Language
We detect your regional language is French. Do you want to change site's language to French? You may choose other languages.International
English
中文
русск
français
español
العربية
User Center
My Training Class
Feedback
IEEE Robotics and Automation Letters | Vol.3, Issue.1 | | Pages 551-558
HDRM: A Resolution Complete Dynamic Roadmap for Real-Time Motion Planning in Complex Scenes
In this letter, we first theoretically prove the conditions and boundaries of resolution completeness for deterministic roadmap methods with a discretized workspace. A novel variant of such methods, the hierarchical dynamic roadmap (HDRM), is then proposed for solving complex planning problems. A unique hierarchical structure to efficiently encode the configuration-to-workspace occupation information is introduced and allows the robot to check the collision state of tens of millions of samples on-the-fly—the number of which was previously strictly limited by available memory. The hierarchical structure also significantly reduces the time for path searching, hence, the robot is able to find feasible motion plans in real-time in extremely constrained environments. A rigorous benchmarking shows that HDRM is robust and computationally fast compared with classical dynamic roadmap methods and other state-of-the-art planning algorithms. Experiments on the seven degree-of-freedom KUKA LWR robotic arm integrated with live perception further validate the effectiveness of HDRM in complex environments.
Original Text (This is the original text for your reference.)
HDRM: A Resolution Complete Dynamic Roadmap for Real-Time Motion Planning in Complex Scenes
In this letter, we first theoretically prove the conditions and boundaries of resolution completeness for deterministic roadmap methods with a discretized workspace. A novel variant of such methods, the hierarchical dynamic roadmap (HDRM), is then proposed for solving complex planning problems. A unique hierarchical structure to efficiently encode the configuration-to-workspace occupation information is introduced and allows the robot to check the collision state of tens of millions of samples on-the-fly—the number of which was previously strictly limited by available memory. The hierarchical structure also significantly reduces the time for path searching, hence, the robot is able to find feasible motion plans in real-time in extremely constrained environments. A rigorous benchmarking shows that HDRM is robust and computationally fast compared with classical dynamic roadmap methods and other state-of-the-art planning algorithms. Experiments on the seven degree-of-freedom KUKA LWR robotic arm integrated with live perception further validate the effectiveness of HDRM in complex environments.
+More
Translate engine
Article's language
Action
Report
Share
Recommended articles
Report
Select your report category*
Reason*
Your level privilege is in sufficient. You can go to the “User Center” to view your level information and perform points tasks to upgrade your level.
New sign-in location：
Last sign-in location：
Last sign-in date：
About to jump to an external site.
Security unknown, whether to continue