iDb-A*: Iterative Search and Optimization for Optimal Kinodynamic Motion Planning

Understand

Motion planning for robotic systems with complex dynamics is a challenging problem.

• While recent sampling-based algorithms achieve asymptotic optimality by propagating random control inputs, their empirical convergence rate is often poor, especially in high-dimensional systems such as multirotors.
• An alternative approach is to first plan with a simplified geometric model and then use trajectory optimization to follow the reference path while accounting for the true dynamics.
• However, this approach may fail to produce a valid trajectory if the initial guess is not close to a dynamically feasible trajectory.