Browsing by Author "Kontoudis, Georgios Pantelis"

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    Adaptive, Anthropomorphic Robot Hands for Grasping and In-Hand Manipulation
    Kontoudis, Georgios Pantelis (Virginia Tech, 2019-02-01)
    This thesis presents the design, modeling, and development of adaptive robot hands that are capable of performing dexterous, in-hand manipulation. The robot hand comprises of anthropomorphic robotic fingers, which employ an adaptive actuation mechanism. The mechanism achieves both flexion/extension and adduction/abduction, on the finger's metacarpophalangeal joint, by using two actuators. Moment arm pulleys are employed to drive the tendon laterally, such that an amplification on the abduction motion occurs, while also maintaining the flexion motion. Particular emphasis has been given to the modeling and the analysis of the actuation mechanism. Also, a model for spatial motion is provided that relates the actuation modes with the finger motion and the tendon force with the finger characteristics. For the hand design, the use of differential mechanisms simplifies the actuation scheme, as we utilize only two actuators for four fingers, achieving affordable dexterity. A design optimization framework assess the results of hand anthropometry studies to derive key parameters for the bio-inspired actuation design. The model assumptions are evaluated with the finite element method. The proposed finger has been fabricated with the Hybrid Deposition Manufacturing technique and the actuation mechanism's efficiency has been validated with experiments that include the computation of the finger workspace, the assessment of the force exertion capabilities, the demonstration of the feasible motions, and the grasping and manipulation capabilities. Also, the hand design is fabricated with off-the-shelf materials and rapid prototyping techniques while its efficiency has been validated using an extensive set of experimental paradigms that involved the execution of grasping and in-hand manipulation tasks with everyday objects.
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    Communication-Aware, Scalable Gaussian Processes for Decentralized Exploration
    Kontoudis, Georgios Pantelis (Virginia Tech, 2022-01-25)
    In this dissertation, we propose decentralized and scalable algorithms for Gaussian process (GP) training and prediction in multi-agent systems. The first challenge is to compute a spatial field that represents underwater acoustic communication performance from a set of measurements. We compare kriging to cokriging with vehicle range as a secondary variable using a simple approximate linear-log model of the communication performance. Next, we propose a model-based learning methodology for the prediction of underwater acoustic performance using a realistic propagation model. The methodology consists of two steps: i) estimation of the covariance matrix by evaluating candidate functions with estimated parameters; and ii) prediction of communication performance. Covariance estimation is addressed with a multi-stage iterative training method that produces unbiased and robust results with nested models. The efficiency of the framework is validated with simulations and experimental data from field trials. The second challenge is to perform predictions at unvisited locations with a team of agents and limited inter-agent information exchange. To decentralize the implementation of GP training, we employ the alternating direction method of multipliers (ADMM). A closed-form solution of the decentralized proximal ADMM is provided for the case of GP hyper-parameter training with maximum likelihood estimation. Multiple aggregation techniques for GP prediction are decentralized with the use of iterative and consensus methods. In addition, we propose a covariance-based nearest neighbor selection strategy that enables a subset of agents to perform predictions. Empirical evaluations illustrate the efficiency of the proposed methods