Open access
Date
2021Type
- Conference Paper
ETH Bibliography
yes
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Abstract
Planning for legged-wheeled machines is typically done using trajectory optimization because of many degrees of freedom, thus rendering legged-wheeled planners prone to falling prey to bad local minima. We present a combined sampling and optimization-based planning approach that can cope with challenging terrain. The sampling-based stage computes whole-body configurations and contact schedule, which speeds up the optimization convergence. The optimization-based stage ensures that all the system constraints, such as nonholonomic rolling constraints, are satisfied. The evaluations show the importance of good initial guesses for optimization. Furthermore, they suggest that terrain/collision (avoidance) constraints are more challenging than the robot model’s constraints. Lastly, we extend the optimization to handle general terrain representations in the form of elevation maps. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000477441Publication status
publishedExternal links
Book title
2021 IEEE International Conference on Robotics and Automation (ICRA)Pages / Article No.
Publisher
IEEEEvent
Organisational unit
09570 - Hutter, Marco / Hutter, Marco
02284 - NFS Digitale Fabrikation / NCCR Digital Fabrication
Funding
188596 - Perceptive Dynamic Locomotion on Rough Terrain (SNF)
852044 - Learning Mobility for Real Legged Robots (EC)
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ETH Bibliography
yes
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