3 research outputs found
Combined Sampling and Optimization Based Planning for Legged-Wheeled Robots
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
Contact Planning for the ANYmal Quadruped Robot using an Acyclic Reachability-Based Planner
International audienceDespite the great progress in quadrupedal robotics during the last decade, selecting good contacts (footholds) in highly uneven and cluttered environments still remains an open challenge. This paper builds upon a state-of-the-art approach, already successfully used for humanoid robots, and applies it to our robotic platform; the quadruped robot ANY-mal. The proposed algorithm decouples the problem into two subprob-lems: first a guide trajectory for the robot is generated, then contacts are created along this trajectory. Both subproblems rely on approximations and heuristics that need to be tuned. The main contribution of this work is to explain how this algorithm has been retuned to work with ANY-mal and to show the relevance of the approach with a variety of tests in realistic dynamic simulations