Motion planning for humanoid robot anticipating environmental changes

2017 Fiscal Year Annual Research Report

• Project/Area Number: 16F16701
• Research Institution: National Institute of Advanced Industrial Science and Technology
• Principal Investigator: 吉田 英一 国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 副研究部門長 (30358329)
• Co-Investigator(Kenkyū-buntansha): ORTHEY ANDREAS 国立研究開発法人産業技術総合研究所, 知能システム研究部門, 外国人特別研究員
• Project Period (FY): 2016-11-07 – 2019-03-31
• Keywords: 知能ロボティクス / 動作計画 / ヒューマノイド

Outline of Annual Research Achievements

In FY2017 we investigated abstraction methods to simplify the motion planning problem in robotics. We proposed a new method of nesting robots in each other, which translates to nesting a sequence of quotient spaces in the configuration space of the robot. Each quotient space abstracts away some information from the robot, for example a robot described by position and orientation would have a quotient space described solely by its position.
After constructing such a quotient space decomposition for a robot, we developed a new algorithm called the Quotient Space RoadMap Planner (QMP) which can exploit such a decomposition. QMP starts at the lowest quotient space level, and constructs a graph to connect start and goal configuration. Once such a graph has been found, QMP constructs a new graph in the next bigger quotient space, but constrained by the edges of the underlying graph. In that way we can sample the configuration space in a more efficient way, which translates to a faster runtime of our algorithm compared to state-of-the-art planning algorithms. We have proven that QMP is probabilistically complete, this means that for any planning problem, the algorithm will find a path if one exists as the time goes to infinity.
We have demonstrated experimentally that QMP is faster than RRT, PRM and EST (three state-of-the-art planning algorithms) on four different scenarios. We showed the applicability to a rigid body free floating in space, to an articulated body free floating in space, to a fixed-base manipulators.

Current Status of Research Progress

2: Research has progressed on the whole more than it was originally planned.

Reason

The research has been making progress as planned, by showing the effectiveness of the proposed planning method based on the quotient space introduced in this project and the results have been submitted to a major robotics confeference. Software has been developed to demonstrate the proposed algorithm practically outperforms the previous method. Our software framework is based on the open motion planning library (OMPL) and the Kris' Locomotion and Manipulation Planning Toolkit (KLAMPT) library. The decomposition of the robot has to be done manually by explicitly constructing a nesting of robots with a set of robots description files (URDF). Those results have been submitted to the international conference of intelligent robots (IROS).

Strategy for Future Research Activity

The basic effectiveness of the proposed method developed so far has been shown. We will continue the development for broader classes of applications, mainly for kinodynamics and coping with narrow spaces. We will make progress based on enough consideration on the generality and scalability of the algorithm together with benchmarking for comparison to the previous methods.