Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot

This paper describes a collection of optimization algorithms for achieving dynamic planning, control, and state estimation for a bipedal robot designed to operate reliably in complex environments. To make challenging locomotion tasks tractable, we describe several novel applications of convex, mixed...

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Veröffentlicht in:Autonomous robots Jg. 40; H. 3; S. 429 - 455
Hauptverfasser: Kuindersma, Scott, Deits, Robin, Fallon, Maurice, Valenzuela, Andrés, Dai, Hongkai, Permenter, Frank, Koolen, Twan, Marion, Pat, Tedrake, Russ
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.03.2016
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Computer Imaging
Control
Engineering
Humanoid
Locomotion
Mechatronics
Optimization
Pattern Recognition and Graphics
Planning
Robotics
Robotics and Automation
Robots
State estimation
Task complexity
Vision
Walking
Legged locomotion
Humanoid
State estimation
Optimization
ISSN:0929-5593, 1573-7527
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Zusammenfassung:This paper describes a collection of optimization algorithms for achieving dynamic planning, control, and state estimation for a bipedal robot designed to operate reliably in complex environments. To make challenging locomotion tasks tractable, we describe several novel applications of convex, mixed-integer, and sparse nonlinear optimization to problems ranging from footstep placement to whole-body planning and control. We also present a state estimator formulation that, when combined with our walking controller, permits highly precise execution of extended walking plans over non-flat terrain. We describe our complete system integration and experiments carried out on Atlas, a full-size hydraulic humanoid robot built by Boston Dynamics, Inc.
Bibliographie:ObjectType-Article-1
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ISSN:0929-5593
1573-7527
DOI:10.1007/s10514-015-9479-3