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...

Full description

Saved in:
Bibliographic Details
Published in:Autonomous robots 2016-03, Vol.40 (3), p.429-455
Main Authors: Kuindersma, Scott, Deits, Robin, Fallon, Maurice, Valenzuela, Andrés, Dai, Hongkai, Permenter, Frank, Koolen, Twan, Marion, Pat, Tedrake, Russ
Format: Article
Language:English
Subjects:
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
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0929-5593
1573-7527
DOI:10.1007/s10514-015-9479-3