6 papers
MR. POP: Multi-Robot Parallel Optimizing Planner for Almost-Surely Asymptotically Optimal Planning
Chih H. Huang, Roy Xing, Brian Plancher +1
Finding globally optimal paths remains a fundamental challenge in multi-robot motion planning. Despite acceleration of almost-surely asymptotically optimal (a.s.a.o.) planners via…
Anytime Global Tensor Motion Planning
Sai Coumar, An T. Le, Zachary Kingston
Global Tensor Motion Planning (GTMP) solves motion planning with batched tensor operations over a layered multipartite graph. We generalize GTMP so that adjacent-layer edges are re…
HJCD-IK: GPU-Accelerated Inverse Kinematics through Batched Hybrid Jacobian Coordinate Descent
Cael Yasutake, Andrew H. Liu, Zachary Kingston +1
Inverse Kinematics (IK) is a core problem in robotics, in which joint configurations are found to achieve a (collision-free) desired end-effector pose. Modern IK solvers face a fun…
Evaluating Machine Learning Approaches for ASCII Art Generation
Sai Coumar, Zachary Kingston
Generating structured ASCII art using computational techniques demands a careful interplay between aesthetic representation and computational precision, requiring models that can e…
Foam: A Tool for Spherical Approximation of Robot Geometry
Sai Coumar, Gilbert Chang, Nihar Kodkani +1
Many applications in robotics require primitive spherical geometry, especially in cases where efficient distance queries are necessary. Manual creation of spherical models is time-…
pRRTC: GPU-Parallel RRT-Connect for Fast, Consistent, and Low-Cost Motion Planning
Chih H. Huang, Pranav Jadhav, Brian Plancher +1
Sampling-based motion planning algorithms, like the Rapidly-Exploring Random Tree (RRT) and its widely used variant, RRT-Connect, provide efficient solutions for high-dimensional p…