Open access
Date
2023-12Type
- Journal Article
Abstract
This article presents a hybrid motion planning and
control approach applicable to various ground robot types and
morphologies. Our two-step approach uses a sampling-based
planner to compute an approximate motion which is then fed to
numerical optimization for refinement. The sampling-based stage
finds a long-term global plan consisting of a contact schedule
and sequence of keyframes, i.e., stable whole-body configura-
tions. Subsequently, the optimization refines the solution with a
short-term planning horizon to satisfy all nonlinear dynamics
constraints. The proposed hybrid planner can compute plans
for scenarios that would be difficult for trajectory optimization
or sampling planner alone. We present tasks of traversing
challenging terrain that requires discovering a contact schedule,
navigating non-convex obstacles, and coordinating many degrees
of freedom. Our hybrid planner has been applied to three
different robots: a quadruped, a wheeled quadruped, and a
legged excavator. We validate our hybrid locomotion planner in
the real world and simulation, generating behaviors we could not
achieve with previous methods. The results show that computing
and executing hybrid locomotion plans is possible on hardware
in real-time. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000625515Publication status
publishedExternal links
Journal / series
IEEE Transactions on RoboticsVolume
Pages / Article No.
Publisher
IEEESubject
Motion Planning; Optimization and Optimal Control; Random sampling; Autonomous Excavator; Wheeled-legged robotsOrganisational unit
09570 - Hutter, Marco / Hutter, Marco
02284 - NFS Digitale Fabrikation / NCCR Digital Fabrication
Funding
188596 - Perceptive Dynamic Locomotion on Rough Terrain (SNF)
-- - NCCR Digital Fabrication (SNF)
852044 - Learning Mobility for Real Legged Robots (EC)
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