This paper presents the design of Cataglyphis, a research rover that won the NASA Sample Return Robot Centennial Challenge in 2015. During the challenge, Cataglyphis was the only robot that was able to autonomously find, retrieve, and return multiple types of samples in a large natural environment without using Earth‐specific sensors such as GPS and magnetic compasses. It navigates through a fusion of measurements collected from inertial sensors, wheel encoders, a nodding Lidar, a set of ranging radios, a camera on a panning platform, and a sun sensor. In addition to visual detection of a homing beacon, computer vision algorithms provide the sample detection, identification, and localization capabilities, with low false positive and false negative rates demonstrated during the competition. The mission planning and control software enables robot behaviors, determines sequences of actions, and helps the robot to recover from various failure conditions. A compliant, under‐actuated manipulator conforms to the natural terrain before picking up samples of various size, weight, and shape.
There is a need to integrate tactile sensing into robotic manipulators performing tasks in space environments, including those used to repair satellites. Integration can be achieved by embedding specialized tactile sensors. Reliable and consistent signal interpretation can be obtained by ensuring that sensors with a suitable sensing mechanism are selected based on operational demands, and that materials used within the sensors do not change structurally under vacuum and expected applied pressures, and between temperatures of -80°C to +120°C. The sensors must be able to withstand space environmental conditions and remain adequately sensitive throughout their operating life. Additionally, it is necessary to integrate the sensors into the target system with minimum disturbance while remaining responsive to applied loads. Previous work has been completed to characterize sensors within the selected temperature and pressure ranges. The current work builds on this investigation by embedding these sensors in different geometries and testing the response measured among varying configurations. Embedding material selection was aided by using a dynamic mechanical analyzer (DMA) to determine stress/strain behavior for adhesives and compliant layers used to keep the sensors in place and distribute stresses evenly. Electromechanical characterization of the embedded sensor packages was conducted by using the DMA in tandem with an inductance-capacitance-resistance (LCR) meter. Methods for embedding the sensor packages were developed with the aid of finite element analysis and physical testing to account for specific geometrical constraints. Embedded sensor prototypes were tested within representative models of potential embedding locations to compare final embedded sensor performance.
The coordination between signals from cortical structures and spinal segmental pathways responsible for the control of locomotion remains a contentious issue in human motor control. The signals are known to be integrated, but the nature of these neural calculations is unknown. To understand these interactions in humans, noninvasive cortical stimulation techniques can be combined with detailed analyses of muscle activity patterns in locomotor tasks. Firstly, I would like to thank my family and friends, both near and far, for their support throughout my time here in West Virginia. This work could not have been conducted without the help of my lab mate Matthew Boots, who helped run all the experiments conducted for this work and was instrumental during the development of the experimental set up. Thank you also to the Neural Engineering Laboratory's technicians, Sarah Freeman and Neha Lal for their support. And of course, thank you to all my participants without whom this work would not have been possible. Thank you to Bradley Pollard, Erienne Olesh, and Russell Hardesty of the Neural Engineering Research Laboratory for their help in setting up my experiment, discussing analysis strategies, and reviewing endless figures. Thank you to Dr. Yu Gu and the entire WVU Sample Return Robot team. Your mentorship and friendship has been irreplaceable, and working with this team is an experience I will build on throughout my career. I feel lucky to have worked with you all.
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