Thèse (M.Sc.A.)--Université de Sherbrooke (Canada), 2007. / Titre de l'écran-titre (visionné le 1 février 2007). In ProQuest dissertations and theses. Publié aussi en version papier.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008. / Source: Dissertation Abstracts International, Volume: 69-11, Section: B, page: 7032. Adviser: Mark W. Spong. Includes bibliographical references (leaves 156-162) Available on microfilm from Pro Quest Information and Learning.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008. / Source: Dissertation Abstracts International, Volume: 69-11, Section: B, page: 6842. Adviser: Stephanie Alexander. Includes bibliographical references (leaves 83-85) Available on microfilm from Pro Quest Information and Learning.

<p> Technology in robotics has improved significantly in recent years. While the majority of research has focused on improving existing methods, it is advantageous to challenge these established methodologies and develop new solutions. This new research centers on a novel method of robot movement design. The proposed model concentrates on a robot containing three steerable wheels, allowing the mobile robot to reach the desired orientation and coordinates with minimal movement. This goal is accomplished by simultaneously moving and rotating the robot while moving in a straight path, unlike the movement provided by standard wheeled vehicles. This method provides greater control of performance and more power of movement on various surfaces, compared to using Omni wheels, which contains the design with the greatest similarity to this proposed method. While this new method may result in added complexity due to the goal-based flexible constraints in speed, wheel rotation, and overall movement, this complication may be mitigated by using appropriate software and hardware.</p><p>

<p> Prior work at Marquette University developed the Marquette Prosthesis, an active transtibial prosthesis that utilized a torsional spring and a four-bar mechanism. The controls for the Marquette Prosthesis implemented a finite state control algorithm to determine the state of gait of the amputee along with two lower level controllers, a PI moment controller to control the moment during stance and a PID position controller to control the position during stance. The Marquette Prosthesis was successful in mimicking the gait profile presented by Winter. However, after completing human subject testing, the Marquette Prosthesis was insufficient in trying to match the gait profile of those who varied from this textbook stride. </p><p> Active transtibial prostheses typically apply finite state control algorithms that struggle with cadence and gait variability of the amputee. Recent work in artificial neural networks (ANN) have shown the possibility to predict the user's intent which can be used as an input signal in an improved controller. The Marquette Prosthesis II was developed that uses a stiffness controller to control the relationship between the position and torque of the ankle. A model of the improved Marquette Prosthesis II was developed in Simulink to ensure that the stiffness controller was robust enough and that this type of control was possible with the limitations of the Marquette Prosthesis, i.e., the link lengths, torsional spring and motor. The mechanical system of the Marquette Prosthesis was then changed such that the spring was in series between the motor and four-bar mechanism to establish a relationship between the motor position, torque of the spring and four-bar mechanism. The control hardware was selected and the stiffness controller was implemented on the Marquette Prosthesis II. The Marquette Prosthesis II control algorithm was tested and validated to show that this approach is feasible.</p><p>

<p> Mobile manipulators are a potential solution to the increasing need for additional flexibility and mobility in industrial robotics applications. However, they tend to lack the accuracy and precision achieved by fixed manipulators, especially in scenarios where both the manipulator and the autonomous vehicle move simultaneously. This thesis analyzes the problem of dynamically evaluating the positioning error of mobile manipulators. In particular, it investigates the use of Bayesian methods to predict the position of the end-effector in the presence of uncertainty propagated from the mobile platform. Simulations and real-world experiments are carried out to test the proposed method against a deterministic approach. These experiments are carried out on two mobile manipulators&mdash;a proof-of-concept research platform and an industrial mobile manipulator&mdash;using ROS and Gazebo. The precision of the mobile manipulator is evaluated through its ability to intercept retroreflective markers using a photoelectric sensor attached to the end-effector. Compared to the deterministic search approach, we observed improved interception capability with comparable search times, thereby enabling the effective performance measurement of the mobile manipulator.</p><p>

<p> Control of robot manipulators can be greatly improved with the use of velocity and torque feedforward control. However, the effectiveness of feedforward control greatly relies on the accuracy of the model. In this study, kinematics and dynamics analysis is performed on a six axis arm, a Delta2 robot, and a Delta3 robot. Velocity feedforward calculation is performed using the traditional means of using the kinematics solution for velocity. However, a neural network is used to model the torque feedforward equations. For each of these mechanisms, we first solve the forward and inverse kinematics transformations. We then derive a dynamic model. Later, unlike traditional methods of obtaining the dynamics parameters of the dynamics model, the dynamics model is used to infer dependencies between the input and output variables for neural network torque estimation. The neural network is trained with joint positions, velocities, and accelerations as inputs, and joint torques as outputs. After training is complete, the neural network is used to estimate the feedforward torque effort. Additionally, an investigation is done on the use of neural networks for deriving the inverse kinematics solution of a six axis arm. Although the neural network demonstrated outstanding ability to model complex mathematical equations, the inverse kinematics solution was not accurate enough for practical use.</p><p>

<p> As technology becomes more integrated into our daily lives, the need for machines that can safely and comfortably interact with the human body has grown. While the rigidity of traditional robotic materials, such as metals and plastics, can provide mechanical and electrical stability to these devices, it can also reduce safety and comfort when placed in contact with soft human tissue. In recent years, these issues have been addressed by incorporating compliant materials, like liquids or soft polymers, into wearable or biomedical devices. However, these materials, by virtue of their softness, cannot support the high loads required for operations like stabilization or gripping. To address this apparent trade-off between load-bearing stiffness and conformable softness, several groups have constructed stiffness-tuning devices, capable of alternating between a high-stiffness state and a low-stiffness state. Although there exist a wide variety of mechanisms by which we can achieve this switching behavior, thermally activated phase change provides the highest stiffness ratio between the soft and stiff states. In this work, use low-melting point conductive thermoplastics to create electrically activated stiffness-switching devices. When a voltage is applied across this thermoplastic, the resulting electric current causes the polymer to heat and melt. This phase change corresponds to an effective stiffness change. </p><p> In the first study, we introduce a novel stiffness switch layout that employs liquid metal as compliant electrodes oriented across the face of a conductive thermoplastic. This new layout results in an 80% decrease in required voltage, a 60% decrease in activation time, and the ability to switch the stiffness of arbitrary geometries. </p><p> In the second study, we examine the effects of the composition of a conductive thermoplastic composite on its stiffness-switching properties, and use these findings can help guide the design of stiffness-switching composites for a three soft robotic applications.</p><p>

<p> Research on natural language processing, such as for image and speech recognition, is rapidly changing focus from statistical methods to neural networks. In this study, we introduce speech recognition capabilities along with computer vision to allow a robot to play snooker completely by itself. The color of the ball to be pocketed is provided as an audio input using an audio device such as a microphone. The system is able to recognize the color from the input using a trained deep learning network. The system then commands the camera to locate the ball of the identified color on a snooker table by using computer vision. To pocket the target ball, the system then predicts the best shot using an algorithm. This activity can be executed accurately based on the efficiency of the trained deep learning model.</p><p>

Navigation techniques are of a paramount importance in the field of mobile robotics. They are employed in many contexts in indoor and outdoor environments such as delivering payloads in a dynamic environment, building safety, security, building measurement, research, and driving on highways. Skilled navigation in mobile robotics usually requires solving two problems, determining the position of the robot, and selecting a motion control strategy. Moreover, when no prior knowledge of the environment is available, the problem becomes even more difficult, as the robot has to build a map of its surroundings as it moves. These three tasks ought to be solved in conjunction, since they depend on each other. This dissertation explores the design of a cost-effective and modular navigation method for mobile robots. In particular, we will look at the process of navigating a mobile robot using the emerging RFID technology. A successful realization of this process has been addressed with two separate navigation modules. Each module presents a separate navigation algorithm for a mobile robot. In the first module, a customized RFID reader is mounted on the robot. The information provided by the reader will then be used for navigation. On the contrary, in the second module, custom-made RFID tags are attached at different locations in the navigation environment (on the ceiling of a building, posts, for instance). The position of the mobile robot is then determined based on the information provided by the tags in the robot's operating region. The angle between the robot's current direction and the target tag is used to provide actions to the actuators. In both modules, the algorithms take advantage of using analogue features of the RFID system instead of relying only on the binary tag number which conventional RFID-driven applications depend on.