Efficient Nearest Neighbor Searching for Motion Planning
نویسندگان
چکیده
We present and implement an efficient algorithm for performing nearest-neighbor queries in topological spaces that usually arise in the context of motion planning. Our approach extends the Kd tree-based ANN algorithm, which was developed by Arya and Mount for Euclidean spaces. We argue the correctness of the algorithm and illustrate its efficiency through computed examples. We have applied the algorithm to both probabilistic roadmaps (PRMs) and Rapidly-exploring Random Trees (RRTs). Substantial performance improvements are shown for motion planning examples.
منابع مشابه
Fast Nearest Neighbor Search in SE(3) for Sampling-Based Motion Planning
Nearest neighbor searching is a fundamental building block of most sampling-based motion planners. We present a novel method for fast exact nearest neighbor searching in SE(3)—the 6 dimensional space that represents rotations and translations in 3 dimensions. SE(3) is commonly used when planning the motions of rigid body robots. Our approach starts by projecting a 4-dimensional cube onto the 3-...
متن کاملNon-zero probability of nearest neighbor searching
Nearest Neighbor (NN) searching is a challenging problem in data management and has been widely studied in data mining, pattern recognition and computational geometry. The goal of NN searching is efficiently reporting the nearest data to a given object as a query. In most of the studies both the data and query are assumed to be precise, however, due to the real applications of NN searching, suc...
متن کاملCollision detection or nearest-neighbor search? On the computational bottleneck in sampling-based motion planning
The complexity of nearest-neighbor search dominates the asymptotic running time of many sampling-based motion-planning algorithms. However, collision detection is often considered to be the computational bottleneck in practice. Examining various asymptotically optimal planning algorithms, we characterize settings, which we call NNsensitive, in which the practical computational role of nearest-n...
متن کاملTechniques for Efficient K-nearest Neighbor Searching in Non-ordered Discrete and Hybrid Data Spaces
TECHNIQUES FOR EFFICIENT K-NEAREST NEIGHBOR SEARCHING IN NON-ORDERED DISCRETE AND HYBRID DATA SPACES
متن کاملApproximate Nearest Neighbor Queries among Parallel Segments
We develop a data structure for answering efficiently approximate nearest neighbor queries over a set of parallel segments in three dimensions. We connect this problem to approximate nearest neighbor searching under weight constraints and approximate nearest neighbor searching on historical data in any dimension and give efficient solutions for these as well.
متن کامل