Kinodynamic planning explores the collision-free configuration space by constructing a tree on-the-fly. The process terminates when the tree expands into a specified neighborhood of the goal configuration. Often, the resultant path does not reach the goal accurately enough, which raises the question: how does one make an accurate, kinematically feasible connection between the tree and the goal. This is the non-trivial steering problem. Aiming to balance computational efficiency and position accuracy, this work solves an approximate steering problem through applying Pontryagin’s Maximum Principle (PMP). The main contributions of this work are: establishment of an exhaustive set of possible structures of optimal control solutions; and development of a custom solver based on the these structures. Simulations demonstrate the PMP-based custom solver achieves better accuracy than a PID feedback controlbased approach, and is more computationally efficient than a gradient descent-based numerical optimization approach.
Where: Denver, Colorado (virtual)
MERL Contacts: Mouhacine Benosman; Karl Berntorp; Ankush Chakrabarty; Stefano Di Cairano; Saleh Nabi; Rien Quirynen; Yebin Wang; Avishai Weiss
Research Areas: Control, Machine Learning, OptimizationBrief
Date: July 1, 2020 - July 3, 2020
- At the American Control Conference, MERL presented 10 papers on subjects including autonomous-vehicle decision making and motion planning, nonlinear estimation for thermal-fluid models and GNSS positioning, learning-based reference governors and reference governors for railway vehicles, and fail-safe rendezvous control.