TY - JOUR
T1 - Full-Body Motion Planning for Humanoid Robots using Rapidly Exploring Random Trees
AU - Baltes, Jacky
AU - Bagot, Jonathan
AU - Sadeghnejad, Soroush
AU - Anderson, John
AU - Hsu, Chen Hsien
N1 - Publisher Copyright:
© 2016, Springer-Verlag Berlin Heidelberg.
PY - 2016/10/1
Y1 - 2016/10/1
N2 - 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.
AB - 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.
KW - Humanoid robots
KW - Motion planning
KW - RRT
KW - Real time AI
UR - http://www.scopus.com/inward/record.url?scp=85063302494&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063302494&partnerID=8YFLogxK
U2 - 10.1007/s13218-016-0450-z
DO - 10.1007/s13218-016-0450-z
M3 - Article
AN - SCOPUS:85063302494
SN - 0933-1875
VL - 30
SP - 245
EP - 255
JO - KI - Kunstliche Intelligenz
JF - KI - Kunstliche Intelligenz
IS - 3-4
ER -