Solving Footstep Planning as a Feasibility Problem Using L1-Norm Minimization
- 주제(키워드) Humanoid and bipedal locomotion , legged robots , motion and path planning
- 주제(기타) Robotics
- 설명문(일반) [Song, Daeun; Kim, Young J.] Ewha Womans Univ, Dept Comp Sci & Engn, Seoul 03760, South Korea; [Fernbach, Pierre; Flayols, Thomas; Mansard, Nicolas] Univ Toulouse, CNRS, LAAS, F-31400 Toulouse, France; [Prete, Andrea Del] Univ Trento, Dept Ind Engn, Via Sommarive 9, I-30123 Trento, Italy; [Tonneau, Steve] Univ Edinburgh, IPAB, Edinburgh EH8 9YL, Midlothian, Scotland
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- 발행기관 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- 발행년도 2021
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000182206
- 본문언어 영어
- Published As http://dx.doi.org/10.1109/LRA.2021.3088797
초록/요약
One challenge of legged locomotion on uneven terrains is to deal with both the discrete problem of selecting a contact surface for each footstep and the continuous problem of placing each footstep on the selected surface. Consequently, footstep planning can be addressed with a Mixed Integer Program (MIP), an elegant but computationally demanding method, which can make it unsuitable for online planning. We reformulate the MIP into a cardinality problem, then approximate it as a computationally efficient similar to 1-norm minimisation, called SL1M. Moreover, we improve the performance and convergence of SL1M by combining it with a sampling-based root trajectory planner to prune irrelevant surface candidates. Our tests on the humanoid Talos in four representative scenarios show that SL1M always converges faster than MIP. For scenarios when the combinatorial complexity is small (< 10 surfaces per step), SL1M converges at least two times faster than MIP with no need for pruning. In more complex cases, SL1M converges up to 100 times faster thanMIP with the help of pruning. Moreover, pruning can also improve the MIP computation time. The versatility of the framework is shown with additional tests on the quadruped robot ANYmal.
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