Maximize-perturb-minimize: A fast and effective heuristic to obtain sets of locally optimal robot postures

Martim Brandao, Kenji Hashimoto, Atsuo Takanishi

研究成果: Conference contribution

抄録

Complex robots such as legged and humanoid robots are often characterized by non-convex optimization landscapes with multiple local minima. Obtaining sets of these local minima has interesting applications in global optimization, as well as in smart teleoperation interfaces with automatic posture suggestions. In this paper we propose a new heuristic method to obtain sets of local minima, which is to run multiple minimization problems initialized around a local maximum. The method is simple, fast, and produces diverse postures from a single nominal posture. Results on the robot WAREC-1 using a sum-of-squared-Torques cost function show that our method quickly obtains lower-cost postures than typical random restart strategies. We further show that obtained postures are more diverse than when sampling around nominal postures, and that they are more likely to be feasible when compared to a uniform-sampling strategy. We also show that lack of completeness leads to the method being most useful when computation has to be fast, but not on very large computation time budgets.

本文言語English
ホスト出版物のタイトル2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017
出版社Institute of Electrical and Electronics Engineers Inc.
ページ2624-2629
ページ数6
ISBN(電子版)9781538637418
DOI
出版ステータスPublished - 2018 3月 23
イベント2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017 - Macau, China
継続期間: 2017 12月 52017 12月 8

出版物シリーズ

名前2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017
2018-January

Other

Other2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017
国/地域China
CityMacau
Period17/12/517/12/8

ASJC Scopus subject areas

  • 人工知能
  • 機械工学
  • 制御と最適化
  • モデリングとシミュレーション

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