by Lam Pak Chio. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 101-105). / Abstract also in Chinese. / Abstract --- p.ii / Acknowledgements --- p.v / Contents / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Problem Definition and Approach --- p.3 / Chapter 1.3 --- Outline --- p.5 / Chapter 2 --- Algorithm Outline --- p.7 / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Assumptions --- p.7 / Chapter 2.3 --- Object Model --- p.8 / Chapter 2.4 --- Hand Model --- p.9 / Chapter 2.5 --- Measurement Data --- p.11 / Chapter 2.6 --- Algorithm Outline --- p.12 / Chapter 3 --- Calculation of Contact States --- p.14 / Chapter 3.1 --- Introduction --- p.14 / Chapter 3.2 --- Problem Analysis --- p.15 / Chapter 3.3 --- Details of Algorithm --- p.17 / Chapter 3.3.1 --- Calculation of Contact Points --- p.18 / Chapter 3.3.2 --- Calculation of Object Position and Orientation --- p.26 / Chapter 3.3.2.1 --- The Object Orientation --- p.26 / Chapter 3.3.2.2 --- The Object Position --- p.28 / Chapter 3.3.3 --- Contact Points on Other Fingers --- p.32 / Chapter 4 --- Calculation of Contact Motion --- p.34 / Chapter 4.1 --- Introduction --- p.34 / Chapter 4.2 --- Search-tree --- p.34 / Chapter 4.3 --- Cost Function --- p.36 / Chapter 4.4 --- Details of Algorithm --- p.37 / Chapter 4.4.1 --- Calculation of the Next Instant Contact States --- p.39 / Chapter 4.4.1.1 --- Contact Region Estimation --- p.41 / Chapter 4.4.1.2 --- Contact Point Calculation --- p.45 / Chapter 4.4.1.3 --- Object Position and Orientation Calculation --- p.48 / Chapter 4.4.1.4 --- Contact Motion Calculation --- p.50 / Chapter 5 --- Implementation --- p.56 / Chapter 5.1 --- Introduction --- p.56 / Chapter 5.2 --- Architecture of Friendly Teaching System --- p.56 / Chapter 5.2.1 --- CyberGlove --- p.57 / Chapter 5.2.2 --- CyberGlove Interface Unit --- p.57 / Chapter 5.2.3 --- Host Computer --- p.58 / Chapter 5.2.4 --- Software --- p.58 / Chapter 5.3 --- Algorithm Implementation --- p.59 / Chapter 5.4 --- Examples for Calculation of Contact Configuration --- p.59 / Chapter 5.5 --- Simulation --- p.68 / Chapter 5.6 --- Experiments --- p.82 / Chapter 5.6.1 --- Translation of an Object --- p.82 / Chapter 5.6.2 --- Rotation of an Object --- p.90 / Chapter 6 --- Conclusions --- p.98 / References --- p.101 / Appendix --- p.106

Unmanned aerial vehicles are a salient solution for rapid deployment in disaster relief, search and rescue, and warfare operations. In these scenarios, the agility, maneuverability and speed of the UAV are vital components towards saving human lives, successfully completing a mission, or stopping dangerous threats. Hence, a high speed, highly agile, and small footprint unmanned aerial vehicle capable of carrying minimal payloads would be the best suited design for completing the desired task. This thesis presents the design, analysis, and realization of a dual-nacelle tiltrotor unmanned aerial vehicle. The design of the dual-nacelle tiltrotor aerial vehicle utilizes two propellers for thrust with the ability to rotate the propellers about the sagittal plane to provide thrust vectoring. The dual-nacelle thrust vectoring of the aerial vehicle provides a slimmer profile, a smaller hover footprint, and allows for rapid aggressive maneuvers while maintaining a desired speed to quickly navigate through cluttered environments. The dynamic model of the dual-nacelle tiltrotor design was derived using the Newton-Euler method and a nonlinear PD controller was developed for spatial trajectory tracking. The dynamic model and nonlinear PD controller were implemented in Matlab Simulink using SimMechanics. The simulation verified the ability of the controlled tiltrotor to track a helical trajectory. To study the scalability of the design, two prototypes were developed: a micro scale tiltrotor prototype, 50mm wide and weighing 30g, and a large scale tiltrotor prototype, 0.5m wide and weighing 2.8kg. The micro scale tiltrotor has a 1.6:1 thrust to weight ratio with an estimated flight time of 6 mins in hover. The large scale tiltrotor has a 2.3:1 thrust to weight ratio with an estimated flight time of 4 mins in hover. A detailed realization of the tiltrotor prototypes is provided with discussions on mechanical design, fabrication, hardware selection, and software implementation. Both tiltrotor prototypes successfully demonstrated hovering, altitude, and yaw maneuvering while tethered and remotely controlled. The developed prototypes provide a framework for further research and development of control strategies for the aggressive maneuvering of underactuated tiltrotor aerial vehicles.

The rise of robots is becoming unstoppable judging by how much effort and money has been invested in this Robotics field so far just these years. Carl Frey and Michael Osbourne in Oxford University released a paper in 2013 and claimed that around 47 percent of current jobs would be automated in the next two decades. But planning robot motion still remains a major problem in Robotics regardless of countless approaches proposed in multiple aspects trying to solve it. TrajOpt(Trajectory Optimizer) is a state-of-art optimization-based software framework for planning robot motions. TrajOpt generates trajectory through constrained sequential convex optimization given several initial guesses, meaning TrajOpt would focus on finding the local minimum guided by an initial guess. However, depending on the complex environment and robot mechanical structure, it sometimes would suffer from being stuck in the local minimum which is not a feasible trajectory. However, BiRRT(Bidirectional Rapidly exploring random tree) is probabilistic complete. BiRRT is a sampling-based method. It has been widely used due to its property, probabilistic completeness. But without using any smoothing techniques, the trajectory generated by BiRRT mostly is inexecutable on the real robot. The objective of proposing this work is to use the sample-based method to enable the TrajOpt become probabilistic complete, which guarantees that considering the solution being present the planner has the capability of acquiring the optimized trajectory. I also intend to experimentally evaluate the performance of this improved method in the simulation called Gazebo and on the real Atlas robot over a wide range of environmental settings.

Ding Dan. / "October 2001." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (p. 121-130). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

El presente trabajo de tesis consiste en el diseño e implementación de un robot móvil de tres grados de libertad, capaz de controlar su posición y trayectoria en un plano cartesiano, además de posicionarse en lugares definidos por el usuario. El objetivo del proyecto es controlar el movimiento del robot, manipulando su traslación y rotación de manera precisa y eficiente. El móvil utiliza dos motores acoplados a llantas para su locomoción, estos motores están colocados en una configuración diferencial, haciendo que el desplazamiento y la rotación sobre su eje sea mucho más eficiente. El robot cuenta con un sistema de medición basado en dos encoders incrementales situados a los lados de los motores. Las señales generadas por estos sensores son procesadas por el móvil, el cual hará el análisis cinemático en línea empleando principios de odometría y ecuaciones en diferencia para estimar la posición y orientación relativa del robot. El resultado de esta operación es utilizado en el algoritmo de control, que consiste en dos controladores PID (proporcional, integral y derivativo) discretos [1]. El primero controla la orientación del robot, asegurando que se posicione en el ángulo correcto antes de iniciar su movimiento y durante el recorrido lineal para que el móvil no se desvíe de su trayectoria. El segundo controlador PID regula la posición lineal del robot en función de las coordenadas iniciales y finales de la trayectoria trazada. Este recorrido es planificado en línea en función a las coordenadas de puntos predefinidos en la lógica de generación de trayectorias. El robot es monitoreado en tiempo real por una computadora que a través de una interfaz gráfica desarrollada en Java permite observar los parámetros de control en cuadros de texto y gráficas dinámicas. Además, permite el envío de comandos pre configurados y secuencias de trayectorias lineales. Para establecer la conexión entre el robot y la PC se utilizó comunicación serial asíncrona bajo el estándar RS-232 y utilizando el protocolo UART. La unidad de procesamiento para la implementación de lógica y algoritmos de control fue un dsPIC30F4011 [2] (controlador digital de señales), ya que posee una alta velocidad para el procesamiento de señales y operaciones matemáticas de punto flotante. Además, cuenta con módulos especializados para el control de motores y comunicación serial, haciendo que la programación sea mucho más eficiente. Al finalizar la implementación del robot, este mostró muy buenos resultados durante las pruebas cumpliendo con los algoritmos de control de rotación y traslación, así como el monitoreo y control desde la PC. Uno de los principales aportes de este trabajo es que se demostró poder tener un control eficiente y preciso de un robot móvil empleando únicamente 2 encoders como sistema de medición. / --- The present thesis consists in the design and implementation of a mobile robot of three degrees of freedom, able to control their position and trajectory in a Cartesian plane, besides being positioned in user-defined locations. The objective of the project is controlling the movement of the robot, manipulating its translation and rotation accurately and efficiently. The robot uses two motors coupled wheels for locomotion, these engines are placed in a differential configuration, causing the displacement and rotation on its axis much more efficient. The robot has a measurement system based on two incremental encoders situated on the sides of the engines. The signals generated by these sensors are processed by the robot, which will do a kinematic analysis in line using odometry principles and difference equations to estimate the relative position and orientation of the robot. The result of this operation is used in the control algorithm, which consists of two discrete PID controllers (proportional, integral and derivative). The first controls the orientation of the robot, ensuring that it is positioned at the correct angle before starting its motion and during the linear path in order to the robot does not deviate from its trajectory. The second linear PID controller regulates the position of the robot according to the initial and final coordinates of the traced path. This trajectory is planned in line according to the coordinates of the predefined points in the logic of paths generation. The robot is monitored in real time by a computer through a graphical interface developed in Java, which allows observing the control parameters in dynamic text boxes and graphics. Additionally, allows sending pre-configured commands and sequences of linear trajectories. To establish the connection between the robot and the PC, it has used serial asynchronous communication under the RS- 232 standard and using the UART protocol. The processing unit for the implementation of logic and control algorithms was a dsPIC30F4011 (digital signal controller), as it has a highspeed signal processing and floating point math operations. It also has specialist modules for motor control and serial communication, making programming much more efficient. After the implementation of the robot, this showed very good results during testing, compliance with the rotation and translation control algorithms, as well as monitoring and controlling from the PC. One of the main contributions of this work is that it showed that you could have an efficient and accurate control of a mobile robot with three degrees of freedom using only two encoders as a measurement system. / Tesis

PhD / This thesis is concerned with combining the perceptual abilities of mobile robots and human operators to execute tasks cooperatively. It is generally agreed that a synergy of human and robotic skills offers an opportunity to enhance the capabilities of today’s robotic systems, while also increasing their robustness and reliability. Systems which incorporate both human and robotic information sources have the potential to build complex world models, essential for both automated and human decision making. In this work, humans and robots are regarded as equal team members who interact and communicate on a peer-to-peer basis. Human-robot communication is addressed using probabilistic representations common in robotics. While communication can in general be bidirectional, this work focuses primarily on human-to-robot information flow. More specifically, the approach advocated in this thesis is to let robots fuse their sensor observations with observations obtained from human operators. While robotic perception is well-suited for lower level world descriptions such as geometric properties, humans are able to contribute perceptual information on higher abstraction levels. Human input is translated into the machine representation via Human Sensor Models. A common mathematical framework for humans and robots reinforces the notion of true peer-to-peer interaction. Human-robot information fusion is demonstrated in two application domains: (1) scalable information gathering, and (2) cooperative decision making. Scalable information gathering is experimentally demonstrated on a system comprised of a ground vehicle, an unmanned air vehicle, and two human operators in a natural environment. Information from humans and robots was fused in a fully decentralised manner to build a shared environment representation on multiple abstraction levels. Results are presented in the form of information exchange patterns, qualitatively demonstrating the benefits of human-robot information fusion. The second application domain adds decision making to the human-robot task. Rational decisions are made based on the robots’ current beliefs which are generated by fusing human and robotic observations. Since humans are considered a valuable resource in this context, operators are only queried for input when the expected benefit of an observation exceeds the cost of obtaining it. The system can be seen as adjusting its autonomy at run-time based on the uncertainty in the robots’ beliefs. A navigation task is used to demonstrate the adjustable autonomy system experimentally. Results from two experiments are reported: a quantitative evaluation of human-robot team effectiveness, and a user study to compare the system to classical teleoperation. Results show the superiority of the system with respect to performance, operator workload, and usability.

No description available.

<p>Development of Industrial robots is becoming more expensive and time consuming over the years. A lot of costs are spent in the development, and so it is necessary to improve the conceptual design phase.</p><p> </p><p>This thesis is an object lesson that shows one of the multiple ways to improve the named phase.</p><p>It basically consists on, using a CAD program, build a robot whose parameters have to be modified from a user interface. The parameters to change are the dimensions of the robot’s parts (morphology parameterization) and also the parts to use (topology parameterization), which can be chosen from a large library of different parts.</p><p> </p><p>Some parameters are changed so as the build robot has similar mass properties to a given one, in order to be able to do some tests with it and export the results to improve the real robot. For this reason, in the interface done there is also written some code to get the mass properties of the built robot. Even so, this thesis only shows how to do the named actions but it has not been done any kind of test.</p>

<p>This thesis investigates the use of Genetic Programming (GP) to evolve controllers for an autonomous robot.</p><p>GP is a type of Genetic Algorithm (GA) using the Darwinian idea of natural selection and genetic recombination, where the individuals most often is represented as a tree-structure. The GP is used to evolve a population of possible solutions over many generations to solve problems.</p><p>The most common approach used today, to develop controllers for autonomous robots, is to employ a GA to evolve an Artificial Neural Network (ANN). This approach is most often used in simulation only or in conjunction with online evolution; where simulation still covers the largest part of the process.</p><p>The GP has been largely neglected in Behaviour Based Robotics (BBR). The is primarily due to the problem of speed, which is the biggest curse of any standard GP. The main contribution of this thesis is the approach of using a linear representation of the GP in online evolution, and to establish whether or not the GP is feasible in this situation. Since this is not a comparison with other methods, only a demonstration of the possibilities with GP, there is no need for testing the particular test cases with other methods.</p><p>The work in this thesis builds upon the work by Wolfgang Banzhaf and Peter Nordin, and therefore a comparison with their work will be done.</p>

This paper describes a method by which range data from a sonar or other type of rangefinder can be used to determine the 2-dimensional position and orientation of a mobile robot inside a room. The plan of the room is modeled as a list of segments indicating the positions of walls. The method works by extracting straight segments from the range data and examining all hypotheses about pairings between the segments and walls in the model of the room. Inconsistent pairings are discarded efficiently by using local constraints based on distances between walls, angles between walls, and ranges between walls along their normal vectors. These constraints are used to obtain a small set of possible positions, which is further pruned using a test for physical consistency. The approach is extremely tolerant of noise and clutter. Transient objects such as furniture and people need not be included in the room model, and very noisy, low-resolution sensors can be used. The algorithm's performance is demonstrated using Polaroid Ultrasonic Rangefinder, which is a low-resolution, high-noise sensor.