numerical optimization techniques and attempts to have the hybrid VSP/finite gain controllers mimic a H2 controller. One of our synthesis methods uses the Generalized Kalman-Yakubovich-Popov Lemma, thus realizing a convex optimization problem.

This thesis is concerned with input-output stability theory. Within this framework, it is of interest how inputs map to outputs through an operator that represents a system to be controlled or the controller itself. The Small Gain, Passivity, and Conic Sector Stability Theorems can be used to assess the stability of a negative feedback interconnection involving two systems that each have specific input-output properties. Our first contribution concerns characterization of the input-output properties of linear time-varying (LTV) systems. We present various theorems that ensure that a LTV system has finite gain, is passive, or is conic. We also consider the stability of various negative feedback interconnections. Motivated by the robust nature of passivity-based control, we consider how to overcome passivity violations. This investigation leads to the hybrid conic systems framework whereby systems are described in terms of multiple conic bounds over different operating ranges. A special case relevant to systems that experience a passivity violation is the hybrid passive/finite gain framework. Sufficient conditions are derived that ensure the negative feedback interconnection of two hybrid conic systems is stable. The input-output properties of gain-scheduled systems are also investigated. We show that a gain-scheduled system composed of conic subsystems has conic bounds as well. Using the conic bounds of the subsystems along with the scheduling signal properties, the overall conic bounds of the gain-scheduled system can be calculated. We also show that when hybrid very strictly passive/finite gain (VSP/finite gain) subsystems are gain-scheduled, the overall map is also hybrid VSP/finite gain. Being concerned with the control of aerospace systems, we use the theory developed in this thesis to control two interesting plants. We consider passivity-based control of a spacecraft endowed with magnetic torque rods and reaction wheels. In particular, we synthesize a LTV input strictly passive controller. Using hybrid theory we control single- and two-link flexible manipulators. We present two controller synthesis schemes, each of which employs numerical optimization techniques and attempts to have the hybrid VSP/finite gain controllers mimic a H2 controller. One of our synthesis methods uses the Generalized Kalman-Yakubovich-Popov Lemma, thus realizing a convex optimization problem.

The objective of this research effort is to provide a methodology for assessing the effectiveness of sampling techniques used to gather different types of geo-physical information by a robotic agent. We focus on assessing how well unique real-time sampling strategies acquire information that is, otherwise, too dangerous or costly to collect by human scientists. Traditional sampling strategies and informed search tech- niques provide the underlying structure for a navigating robotic surveyor whose goal is to collect samples that yield an accurate representation of the measured phenomena under realistic constraints. These sampling strategies are alternative improvements that provide greater information gain than current sampling technology allows. The contributions of this work include the following: 1) A method for estimating spa- tially distributed phenomena, using a partial sample set of information, that shows improvement over that of a more traditional estimation method. 2) A method for sampling this phenomena in the form of a navigation scheme for a mobile robotic survey system. 3) A method of ranking and comparing different navigation algorithms relative to one another based on performance (reconstruction error) and resource (distance) constraints. We introduce a specific class of navigation algorithms as example sampling strategies to demonstrate how our methodology allows different robot navigation options to be contrasted and the most practical strategy selected.

A robotic microscope was designed using a microcontroller to take time-lapse digital photographs of developing salamander embryos. The microcontroller operated three stepper motors to control three-axis movement accurately, and two six mega-pixel digital cameras to capture through-focus time-lapse digital pictures of six views of Ambystoma mexicanum embryos (axolotl, a salamander). The device is designed to take images every five minutes for 80 hours of early development, from fertilization to stage 20, when the neural tube closes to form the brain and spinal column. Techniques to enhance the embryo images were investigated including image fusion to get in-focus views from a stack of images. In the early embryo surface epithelial cells differentiate to form neural tissue and external skin tissue. Observing the whole embryo surface at cellular level will give a better idea of the stress and strain each cell undergoes and what physical forces are involved in cell differentiation. / May 2007

The aim of this study is to investigate the critical design and development issues for educational robotics training camps. More specifically, the purpose of the study is to explore and describe critical design issues for educational robotics training camps, illustrating how each factor affects robotic camps and enlightening how these factors should be implemented for the design of a robotic training camp. For this purpose, two robotic training camps were organized with elementary school students. Thirty children attended the first camp and twenty two children attended to the second one. The research design was qualitative in nature, more specifically / multiple-case design approach was used. Interviews with children and instructors, observations, field notes, and camp evaluation forms were the data collection methods. The data were analyzed through the qualitative data analysis techniques. The data were categorized under emerged themes, learning outcomes, evaluation of the camps&rsquo / components, career, group issues, competition, coaching, technical issues, challenges and camp duration. Some prominent findings of the study are / the instruction strategy for a robotics camp should be designed from simple to complex. The most effective and liked part of the camps are project studies, therefore projects studies should be encouraged at robotic camps. Robotics training camps should give chance to practice to the children what they have learned at schools. The group size should be arranged that every child in the group should have duties at any time. The study was concluded with a robotics camp design guideline and a sample robotic training camp curriculum.

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The applications of robotics are changing. Just as computers evolved from the realm of research and extreme novelty tools to now becoming essential components of modern life, robotics is also making a similar transition. With the changes in applications come changes in the user base of robotics. These users will span a broad range of society, but there are some key properties that can be used to characterize them. First they, more often than not, will not be the designers of the robots. Second, they will not have robot control as their primary task while operating the robot. Third, they will not have the resources or the desire to provide all the training that the robot will require, yet they will have the need to fine tune robot performance to their specific needs. Fourth, they will want to use multiple modes of interaction to make the robot accomplish the primary task. Fifth, they will expect and demand that the robot remain safe at all times (safe to humans, pets, or personal property) and expect the robot to be a readily replaceable appliance (cheap). Sixth, they will expect that the robot will be intelligent, at least in the confines of the task at hand. These are some of the key properties that will exist for the new user base. To address some of the needs that will arise because of these properties, we propose work that enables behavior transfer from teacher to robotic student that is facilitated through observation and interaction. Many users in the projected user base will not have exposure to the technologies that enable robotic operation. These users will however have some degree of understanding of how they would like the robot to provide assistance in accomplishing the task. The goal of this work is specifically to enable the user to transfer this understanding to the robot, and have the robot acquire this understanding via interactive learning. To make interactive learning possible via interaction we believe that the robot will have to be able to perform some degree of self regulation. Further, since it is assumed that the user will not have access to the robot's internal machinations, the robot will also have to be able to properly manage the knowledge it acquires over time and to verify and validate its understanding periodically. Scaffolding, a method in which teachers provide support while the student learns to master portions of a task, is likely to be the primary method to facilitate this process. This research will undertake study of coherence and its relevance to learning by observation. It will also implement the components that would enable a robot to learn to perform a small set of tasks and demonstrate them in various settings. For this work a robot will be defined as a hardware platform upon which a software agent operates. It is our desire that this software agent will be equipped to operate on any platform and learn any task that a human could perform with the same resources.