Terrain-Adaptive Planning and Control of Complex Motions for Walking Excavators
Author / Producer
Date
2020
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
Data
Rights / License
Abstract
This article presents a planning and control pipeline for legged-wheeled (hybrid) machines. It consists of a Trajectory Optimization based planner that computes references for end-effectors and joints. The references are tracked using a whole-body controller based on a hierarchical optimization approach. Our controller is capable of performing terrain adaptive whole-body control. Furthermore, it computes both torque and position/velocity references, depending on the actuator capabilities. We perform experiments on a Menzi Muck M545, a full size 31 Degrees of Freedom (DoF) walking excavator with five limbs: four wheeled legs and an arm. We show motions that require full-body coordination executed in realistic conditions. To the best of our knowledge, this is the first work that shows the execution of whole-body motions on a full size walking excavator, using all DoFs for locomotion.
Permanent link
Publication status
published
Editor
Book title
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Journal / series
Volume
Pages / Article No.
2684 - 2691
Publisher
IEEE
Event
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
09570 - Hutter, Marco / Hutter, Marco
02284 - NFS Digitale Fabrikation / NCCR Digital Fabrication
Notes
Conference lecture held on October 26, 2020. Due to the Coronavirus (COVID-19) the conference was conducted virtually.
Funding
182132 - History of Vernacular mathematics in medieval South India (Malayalam and Tamil, 9th-16th centuries) (SNF)
852044 - Learning Mobility for Real Legged Robots (EC)
852044 - Learning Mobility for Real Legged Robots (EC)