Optimal Navigation of an Automatic Guided Vehicle With Obstacle Constraints: A Broad Learning-Based Approach

Abstract

Trajectoryplanningis a critical component of realizing intelligence in autonomous unmanned systems. While learning-based control offers intriguing real-time control properties and generalization, it has shortcomings when it comes to long offline training requirements. To address this limitation, this paper introduces a real-time optimal navigation control approach based on Broad Learning (BL) for the trajectory planning problem of Automatic Guided Vehicle (AGV) in the presence of obstacle constraints. The proposed framework fully leverages the optimality achieved through traditional optimal control methods and the novel BL approach. To achieve this, the optimal control problem (OCP) is first constructed and then solved offline for the long-term trajectory planning problem for an AGV working within obstacle constraints. After that, an optimal state-control dataset is acquired with the task information embedding. Subsequently, the BL architecture is meticulously designed and trained offline using the dataset to acquire proficiency in the optimal state-control mapping, and a secondary improvement based on the spectral norm constraint is introduced into the original BL architecture. Consequently, this well-trained BL-based controller is proficiently employed to provide feedback control based on the AGV's current state. The numerical simulation section provides a comparative analysis of the network training time consumption for the BL method and the Deep Neural Network (DNN) method, and the impact of different feature representation methods on the BL-based controller is discussed. Additionally, a local re-planning framework for scenario alterations is proposed based on favourable performances. It further showcases the method's ability to mitigate the excessively long offline training times typically associated with learning-based approaches.