Full-Body Motion Planning for Humanoid Robots using Rapidly Exploring Random Trees

Jacky Baltes, Jonathan Bagot, Soroush Sadeghnejad, John Anderson, Chen Hsien Hsu

研究成果: 雜誌貢獻文章同行評審

2 引文 斯高帕斯(Scopus)

摘要

Humanoid robots with many degrees of freedom have an enormous range of possible motions. To be able to move in complex environments and dexterously manipulate objects, humanoid robots must be capable of creating and executing complex sequences of motions to accomplish their tasks. For soccer playing robots (e.g., the participants of RoboCup), the highly dynamic environment require real-time motion planning in spite of the enormous search space of possible motions. In this research, we propose a practical solution to the general movers problem in the context of motion planning for robots. The proposed robot motion planner uses a sample-based tree planner combined with an incremental simulator that models not only collisions, but also the dynamics of the motion. Thus it can ensure that the robot will be dynamically stable while executing the motion. The effectiveness of the robot motion planner is demonstrated both in simulation and on a real robot, using a variation of the Rapidly Exploring Random Tree (RRT) type of motion planner. The results of our empirical evaluation show that CONNECT works better than EXTEND versions of the RRT algorithms in simple domains, but that this advantage disappears in more obstacle-filled environments. The evaluation also shows that our motion planning system is able to find and execute complex motion plans for a small humanoid robot.

原文英語
頁(從 - 到)245-255
頁數11
期刊KI - Kunstliche Intelligenz
30
發行號3-4
DOIs
出版狀態已發佈 - 2016 十月 1
對外發佈Yes

ASJC Scopus subject areas

  • Artificial Intelligence

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