Report
Autonomous Navigation in Unknown Environments using Sparse Kernel-based Occupancy Mapping
| العنوان: | Autonomous Navigation in Unknown Environments using Sparse Kernel-based Occupancy Mapping |
|---|---|
| المؤلفون: | Duong, Thai, Das, Nikhil, Yip, Michael, Atanasov, Nikolay |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Robotics, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control |
| الوصف: | This paper focuses on real-time occupancy mapping and collision checking onboard an autonomous robot navigating in an unknown environment. We propose a new map representation, in which occupied and free space are separated by the decision boundary of a kernel perceptron classifier. We develop an online training algorithm that maintains a very sparse set of support vectors to represent obstacle boundaries in configuration space. We also derive conditions that allow complete (without sampling) collision-checking for piecewise-linear and piecewise-polynomial robot trajectories. We demonstrate the effectiveness of our mapping and collision checking algorithms for autonomous navigation of an Ackermann-drive robot in unknown environments. Comment: Accepted to ICRA 2020 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2002.01921 |
| رقم الانضمام: | edsarx.2002.01921 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |