Fastron: An Online Learning-Based Model and Active Learning Strategy for Proxy Collision Detection

Nikhil Das; Naman Gupta; Michael Yip

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arXiv · 1709.02316

Fastron: An Online Learning-Based Model and Active Learning Strategy for Proxy Collision Detection

Nikhil Das·Naman Gupta·Michael Yip

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Abstract

We introduce the Fastron, a configuration space (C-space) model to be used as a proxy to kinematic-based collision detection. The Fastron allows iterative updates to account for a changing environment through a combination of a novel formulation of the kernel perceptron learning algorithm and an active learning strategy. Our simulations on a 7 degree-of-freedom arm indicate that proxy collision checks may be performed at least 2 times faster than an efficient polyhedral collision checker and at least 8 times faster than an efficient high-precision collision checker. The Fastron model provides conservative collision status predictions by padding C-space obstacles, and proxy collision checking time does not scale poorly as the number of workspace obstacles increases. All results were achieved without GPU acceleration or parallel computing.

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BibTeX RIS

Nikhil Das, Naman Gupta, Michael Yip. 2017-09-07. Fastron: An Online Learning-Based Model and Active Learning Strategy for Proxy Collision Detection. https://arxiv.org/abs/1709.02316

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