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| Field |
Value |
| Title: | An Incremental Learning Approach to Motion Planning with Roadmap Management |
| Authors: | 李蔡彥;SHIE, YANG-CHUAN |
| Keywords: | incremental learning; motion planning; probabilistic roadmap management;
reconfigurable random forest; planning for dynamic environments |
| Date: | 2007-03 |
| Issue Date: | 2008-12-16 16:41:29 (UTC+8) |
| Abstract: | Traditional approaches to the motion-planning problem can be classified into solutions
for single-query and multiple-query problems with the tradeoffs on run-time computation
cost and adaptability to environment changes. In this paper, we propose a novel
approach to the problem that can learn incrementally on every planning query and effectively
manage the learned road-map as the process goes on. This planner is based on previous
work on probabilistic roadmaps and uses a data structure called Reconfigurable
Random Forest (RRF), which extends the Rapidly-exploring Random Tree (RRT) structure
proposed in the literature. The planner can account for environmental changes while
keeping the size of the roadmap small. The planner removes invalid nodes in the roadmap
as the obstacle configurations change. It also uses a tree-pruning algorithm to trim
RRF into a more concise representation. Our experiments show that the resulting roadmap
has good coverage of freespace as the original one. We have also successful incorporated
the planner into the application of intelligent navigation control. |
| Relation: | Journal of Information Science and Engineering, 23(2), 525-238 |
| Data Type: | article |
| DCField |
Value |
Language |
| dc.creator (Authors) | 李蔡彥;SHIE, YANG-CHUAN | zh_TW |
| dc.date (Date) | 2007-03 | en_US |
| dc.date.accessioned | 2008-12-16 16:41:29 (UTC+8) | - |
| dc.date.available | 2008-12-16 16:41:29 (UTC+8) | - |
| dc.date.issued (Issue Date) | 2008-12-16 16:41:29 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccuir.lib.nccu.edu.tw/handle/140.119/14988 | - |
| dc.description.abstract (Abstract) | Traditional approaches to the motion-planning problem can be classified into solutions
for single-query and multiple-query problems with the tradeoffs on run-time computation
cost and adaptability to environment changes. In this paper, we propose a novel
approach to the problem that can learn incrementally on every planning query and effectively
manage the learned road-map as the process goes on. This planner is based on previous
work on probabilistic roadmaps and uses a data structure called Reconfigurable
Random Forest (RRF), which extends the Rapidly-exploring Random Tree (RRT) structure
proposed in the literature. The planner can account for environmental changes while
keeping the size of the roadmap small. The planner removes invalid nodes in the roadmap
as the obstacle configurations change. It also uses a tree-pruning algorithm to trim
RRF into a more concise representation. Our experiments show that the resulting roadmap
has good coverage of freespace as the original one. We have also successful incorporated
the planner into the application of intelligent navigation control. | - |
| dc.format | application/ | en_US |
| dc.language (Language) | en | en_US |
| dc.language (Language) | en-US | en_US |
| dc.language.iso | en_US | - |
| dc.relation (Relation) | Journal of Information Science and Engineering, 23(2), 525-238 | en_US |
| dc.subject (Keywords) | incremental learning; motion planning; probabilistic roadmap management;
reconfigurable random forest; planning for dynamic environments | - |
| dc.title (Title) | An Incremental Learning Approach to Motion Planning with Roadmap Management | en_US |
| dc.type (Data Type) | article | en |
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