Abstract-We propose a new method for applying RRT * to kinodynamic motion planning problems by using finite-horizon linear quadratic regulation (LQR) to measure cost and to extend the tree. First, we introduce the method in the context of arbitrary affine dynamical systems with quadratic costs. For these systems, the algorithm is shown to converge to optimal solutions almost surely. Second, we extend the algorithm to nonlinear systems with non-quadratic costs, and demonstrate its performance experimentally.
Finally, in REF a new method for applying RRT* to kinodynamic motion planning problems is introduced, using finite-horizon linear quadratic regulation (LQR) to measure cost and to extend the tree.
Optimal sampling-based planning for linear-quadratic kinodynamic systems
2013 IEEE International Conference on Robotics and Automation
2013 IEEE International Conference on Robotics and Automation
Mathematics Computer Science