التفاصيل البيبلوغرافية
| العنوان: |
Local Randomization in Neighbor Selection Improves PRM Roadmap Quality |
| المؤلفون: |
Troy Mcmahon, Sam Jacobs, Bryan Boyd, Lydia Tapia, Nancy M. Amato |
| المساهمون: |
The Pennsylvania State University CiteSeerX Archives |
| المصدر: |
https://parasol.tamu.edu/publications/download.php?file_id=768. |
| المجموعة: |
CiteSeerX |
| الوصف: |
of the most used classes of motion planning methods. These sampling-based methods generate robot configurations (nodes) and then connect them to form a graph (roadmap) containing representative feasible pathways. A key step in PRM roadmap construction involves identifying a set of candidate neighbors for each node. Traditionally, these candidates are chosen to be the k-closest nodes based on a given distance metric. In this paper, we propose a new neighbor selection policy called LocalRand(k,k′), that first computes the k ′ closest nodes to a specified node and then selects k of those nodes at random. Intuitively, LocalRand attempts to benefit from random sampling while maintaining the higher levels of local planner success inherent to selecting more local neighbors. We provide a methodology for selecting the parameters k and k′. We perform an experimental comparison which shows that for both rigid and articulated robots, LocalRand results in roadmaps that are better connected than the traditional k-closest policy or a purely random neighbor selection policy. The cost required to achieve these results is shown to be comparable to k-closest. I. |
| نوع الوثيقة: |
text |
| اللغة: |
English |
| Relation: |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.637.5891 |
| الاتاحة: |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.637.5891 |
| Rights: |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
| رقم الانضمام: |
edsbas.DACEE111 |
| قاعدة البيانات: |
BASE |