by Law Kwok Ho Cedric. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 106-112). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Related work --- p.4 / Chapter 1.3 --- Thesis overview --- p.5 / Chapter 2 --- Single wheel robot: Gyrover --- p.9 / Chapter 2.1 --- Background --- p.9 / Chapter 2.2 --- Robot concept --- p.11 / Chapter 2.3 --- System description --- p.14 / Chapter 2.4 --- Flywheel characteristics --- p.16 / Chapter 2.5 --- Control patterns --- p.20 / Chapter 3 --- Learning Control --- p.22 / Chapter 3.1 --- Motivation --- p.22 / Chapter 3.2 --- Cascade Neural Network with Kalman filtering --- p.24 / Chapter 3.3 --- Learning architecture --- p.27 / Chapter 3.4 --- Input space --- p.29 / Chapter 3.5 --- Model evaluation --- p.30 / Chapter 3.6 --- Training procedures --- p.35 / Chapter 4 --- Control Architecture --- p.38 / Chapter 4.1 --- Behavior-based approach --- p.38 / Chapter 4.1.1 --- Concept and applications --- p.39 / Chapter 4.1.2 --- Levels of competence --- p.44 / Chapter 4.2 --- Behavior-based control of Gyrover: architecture --- p.45 / Chapter 4.3 --- Behavior-based control of Gyrover: case studies --- p.50 / Chapter 4.3.1 --- Vertical balancing --- p.51 / Chapter 4.3.2 --- Tiltup motion --- p.52 / Chapter 4.4 --- Discussions --- p.53 / Chapter 5 --- Implement ation of Learning Control --- p.57 / Chapter 5.1 --- Validation --- p.57 / Chapter 5.1.1 --- Vertical balancing --- p.58 / Chapter 5.1.2 --- Tilt-up motion --- p.62 / Chapter 5.1.3 --- Discussions --- p.62 / Chapter 5.2 --- Implementation --- p.65 / Chapter 5.2.1 --- Vertical balanced motion --- p.65 / Chapter 5.2.2 --- Tilt-up motion --- p.68 / Chapter 5.3 --- Combined motion --- p.70 / Chapter 5.4 --- Discussions --- p.72 / Chapter 6 --- Shared Control --- p.74 / Chapter 6.1 --- Concept --- p.74 / Chapter 6.2 --- Schemes --- p.78 / Chapter 6.2.1 --- Switch mode --- p.79 / Chapter 6.2.2 --- Distributed mode --- p.79 / Chapter 6.2.3 --- Combined mode --- p.80 / Chapter 6.3 --- Shared control of Gyrover --- p.81 / Chapter 6.4 --- How to share --- p.83 / Chapter 6.5 --- Experimental study --- p.88 / Chapter 6.5.1 --- Heading control --- p.89 / Chapter 6.5.2 --- Straight path --- p.90 / Chapter 6.5.3 --- Circular path --- p.91 / Chapter 6.5.4 --- Point-to-point navigation --- p.94 / Chapter 6.6 --- Discussions --- p.95 / Chapter 7 --- Conclusion --- p.103 / Chapter 7.1 --- Contributions --- p.103 / Chapter 7.2 --- Future work --- p.104

Tong, Fung Ling. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 94-96). / Abstracts in English and Chinese. / Abstract --- p.i / 摘要 --- p.iii / Acknowledgement --- p.iii / Table of Contents --- p.iv / List of Figures --- p.vii / List of Tables --- p.x / Chapter Chapter 1 - --- Introduction --- p.1 / Chapter Chapter 2 - --- State of the art in Vision-based Localization --- p.6 / Chapter 2.1 --- Extended Kalman Filter-based Localization --- p.6 / Chapter 2.1.1 --- Overview of the EKF algorithm --- p.6 / Chapter 2.1.2 --- Process of the EKF-based localization algorithm --- p.8 / Chapter 2.1.3 --- Recent EKF-based vision-based localization algorithms --- p.10 / Chapter 2.1.4 --- Advantages of the EKF-based localization algorithms --- p.11 / Chapter 2.1.5 --- Disadvantages of the EKF-based localization algorithm --- p.11 / Chapter 2.2 --- Monte Carlo Localization --- p.12 / Chapter 2.2.1 --- Overview of MCL --- p.12 / Chapter 2.2.2 --- Recent MCL-based localization algorithms --- p.14 / Chapter 2.2.3 --- Advantages of the MCL-based algorithm --- p.15 / Chapter 2.2.4 --- Disadvantages of the MCL-based algorithm --- p.16 / Chapter 2.3 --- Summary --- p.16 / Chapter Chapter 3 - --- Vision-based Localization as an Optimization Problem --- p.18 / Chapter 3.1 --- "Relationship between the World, Camera and Robot Body Coordinate System" --- p.18 / Chapter 3.2 --- Formulation of the Vision-based Localization as an Optimization Problem --- p.21 / Chapter 3.3 --- Summary --- p.26 / Chapter Chapter 4 - --- Existing Search Algorithms --- p.27 / Chapter 4.1 --- Overview of the Existing Search Algorithms --- p.27 / Chapter 4.2 --- Search Algorithm for the Proposed Objective Function --- p.28 / Chapter 4.3 --- Summary --- p.30 / Chapter Chapter 5 - --- Proposed Vision-based Localization using Genetic Algorithm --- p.32 / Chapter 5.1 --- Mechanism of Genetic Algorithm --- p.32 / Chapter 5.2 --- Formation of Chromosome --- p.35 / Chapter 5.3 --- Fitness Function --- p.39 / Chapter 5.4 --- Mutation and Crossover --- p.40 / Chapter 5.5 --- Selection and Stopping Criteria --- p.42 / Chapter 5.6 --- Adaptive Search Space --- p.44 / Chapter 5.7 --- Overall Flow of the Proposed Algorithm --- p.46 / Chapter 5.8 --- Summary --- p.47 / Chapter Chapter 6 - --- Experimental Results --- p.48 / Chapter 6.1 --- Test Robot --- p.48 / Chapter 6.2 --- Simulator --- p.49 / Chapter 6.2.1 --- Camera states simulation --- p.49 / Chapter 6.2.2 --- Oscillated walking motion simulation --- p.50 / Chapter 6.2.3 --- Input images simulation --- p.50 / Chapter 6.3 --- Computer for simulations --- p.51 / Chapter 6.4 --- Position and Orientation errors --- p.51 / Chapter 6.5 --- Experiment 1 一 Feature points with quantized noise --- p.53 / Chapter 6.5.1 --- Setup --- p.53 / Chapter 6.5.2 --- Results --- p.56 / Chapter 6.6 --- Experiment 2 一 Feature points added with Gaussian noise --- p.62 / Chapter 6.6.1 --- Setup --- p.62 / Chapter 6.6.2 --- Results --- p.62 / Chapter 6.7 --- Experiment 3 一 Noise reduction performance of the adaptive search space strategy --- p.77 / Chapter 6.7.1 --- Setup --- p.77 / Chapter 6.7.2 --- Results --- p.79 / Chapter 6.8 --- Experiment 4 一 Comparison with benchmark algorithms --- p.83 / Chapter 6.8.1 --- Setup --- p.83 / Chapter 6.8.2 --- Results --- p.85 / Chapter 6.9 --- Discussions --- p.88 / Chapter 6.10 --- Summary --- p.90 / Chapter Chapter 7- --- Conclusion --- p.91 / References --- p.94

The first part of this thesis documents experimental investigation into the use of vision for wheeled robot navigation problems. Specifically, using a video camera as a source of feedback to control a wheeled robot toward a static and a moving object in an environment in real-time. The wheeled robot control algorithms are dependent on information from a vision system and an estimator. The vision system design consists of a pan video camera and a visual gaze algorithm which attempts to search and continuously maintain an object of interest within limited camera field of view. Several vision-based algorithms are presented to recognize simple objects of interest in an environment and to calculate relevant parameters required by the control algorithms. An estimator is designed for state estimation of the motion of an object using visual measurements. The estimator uses noisy measurements of relative bearing to an object and object's size on an image plane formed by perspective projection. These measurements can be obtained from the vision system. A set of algorithms have been designed and experimentally investigated using a pan video camera and two wheeled robots in real-time in a laboratory setting. Experimental results and discussion are presented on the performance of the vision-based control algorithms where a wheeled robot successfully approached an object in various motions. The second part of this thesis investigates the coordination problem of flocking in multi-robot system using concepts from graph theory. New control laws are presented for flocking motion of groups of mobile robots based on several leaders. Simulation results are provided to illustrate the control laws and its applications.

Coordination is essential to achieving good performance in cooperative multiagent systems. To date, most work has focused on either implicit or explicit coordination mechanisms, while relatively little work has focused on the benefits of combining these two approaches. In this work we demonstrate that combining explicit and implicit mechanisms can significantly improve coordination and system performance over either approach individually. First, we use difference evaluations (which aim to compute an agent's contribution to the team) and stigmergy to promote implicit coordination. Second, we introduce an explicit coordination mechanism dubbed Intended Destination Enhanced Artificial State (IDEAS), where an agent incorporates other agents' intended destinations directly into its state. The IDEAS approach does not require any formal negotiation between agents, and is based on passive information sharing. Finally, we combine these two approaches on a variant of a team-based multi-robot exploration domain, and show that agents using a both explicit and implicit coordination outperform other learning agents up to 25%. / Graduation date: 2012

For robotic manipulation tasks in uncertain environments, research typically revolves around developing the best possible software control strategy. However, the passive dynamics of the mechanical system, including inertia, stiffness, damping and torque limits, often impose performance limitations that cannot be overcome with software control. Discussions about the passive dynamics are often imprecise, lacking comprehensive details about the physical limitations. In the first half of this paper, we develop relationships between an actuator's passive dynamics and the resulting performance, to better understanding how to tune the passive dynamics. We characterize constant-contact physical interaction tasks into two different tasks that can be roughly approximated as force control and position control and calculate the required input to produce a desired output. These exact solutions provide a basis for understanding how the parameters of the mechanical system affect the overall system's bandwidth limit without limitations of a specific control algorithm. We then present our experimental results compared to the analytical prediction for each task using a bench top actuator. Our analytical and experimental results show what, until now, has only been intuitively understood: soft systems are better at force control, stiff systems are better at position control, and there is no way to optimize an actuator for both tasks. / Graduation date: 2012

The effectiveness of linear control of a planar manipulator is presented for robot operation markedly exceeding the limits of linearity assumed in the design of the linear controller. Wolovich's frequency domain pole placement algorithm is utilized to derive the linear controller. The control scheme must include state estimation since only link position is measured in the planar manipulator studied. Extensive simulations have been conducted not only to verify the linear control design but also to examine the behavior of the controlled system when inputs greatly exceed those assumed for linear design. The results from these studies indicate the linear model performs exactly as designed. The non-linear realistic simulation reveals that the linear model results are obtained when the inputs do not exceed linearity limits. However, when large inputs are applied, the nature of the system response changes significantly. Regardless of the change in behavior, for the cases considered, there was no instability detected and steady-state values were realized with reasonable settling times which increased in length as the size of the inputs were increased. From the simulation results, it is concluded that the linear controller scheme studied is suitable for use in moving objects from one position to another but would not work well in the rapid drawing of lines and curves. / Graduation date: 1992

This thesis presents a control system for a walking machine leg. The leg is representative of one of the six legs required for a proposed walking machine based on the geometry of the darkling beetle. Each of the three joints is controlled by a DC servo motor mounted to the base of the leg. The speed of the motors is controlled with pulse width modulation. Feedback of joint positions is accomplished with potentiometers mounted on the actual joints. A five-point path, forming a rectangle in the global coordinate system, is used as a skeleton of the path of movement. Desired times and accelerations from point to point are used to develop the path of movement, which smoothes corners and velocity transitions along the path. To create a model of the dynamics of each joint, a constant motor speed is output and the joint velocity and joint angle are recorded. From several trials at several different motor speeds, relationships between the joint velocity, joint angle, and motor speed can be found. This data is then least squares fit in two dimensions to give two second order functions. The first function uses the desired joint angle to calculate the variance from the mean joint velocity. This variance is then added to the desired joint velocity and is used in the second function to calculate the needed motor signal. Feedback control is accomplished using a PID control system. Because of the high level of noise in the feedback signal, a digital noise filter is used. Both moving average and linear regression techniques are examined. Performance of the system is measured by comparing the actual path in Cartesian coordinates to the desired path of movement. The RMS error is taken along the path, during the time frame of the ideal system. The maximum Cartesian error along the path is also used in evaluation. To determine suitable feedback gain combinations, several experiments are run and evaluated. Data is plotted and suitable values are chosen for the feedback gains based on their performance and sensitivity to change in performance. The performance of the leg is measured for a basic rectangular path, the basic path with a variation in step angle, and the basic path with a constant body velocity. / Graduation date: 1992

In this document, we propose and examine the novel use of a learning mechanism between the reactive and deliberative layers of a hybrid robot control architecture. Balancing the need to achieve complex goals and meet real-time constraints, many modern mobile robot navigation control systems make use of a hybrid deliberative-reactive architecture. In this paradigm, a high-level deliberative layer plans routes or actions toward a known goal, based on accumulated world knowledge. A low-level reactive layer selects motor commands based on current sensor data and the deliberative layer's plan. The desired system-level effect of this architecture is that the robot is able to combine complex reasoning toward global objectives with quick reaction to local constraints. Implicit in this type of architecture, is the assumption that both layers are using the same model of the robot's capabilities and constraints. It may happen, for example, due to differences in representation of the robot's kinematic constraints, that the deliberative layer creates a plan that the reactive layer cannot follow. This sort of conflict may cause a degradation in system-level performance, if not complete navigational deadlock. Traditionally, it has been the task of the robot designer to ensure that the layers operate in a compatible manner. However, this is a complex, empirical task. Working to improve system-level performance and navigational robustness, we propose introducing a learning mechanism between the reactive layer and the deliberative layer, allowing the deliberative layer to learn a model of the reactive layer's execution of its plans. First, we focus on detecting this inter-layer conflict, and acting based on a corrected model. This is demonstrated on a physical robotic platform in an unstructured outdoor environment. Next, we focus on learning a model to predict instances of inter-layer conflict, and planning to act with respect to this model. This is demonstrated using supervised learning in a physics-based simulation environment. Results and algorithms are presented.

published_or_final_version / abstract / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy

published_or_final_version / abstract / toc / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy