iDRM: Humanoid motion planning with realtime end-pose selection in complex environments

Yiming Yang, Vladimir Ivan, Zhibin Li, Maurice Fallon, Sethu Vijayakumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

In this paper, we propose a novel inverse Dynamic Reachability Map (iDRM) that allows a floating base system to find valid end–poses in complex and dynamically changing environments in real–time. End–pose planning for valid stance pose and collision–free configuration is an essential problem for humanoid applications, such as providing goal states for walking and motion planners. However, this is non–trivial in complex environments, where standing locations and reaching postures are restricted by obstacles. Our proposed iDRM customizes the robot–to–workspace occupation list and uses an online update algorithm to enable efficient reconstruction of the reachability map to guarantee that the selected end–poses are always collision–free. The iDRM was evaluated in a variety of reaching tasks using the 38 degree–of–freedom (DoF) humanoid robot Valkyrie. Our results show that the approach is capable of finding valid end–poses in a fraction of a second. Significantly, we also demonstrate that motion planning algorithms integrating our end–pose planning method are more efficient than those not utilizing this technique.
Original languageEnglish
Title of host publication Humanoid Robots (Humanoids), 2016 IEEE-RAS 16th International Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Electronic)978-1-5090-4718-5
ISBN (Print)978-1-5090-4719-2
Publication statusPublished - 2 Jan 2017
Event2016 IEEE-RAS 16th International Conference on Humanoid Robots - The Westin Resort & Spa, Cancun, Mexico
Duration: 15 Nov 201617 Nov 2016

Publication series

ISSN (Print)2164-0580


Conference2016 IEEE-RAS 16th International Conference on Humanoid Robots
Abbreviated titleHumanoids 2016
Internet address


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