Combined Sampling and Optimization Based Planning for Legged-Wheeled Robots
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 non-holonomic 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.
- Publication:
-
arXiv e-prints
- Pub Date:
- April 2021
- DOI:
- 10.48550/arXiv.2104.04247
- arXiv:
- arXiv:2104.04247
- Bibcode:
- 2021arXiv210404247J
- Keywords:
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- Computer Science - Robotics;
- Electrical Engineering and Systems Science - Systems and Control