التفاصيل البيبلوغرافية
| العنوان: |
A scalable method for parallelizing sampling-based motion planning algorithms |
| المؤلفون: |
Sam Ade Jacobs, Kasra Manavi, Juan Burgos, Jory Denny, Shawna Thomas, Nancy M. Amato |
| المساهمون: |
The Pennsylvania State University CiteSeerX Archives |
| المصدر: |
https://parasol.tamu.edu/publications/download.php?file_id=734. |
| سنة النشر: |
2012 |
| المجموعة: |
CiteSeerX |
| الوصف: |
—This paper describes a scalable method for paral-lelizing sampling-based motion planning algorithms. It subdi-vides configuration space (C-space) into (possibly overlapping) regions and independently, in parallel, uses standard (sequen-tial) sampling-based planners to construct roadmaps in each region. Next, in parallel, regional roadmaps in adjacent regions are connected to form a global roadmap. By subdividing the space and restricting the locality of connection attempts, we reduce the work and inter-processor communication associated with nearest neighbor calculation, a critical bottleneck for scalability in existing parallel motion planning methods. We show that our method is general enough to handle a variety of planning schemes, including the widely used Probabilistic Roadmap (PRM) and Rapidly-exploring Random Trees (RRT) algorithms. We compare our approach to two other existing parallel algorithms and demonstrate that our approach achieves better and more scalable performance. Our approach achieves almost linear scalability on a 2400 core LINUX cluster and on a 153,216 core Cray XE6 petascale machine. I. |
| نوع الوثيقة: |
text |
| اللغة: |
English |
| Relation: |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.640.134 |
| الاتاحة: |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.640.134 |
| Rights: |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
| رقم الانضمام: |
edsbas.945A507E |
| قاعدة البيانات: |
BASE |