MPC-MPNet: Model-predictive motion planning networks for fast, near-optimal planning under kinodynamic constraints

IEEE Robotics and Automation Letters, 2021

Linjun Li, Yinglong Miao, Ahmed H Qureshi, Michael C Yip

Publisher Link: http://ieeexplore.ieee.org/abstract/document/9384209/

Abstract: Kinodynamic Motion Planning (KMP) is to find a robot motion subject to concurrent kinematics and dynamics constraints. To date, quite a few methods solve KMP problems and those that exist struggle to find near-optimal solutions and exhibit high computational complexity as the planning space dimensionality increases. To address these challenges, we present a scalable, imitation learning-based, Model-Predictive Motion Planning Networks framework that quickly finds near-optimal path solutions with worst-case theoretical guarantees under kinodynamic constraints for practical underactuated systems. Our framework introduces two algorithms built on a neural generator, discriminator, and a parallelizable Model Predictive Controller (MPC). The generator outputs various informed states towards the given target, and the discriminator selects the best possible subset from them for the extension. The MPC locally …

Li et al. (2021) MPC-MPNet: Model-predictive motion planning networks for fast, near-optimal planning under kinodynamic constraints, IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 4496-4503.