For edge collapse, the dihedral angle error was introduced in the edge collapse cost of QEM. The collapsing cost of one triangle was determined by the sum of dimensionless number of its local volume error and its dihedral angle error. For triangle collapse, the folding point of one triangle was determined by its normal vector, the Gaussian curvature of each vertex and vertices projection method. In this method, the original model was simplified by triangle collapse, and then after the average dihedral angle error of mesh model reached the allowable error, the model was simplified by edge collapse. To solve this problem, a simplification method with feature preserving for mesh model was proposed. Most of the existing mesh simplification algorithms ignore some small features of the original model reconstructed by industrial CT serial images and the triangular meshes quality of the simplified model is poor. The resulting system can build structures with more than 70 stones, which in turn provide traversable paths to previously inaccessible locations. We demonstrate the approach on a robotic system that uses a newly developed gripper to reliably pick up stones with minimal additional sensors or complex grasp planning. To reuse the same high-level algorithm, we develop a new physics-based planner that explicitly considers the uncertainty produced by incomplete in-situ sensing and imprecision during pickup and placement. ![]() Utilizing irregularly shaped stones makes the problem significantly more challenging since the outcome of individual placements is sensitive to details of contact geometry and friction, which are difficult to observe. The approach is based on our prior work on adaptive ramp building algorithms, but it eliminates the assumption of having specialized building materials that simplify manipulation and planning for stability. ![]() We develop a specialized construction algorithm and robotic system that can autonomously build motion support structures with previously unseen objects. The ability to autonomously modify their environment dramatically increases the capability of robots to operate in unstructured environments.
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