scholarly journals Model predictive control based trajectory generation for autonomous vehicles — An architectural approach

Author(s):  
Marcus Nolte ◽  
Marcel Rose ◽  
Torben Stolte ◽  
Markus Maurer
2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110195
Author(s):  
Sorin Grigorescu ◽  
Cosmin Ginerica ◽  
Mihai Zaha ◽  
Gigel Macesanu ◽  
Bogdan Trasnea

In this article, we introduce a learning-based vision dynamics approach to nonlinear model predictive control (NMPC) for autonomous vehicles, coined learning-based vision dynamics (LVD) NMPC. LVD-NMPC uses an a-priori process model and a learned vision dynamics model used to calculate the dynamics of the driving scene, the controlled system’s desired state trajectory, and the weighting gains of the quadratic cost function optimized by a constrained predictive controller. The vision system is defined as a deep neural network designed to estimate the dynamics of the image scene. The input is based on historic sequences of sensory observations and vehicle states, integrated by an augmented memory component. Deep Q-learning is used to train the deep network, which once trained can also be used to calculate the desired trajectory of the vehicle. We evaluate LVD-NMPC against a baseline dynamic window approach (DWA) path planning executed using standard NMPC and against the PilotNet neural network. Performance is measured in our simulation environment GridSim, on a real-world 1:8 scaled model car as well as on a real size autonomous test vehicle and the nuScenes computer vision dataset.


2021 ◽  
Author(s):  
Jonas Berlin ◽  
Georg Hess ◽  
Anton Karlsson ◽  
William Ljungbergh ◽  
Ze Zhang ◽  
...  

This paper presents an approach to collision-free, long-range trajectory generation for a mobile robot in an industrial environment with static and dynamic obstacles. For the long range planning a visibility graph together with A* is used to find a collision-free path with respect to the static obstacles. This path is used as a reference path to the trajectory planning algorithm that in addition handles dynamic obstacles while complying with the robot dynamics and constraints. A Nonlinear Model Predictive Control (NMPC) solver generates a collision-free trajectory by staying close the initial path but at the same time obeying all constraints. The NMPC problem is solved efficiently by leveraging the new numerical optimization method Proximal Averaged Newton for Optimal Control (PANOC). The algorithm was evaluated by simulation in various environments and successfully generated feasible trajectories spanning hundreds of meters in a tractable time frame.


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