Combined Sampling and Optimization Based Planning for Legged-Wheeled Robots
This work addresses planning challenges for legged-wheeled robots in complex environments, but it appears incremental as it builds on existing optimization and sampling techniques.
The paper tackles the problem of planning for legged-wheeled robots in challenging terrain by combining sampling and optimization methods to avoid local minima, resulting in improved convergence and handling of constraints like non-holonomic rolling and terrain avoidance.
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.