Russell Gayle

Interactive collision detection between deformable models using chromatic decomposition

We present a novel algorithm for accurately detecting all contacts, including self-collisions, between deformable models. We precompute a chromatic decomposition of a mesh into non-adjacent primitives using graph coloring algorithms. The chromatic decomposition enables us to check for collisions between non-adjacent primitives using a linear-time culling algorithm. As a result, we achieve higher culling efficiency and significantly reduce the number of false positives. We use our algorithm to check for collisions among complex deformable models consisting of tens of thousands of triangles for cloth modeling and medical simulation. Our algorithm accurately computes all contacts at interactive rates. We observed up to an order of magnitude speedup over prior methods.

Interactive 3D distance field computation using linear factorization

We present an interactive algorithm to compute discretized 3D Euclidean distance fields. Given a set of piecewise linear geometric primitives, our algorithm computes the distance field for each slice of a uniform spatial grid. We express the non-linear distance function of each primitive as a dot product of linear factors. The linear terms are efficiently computed using texture mapping hardware. We also improve the performance by using culling techniques that reduce the number of distance function evaluations using bounds on Voronoi regions of the primitives. Our algorithm involves no preprocessing and is able to handle complex deforming models at interactive rates. We have implemented our algorithm on a PC with NVIDIA GeForce 7800 GPU and applied it to models composed of thousands of triangles. We demonstrate its application to medial axis approximation and proximity computations between rigid and deformable models. In practice, our algorithm is more accurate and almost one order of magnitude faster as compared to previous distance computation algorithms that use graphics hardware.

Quick-VDR: Interactive View-Dependent Rendering of Massive Models

We present a novel approach for interactive view-dependent rendering of massive models. Our algorithm combines view-dependent simplification, occlusion culling, and out-of-core rendering. We represent the model as a clustered hierarchy of progressive meshes (CHPM). We use the cluster hierarchy for coarse-grained selective refinement and progressive meshes for fine-grained local refinement. We present an out-of-core algorithm for computation of a CHPM that includes cluster decomposition, hierarchy generation, and simplification. We make use of novel cluster dependencies in preprocess to generate crack-free, drastic simplifications at runtime. The clusters are used for occlusion culling and out-of-core rendering. We add a frame of latency to the rendering pipeline to fetch newly visible clusters from the disk and to avoid stalls. The CHPM reduces the refinement cost for view-dependent rendering by more than an order of magnitude as compared to a vertex hierarchy. We have implemented our algorithm on a desktop PC. We can render massive CAD, isosurface, and scanned models, consisting of tens or a few hundreds of millions of triangles at 10-35 frames per second with little loss in image quality.

Fast proximity computation among deformable models using discrete Voronoi diagrams

We present novel algorithms to perform collision and distance queries among multiple deformable models in dynamic environments. These include inter-object queries between different objects as well as intra-object queries. We describe a unified approach to compute these queries based on N-body distance computation and use properties of the 2nd order discrete Voronoi diagram to perform N-body culling. Our algorithms involve no preprocessing and also work well on models with changing topologies. We can perform all proximity queries among complex deformable models consisting of thousands of triangles in a fraction of a second on a high-end PC. Moreover, our Voronoi-based culling algorithm can improve the performance of separation distance and penetration queries by an order of magnitude.

Path planning for deformable robots in complex environments

Just a test. We present an algorithm for path planning for a flexible robot in complex environments. Our algorithm computes a collision free path by taking into account geometric and physical constraints, including obstacle avoidance, nonpenetration constraint, volume preservation, surface tension, and energy minimization. We describe a new algorithm for collision detection between a deformable robot and fixed obstacles using graphics processors. We also present techniques to efficiently handle complex deformable models composed of tens of thousands of polygons and obtain significant performance improvement over previous approaches. Moreover, we demonstrate a practical application of our algorithm in performing path planning of catheters in liver chemoembolization.

Reactive deformation roadmaps: motion planning of multiple robots in dynamic environments

We present a novel algorithm for motion planning of multiple robots amongst dynamic obstacles. Our approach is based on a new roadmap representation that uses deformable links and dynamically retracts to capture the connectivity of the free space. We use Newtonian physics and Hookes Law to update the position of the milestones and deform the links in response to the motion of other robots and the obstacles. Based on this roadmap representation, we describe our planning algorithms that can compute collision-free paths for tens of robots in complex dynamic environments.

Quick-VDR: out-of-core view-dependent rendering of gigantic models

We present a novel approach for interactive view-dependent rendering of massive models. Our algorithm combines view-dependent simplification, occlusion culling, and out-of-core rendering. We represent the model as a clustered hierarchy of progressive meshes (CHPM). We use the cluster hierarchy for coarse-grained selective refinement and progressive meshes for fine-grained local refinement. We present an out-of-core algorithm for computation of a CHPM that includes cluster decomposition, hierarchy generation, and simplification. We introduce novel cluster dependencies in the preprocess to generate crack-free, drastic simplifications at runtime. The clusters are used for LOD selection, occlusion culling, and out-of-core rendering. We add a frame of latency to the rendering pipeline to fetch newly visible clusters from the disk and avoid stalls. The CHPM reduces the refinement cost of view-dependent rendering by more than an order of magnitude as compared to a vertex hierarchy. We have implemented our algorithm on a desktop PC. We can render massive CAD, isosurface, and scanned models, consisting of tens or a few hundred million triangles at 15-35 frames per second with little loss in image quality.

Multi-robot coordination using generalized social potential fields

We present a novel approach to compute collision-free paths for multiple robots subject to local coordination constraints. More specifically, given a set of robots, their initial and final configurations, and possibly some additional coordination constraints, our goal is to compute a collision-free path between the initial and final configuration that maintains the constraints. To solve this problem, our approach generalizes the social potential field method to be applicable to both convex and nonconvex polyhedra. Social potential fields are then integrated into a “physics-based motion planning” framework which uses constrained dynamics to solve the motion planning problem. Our approach is able to plan for over 200 robots while averaging about 110 ms per step in a variety of environments.

Efficient Motion Planning of Highly Articulated Chains using Physics-based Sampling

We present a novel motion planning algorithm that efficiently generates physics-based samples in a kinematically and dynamically constrained space of a highly articulated chain. Similar to prior kinodynamic planning methods, the sampled nodes in our roadmaps are generated based on dynamic simulation. Moreover, we bias these samples by using constraint forces designed to avoid collisions while moving toward the goal configuration. We adaptively reduce the complexity of the state space by determining a subset of joints that contribute most towards the motion and only simulate these joints. Based on these configurations, we compute a valid path that satisfies non-penetration, kinematic, and dynamics constraints. Our approach can be easily combined with a variety of motion planning algorithms including probabilistic roadmaps (PRMs) and rapidly-exploring random trees (RRTs) and applied to articulated robots with hundreds of joints. We demonstrate the performance of our algorithm on several challenging benchmarks

Cable route planning in complex environments using constrained sampling

We present a route planning algorithm for cable and wire layouts in complex environments. Our algorithm precomputes a global roadmap of the environment by using a variant of the probabilistic roadmap method (PRM) and performs constrained sampling near the contact space. Given the initial and the final configurations, we compute an approximate path using the initial roadmap generated on the contact space. We refine the approximate path by performing constrained sampling and use adaptive forward dynamics to compute a penetration-free path. Our algorithm takes into account geometric constraints like non-penetration and physical constraints like multi-body dynamics and joint limits. We highlight the performance of our planner on different scenarios of varying complexity.