Design and Implementation of the Kinect Controlled Electro-Mechanical Skeleton (K.C.E.M.S)

Mimicking real-time human motion with a low cost solution has been an extremely difficult task in the past but with the release of the Microsoft Kinect motion capture system, this problem has been simplified. This thesis discusses the feasibility and design behind a simple robotic skeleton that utilizes the Kinect to mimic human movements in near real-time. The goal of this project is to construct a 1/3-scale model of a robotically enhanced skeleton and demonstrate the abilities of the Kinect as a tool for human movement mimicry. The resulting robot was able to mimic many human movements but was mechanically limited in the shoulders. Its movements were slower then real-time due to the inability for the controller to handle real-time motions. This research was presented and published at the 2012 SouthEastCon. Along with this, research papers about the formula hybrid accumulator design and the 2010 autonomous surface vehicle were presented and published

Design and implementation of a modular controller for robotic machines

This research focused on the design and implementation of an Intelligent Modular Controller (IMC) architecture designed to be reconfigurable over a robust network. The design incorporates novel communication, hardware, and software architectures. This was motivated by current industrial needs for distributed control systems due to growing demand for less complexity, more processing power, flexibility, and greater fault tolerance. To this end, three main contributions were made. Most distributed control architectures depend on multi-tier heterogeneous communication networks requiring linking devices and/or complex middleware. In this study, first, a communication architecture was proposed and implemented with a homogenous network employing the ubiquitous Ethernet for both real-time and non real-time communication. This was achieved by a producer-consumer coordination model for real-time data communication over a segmented network, and a client-server model for point-to-point transactions. The protocols deployed use a Time-Triggered (TT) approach to schedule real-time tasks on the network. Unlike other TT approaches, the scheduling mechanism does not need to be configured explicitly when controller nodes are added or removed. An implicit clock synchronization technique was also developed to complement the architecture. Second, a reconfigurable mechanism based on an auto-configuration protocol was developed. Modules on the network use this protocol to automatically detect themselves, establish communication, and negotiate for a desired configuration. Third, the research demonstrated hardware/software co-design as a contribution to the growing discipline of mechatronics. The IMC consists of a motion controller board designed and prototyped in-house, and a Java microcontroller. An IMC is mapped to each machine/robot axis, and an additional IMC can be configured to serve as a real-time coordinator. The entire architecture was implemented in Java, thus reinforcing uniformity, simplicity, modularity, and openness. Evaluation results showed the potential of the flexible controller to meet medium to high performance machining requirements

A Robot Operating System (ROS) based humanoid robot control

This thesis presents adapting techniques required to enhance the capability of a commercially available robot, namely, Robotis Bioloid Premium Humanoid Robot (BPHR). BeagleBone Black (BBB), the decision-making and implementing (intelligence providing) component, with multifunctional capabilities is used in this research. Robot operating System (ROS) and its libraries, as well as Python Script and its libraries have been developed and incorporated into the BBB. This fortified BBB intelligence providing component is then transplanted into the structure of the Robotis Bioloid humanoid robot, after removing the latter’s original decision-making and implementing component (controller). Thus, this study revitalizes the Bioloid humanoid robot by converting it into a humanoid robot with multiple features that can be inherited using ROS. This is a first of its kind approach wherein ROS is used as the development framework in conjunction with the main BBB controller and the software impregnated with Python libraries is used to integrate robotic functions. A full ROS computation is developed and a high level Application Programming Interface (API) usable by software utilizing ROS services is also developed. In this revised two-legged-humanoid robot, USB2Dynamixel connector is used to operate the Dynamixel AX-12A actuators through the Wi-Fi interface of the fortified BBB. An accelerometer sensor supports balancing of the robot, and updates data to the BBB periodically. An Infrared (IR) sensor is used to detect obstacles. This dynamic model is used to actuate the motors mounted on the robot leg thereby resulting in a swing-stance period of the legs for a stable forward movement of the robot. The maximum walking speed of the robot is 0.5 feet/second, beyond this limit the robot becomes unstable. The angle at which the robot leans is governed by the feedback from the accelerometer sensor, which is 20 degrees. If the robot tilts beyond a specific degree, then it would come back to its standstill position and stop further movement. When the robot moves forward, the IR sensors sense obstacles in front of the robot. If an obstacle is detected within 35 cm, then the robot stops moving further. Implementation of ROS on top of the BBB (by replacing CM530 controller with the BBB) and using feedback controls from the accelerometer and IR sensor to control the two-legged robotic movement are the novelties of this work

A novel hand exoskeleton with series elastic actuation for modulated torque transfer

Abstract Among wearable robotic devices, hand exoskeletons present an important and persistent challenge due to the compact dimensions and kinematic complexity of the human hand. To address these challenges, this paper introduces HandeXos-Beta (HX-β), a novel index finger-thumb exoskeleton for hand rehabilitation. The HX-β system features an innovative kinematic architecture that allows independent actuation of thumb flexion/extension and circumduction (opposition), thus enabling a variety of naturalistic and functional grip configurations. Furthermore, HX-β features a novel series-elastic actuators (SEA) architecture that directly measures externally transferred torque in real-time, and thus enables both position- and torque-controlled modes of operation, allowing implementation of both robot-in-charge and user-in-charge exercise paradigms. Finally, HX-β's adjustable orthosis, passive degrees of freedom, and under-actuated control scheme allow for optimal comfort, robot-user joint alignment, and flexible actuation for users of various hand sizes. In addition to the mechatronic design and resulting functional capabilities of HX-β, this work presents a series of physical performance characterizations, including the position- and torque-control system performance, frequency response, end effector force, and output impedance. By each measure, the HX-β exhibited performance comparable or superior to previously reported hand exoskeletons, including position and torque step response times on the order of 0.3 s, −3 dB cut-off frequencies ranging from approximately 2.5 to 4 Hz, and fingertip output forces on the order of 4 N. During use by a healthy subject in torque-controlled transparent mode, the HX-β orthosis joints exhibited appropriately low output impedance, ranging from 0.42 to −0.042 Nm/rad at 1 Hz, over a range of functional grasps performed at real-life speeds. This combination of lab bench characterizations and functional evaluation provides a comprehensive verification of the design and performance of the HandeXos Beta exoskeleton, and its suitability for clinical application in hand rehabilitation

An inertial motion capture framework for constructing body sensor networks

Motion capture is the process of measuring and subsequently reconstructing the movement of an animated object or being in virtual space. Virtual reconstructions of human motion play an important role in numerous application areas such as animation, medical science, ergonomics, etc. While optical motion capture systems are the industry standard, inertial body sensor networks are becoming viable alternatives due to portability, practicality and cost. This thesis presents an innovative inertial motion capture framework for constructing body sensor networks through software environments, smartphones and web technologies. The first component of the framework is a unique inertial motion capture software environment aimed at providing an improved experimentation environment, accompanied by programming scaffolding and a driver development kit, for users interested in studying or engineering body sensor networks. The software environment provides a bespoke 3D engine for kinematic motion visualisations and a set of tools for hardware integration. The software environment is used to develop the hardware behind a prototype motion capture suit focused on low-power consumption and hardware-centricity. Additional inertial measurement units, which are available commercially, are also integrated to demonstrate the functionality the software environment while providing the framework with additional sources for motion data. The smartphone is the most ubiquitous computing technology and its worldwide uptake has prompted many advances in wearable inertial sensing technologies. Smartphones contain gyroscopes, accelerometers and magnetometers, a combination of sensors that is commonly found in inertial measurement units. This thesis presents a mobile application that investigates whether the smartphone is capable of inertial motion capture by constructing a novel omnidirectional body sensor network. This thesis proposes a novel use for web technologies through the development of the Motion Cloud, a repository and gateway for inertial data. Web technologies have the potential to replace motion capture file formats with online repositories and to set a new standard for how motion data is stored. From a single inertial measurement unit to a more complex body sensor network, the proposed architecture is extendable and facilitates the integration of any inertial hardware configuration. The Motion Cloud’s data can be accessed through an application-programming interface or through a web portal that provides users with the functionality for visualising and exporting the motion data

PARbot: Personal Assistive Robot

The aging population of the United States is creating a growing need to provide assistive care for elderly and people with disabilities. As the Baby Boomer generation enters retirement, the ratio of caregivers to those that require assistance is projected to decrease. There are currently no commercially available modular assistive robots that can fill this need. Our project aims to provide an alternative to current assisted living options through the development, construction, and testing of a Personal Assistive Robot (PARbot) that allows individuals with general or age related disabilities to maintain some aspects of their independence, such as the ability to shop

Muscle activation mapping of skeletal hand motion: an evolutionary approach.

Creating controlled dynamic character animation consists of mathe- matical modelling of muscles and solving the activation dynamics that form the key to coordination. But biomechanical simulation and control is com- putationally expensive involving complex di erential equations and is not suitable for real-time platforms like games. Performing such computations at every time-step reduces frame rate. Modern games use generic soft- ware packages called physics engines to perform a wide variety of in-game physical e ects. The physics engines are optimized for gaming platforms. Therefore, a physics engine compatible model of anatomical muscles and an alternative control architecture is essential to create biomechanical charac- ters in games. This thesis presents a system that generates muscle activations from captured motion by borrowing principles from biomechanics and neural con- trol. A generic physics engine compliant muscle model primitive is also de- veloped. The muscle model primitive forms the motion actuator and is an integral part of the physical model used in the simulation. This thesis investigates a stochastic solution to create a controller that mimics the neural control system employed in the human body. The control system uses evolutionary neural networks that evolve its weights using genetic algorithms. Examples and guidance often act as templates in muscle training during all stages of human life. Similarly, the neural con- troller attempts to learn muscle coordination through input motion samples. The thesis also explores the objective functions developed that aids in the genetic evolution of the neural network. Character interaction with the game world is still a pre-animated behaviour in most current games. Physically-based procedural hand ani- mation is a step towards autonomous interaction of game characters with the game world. The neural controller and the muscle primitive developed are used to animate a dynamic model of a human hand within a real-time physics engine environment