Optimization-Based Maneuver Automata for Cooperative Trajectory Planning of Autonomous Vehicles

Abstract In this work, a novel combined trajectory planner and tracking controller is developed for autonomous vehicles operating on off-road deformable terrains. Common approaches to trajectory planning and tracking often rely on model-dependent schemes, which utilize a simplified model to predict the impact of control inputs to future vehicle response. However, in an off-road context and especially on deformable terrains, accurately modeling the vehicle response for predictive purposes can be challenging due to the complexity of the tire-terrain interaction and limitations of state-of-the-art terramechanics models in terms of operating conditions, computation time, and continuous differentiability. To address this challenge and improve vehicle safety and performance through more accurate prediction of the plant response, in this paper, a nonlinear model predictive control framework is presented that accounts for terrain deformability explicitly using a neural network terramechanics model for deformable terrains. The utility of the proposed scheme is demonstrated on high fidelity simulations for a notional lightweight military vehicle on soft soil. It is shown that the neural network based controller can outperform a baseline Pacejka model based scheme by improving on performance metrics associated with the cost function. In more severe maneuvers, the neural network based controller can achieve sufficient fidelity as compared to the plant to complete maneuvers that lead to failure for the Pacejka based controller. Finally, it is demonstrated that the proposed framework is conducive to real-time implementability.

Download Full-text

Trajectory Planning and Safety Assessment of Autonomous Vehicles Based on Motion Prediction and Model Predictive Control

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2019.2930684 ◽

2019 ◽

Vol 68 (9) ◽

pp. 8546-8556 ◽

Cited By ~ 3

Author(s):

Yijing Wang ◽

Zhengxuan Liu ◽

Zhiqiang Zuo ◽

Zheng Li ◽

Li Wang ◽

...

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Trajectory Planning ◽

Autonomous Vehicles ◽

Safety Assessment ◽

Motion Prediction

Download Full-text

Application of Polynomial Transition Curves for Trajectory Planning on the Headlands

Agriculture ◽

10.3390/agriculture10050144 ◽

2020 ◽

Vol 10 (5) ◽

pp. 144

Author(s):

Marek Boryga ◽

Paweł Kołodziej ◽

Krzysztof Gołacki

Keyword(s):

Trajectory Planning ◽

Autonomous Vehicles ◽

Displacement Function ◽

Transition Curve ◽

The Third ◽

Runge Kutta Method ◽

Maximal Acceleration ◽

Matlab Program ◽

Planning Algorithm ◽

Transition Curves

This paper presents a method of polynomial transition curve application for making agricultural aggregate movement paths during headland turn drives as well as within the field. Four types of agricultural aggregate paths in five different variant designs are discussed. Each path is composed of only two curves, making the so-called transition bi-curve. The curvature described by the linear function as well as the third, fifth, seventh, and ninth degree polynomials was designated. Moreover, a trajectory planning algorithm in which the movement proceeds along two transition curves composing the so-called bi-curve was proposed. The simulation was carried out applying the MATLAB program in which the 4th order Runge–Kutta method was used. The results were presented by means of figures showing the proposed paths and kinematic quantity courses in the displacement function. The obtained trajectories were compared regarding the size and kinematic quantities. The trajectories, whose curvature is described by the 3° polynomial, were found to possess the smallest absolute values of maximal acceleration and jerk and to lack jerk discontinuity. The proposed solutions can be applied for planning trajectory of not only agriculture machines and aggregates but also autonomous vehicles or mobile robots.

Download Full-text