Meshless Mechanics and Point-Based Visualization Methods for Surgical Simulations

Computer-based modeling and simulation practices have become an integral part of the medical education field. For surgical simulation applications, realistic constitutive modeling of soft tissue is considered to be one of the most challenging aspects of the problem, because biomechanical soft-tissue models need to reflect the correct elastic response, have to be efficient in order to run at interactive simulation rates, and be able to support operations such as cuts and sutures. Mesh-based solutions, where the connections between the individual degrees of freedom (DoF) are defined explicitly, have been the traditional choice to approach these problems. However, when the problem under investigation contains a discontinuity that disrupts the connectivity between the DoFs, the underlying mesh structure has to be reconfigured in order to handle the newly introduced discontinuity correctly. This reconfiguration for mesh-based techniques is typically called dynamic remeshing, and most of the time it causes the performance bottleneck in the simulation. In this dissertation, the efficiency of point-based meshless methods is investigated for both constitutive modeling of elastic soft tissues and visualization of simulation objects, where arbitrary discontinuities/cuts are applied to the objects in the context of surgical simulation. The point-based deformable object modeling problem is examined in three functional aspects: modeling continuous elastic deformations with, handling discontinuities in, and visualizing a point-based object. Algorithmic and implementation details of the presented techniques are discussed in the dissertation. The presented point-based techniques are implemented as separate components and integrated into the open-source software framework SOFA. The presented meshless continuum mechanics model of elastic tissue were verified by comparing it to the Hertzian non-adhesive frictionless contact theory. Virtual experiments were setup with a point-based deformable block and a rigid indenter, and force-displacement curves obtained from the virtual experiments were compared to the theoretical solutions. The meshless mechanics model of soft tissue and the integrated novel discontinuity treatment technique discussed in this dissertation allows handling cuts of arbitrary shape. The implemented enrichment technique not only modifies the internal mechanics of the soft tissue model, but also updates the point-based visual representation in an efficient way preventing the use of costly dynamic remeshing operations

Using UAV-Based Imagery to Determine Volume, Groundcover, and Growth Rate Characteristics of Lentil (Lens culinaris Medik.)

Plant growth rate is an essential phenotypic parameter for crop physiologists and plant breeders to understand in order to quantify potential crop productivity based on specific stages throughout the growing season. While plant growth rate information can be attained though manual collection of biomass, this procedure is rarely performed due to the prohibitively large effort and destruction of plant material that is required. Unmanned Aerial Vehicles (UAVs) offer great potential for rapid collection of imagery which can be utilized for quantification of plant growth rate. In this study, six diverse lines of lentil were grown in three replicates of microplots with six biomass collection time-points throughout the growing season over five site-years. Aerial imagery of each biomass collection time point was collected from a UAV and utilized to produce stitched two-dimensional orthomosaics and three-dimensional point clouds. Analysis of this imagery produced quantification of groundcover and vegetation volume on an individual plot basis. Comparison with manually-measured above-ground biomass suggests strong correlation, indicating great potential for UAVs to be utilized in plant breeding programs for evaluation of groundcover and vegetation volume. Nonlinear logistic models were fit to multiple data collection points throughout the growing season. The growth rate and G50, which is the number of growing degree days (GDD) required to accumulate 50 % of maximum growth, parameters of the model are capable of quantifying growth rate, and have potential utility in plant research and plant breeding programs. Predicted maximum volume was identified as a potential proxy for whole-plot biomass measurement. Six new phenotypes have been described that can be accurately and efficiently collected from field trials with the use of UAV’s or other overhead image-collection systems. These phenotypes are; Area Growth Rate, Area G50, Area Maximum Predicted Growth, Volume Growth Rate, Volume G50, and Volume Maximum Predicted Growth

Efficient configuration space construction and optimization

The configuration space is a fundamental concept that is widely used in algorithmic robotics. Many applications in robotics, computer-aided design, and related areas can be reduced to computational problems in terms of configuration spaces. In this dissertation, we address three main computational challenges related to configuration spaces: 1) how to efficiently compute an approximate representation of high-dimensional configuration spaces; 2) how to efficiently perform geometric, proximity, and motion planning queries in high dimensional configuration spaces; and 3) how to model uncertainty in configuration spaces represented by noisy sensor data. We present new configuration space construction algorithms based on machine learning and geometric approximation techniques. These algorithms perform collision queries on many configuration samples. The collision query results are used to compute an approximate representation for the configuration space, which quickly converges to the exact configuration space. We highlight the efficiency of our algorithms for penetration depth computation and instance-based motion planning. We also present parallel GPU-based algorithms to accelerate the performance of optimization and search computations in configuration spaces. In particular, we design efficient GPU-based parallel k-nearest neighbor and parallel collision detection algorithms and use these algorithms to accelerate motion planning. In order to extend configuration space algorithms to handle noisy sensor data arising from real-world robotics applications, we model the uncertainty in the configuration space by formulating the collision probabilities for noisy data. We use these algorithms to perform reliable motion planning for the PR2 robot.Doctor of Philosoph