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We present new algorithms to perform fast probabilistic collision queries between convex as well as non-convex objects. Our approach is applicable to general shapes, where one or more objects are represented using Gaussian probability distributions. We present a fast new algorithm for a pair of convex objects, and extend the approach to non-convex models using hierarchical representations. We highlight...
We present a rapidly exploring-random-tree-based parallel motion planning algorithm that uses the maximal Poisson-disk sampling scheme. Our approach exploits the free-disk property of the maximal Poisson-disk samples to generate nodes and perform tree expansion. Furthermore, we use an adaptive scheme to generate more samples in challenging regions of the configuration space. The Poisson-disk sampling...
We present an optimization-based motion planning algorithm to compute a smooth, collision-free trajectory for a manipulator used to transfer a liquid from a source to a target container. We take into account fluid dynamics constraints as part of the trajectory computation. In order to avoid the high complexity of exact fluid simulation, we introduce a simplified dynamics model based on physically...
We present a new hybrid approach to computing penetration depth (PD) for general polygonal models. Our approach exploits both local and global approaches to PD computation and can compute error-bounded PD approximations for both deep and shallow penetrations. We use a two-step formulation: the first step corresponds to a global approximation approach that samples the configuration space with bounded...
We present a novel optimization-based motion planning algorithm for high degree-of-freedom (DOF) robots. Our approach combines a multi-contact dynamics formulation with optimization-based planning to compute collision-free, smooth and dynamically stable trajectories. Our formulation is general and can handle multiple simultaneous contacts and non-planar surfaces. We highlight the performance of our...
We present an RRT-based motion planning algorithm that uses the maximal Poisson-disk sampling scheme. Our approach exploits the free-disk property of the maximal Poisson-disk samples to generate nodes and perform tree expansion. Furthermore, we use an adaptive scheme to generate more samples in challenging regions of the configuration space. Our approach can be easily parallelized on multi-core CPUs...
We present a new collision and proximity library that integrates several techniques for fast and accurate collision checking and proximity computation. Our library is based on hierarchical representations and designed to perform multiple proximity queries on different model representations. The set of queries includes discrete collision detection, continuous collision detection, separation distance...
We present a new retraction algorithm for high DOF articulated models and use our algorithm to improve the performance of RRT planners in narrow passages. The retraction step is formulated as a constrained optimization problem and performs iterative refinement on the boundary of C-Obstacle space. We also combine the retraction algorithm with decomposition planners to handle very high DOF articulated...
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