Intention Aware Motion Planning with Model Predictive Control in Highway Merge Scenario

2019 ◽  
Author(s):  
Hayoung Kim ◽  
Dongchan Kim ◽  
Kunsoo Huh
Keyword(s):  
Model Predictive Control ◽  
Motion Planning ◽  
Predictive Control

Autonomous Underwater Vehicle Motion Planning via Sampling Based Model Predictive Control

2014 ◽  
Vol 670-671 ◽  
pp. 1370-1377 ◽  
Author(s):  
Lin Lin Wang ◽  
Hong Jian Wang ◽  
Li Xin Pan
Keyword(s):  
Model Predictive Control ◽  
Motion Planning ◽  
Predictive Control ◽  
Autonomous Underwater Vehicle ◽  
Angular Rate ◽  
Control Variable ◽  
Underwater Vehicle ◽  
Planning Optimization ◽  
Sampling Points ◽  
Rolling Optimization

In order to improve the ability of independent planning for AUV (Autonomous Underwater Vehicle), a new method of motion planning based on SBMPC (Sampling Based Model Predictive Control) is proposed, which is combined with model predictive control theory. Input sampling is directly made in control variable space, and sampling data is substituted into the predictive model of AUV motion. Then surge velocity and yaw angular rate in next sampling time are obtained through calculations. If predictive states are evaluated according to the performance index previously defined, optimal prediction of AUV states in next sampling can be used to realize motion planning optimization. Effects of three sampling methods (viz. uniform sampling, Halton sampling and CVT sampling) on motion planning performance are also compared in simulations. Statistical analysis demonstrates that CVT sampling points has the most uniform coverage in two-dimensional plane when amount of sampling points is the same for three methods. Simulation results show that it is effective and feasible to plan a route for AUV by using CVT sampling and rolling optimization of MPC (Model Predictive Control).

scholarly journals Sensor-Based Task-Constrained Motion Planning using Model Predictive Control

2018 ◽  
Vol 51 (22) ◽  
pp. 220-225 ◽  
Author(s):  
Massimo Cefalo ◽  
Emanuele Magrini ◽  
Giuseppe Oriolo
Keyword(s):  
Model Predictive Control ◽  
Motion Planning ◽  
Predictive Control ◽  
Constrained Motion

scholarly journals INTEGRATION OF MOTION PLANNING AND MODEL-PREDICTIVE-CONTROL-BASED CONTROL SYSTEM FOR AUTONOMOUS ELECTRIC VEHICLES

Transport ◽  
2015 ◽  
Vol 30 (3) ◽  
pp. 353-360 ◽  
Author(s):  
Guodong Yin ◽  
Jianghu Li ◽  
Xianjian Jin ◽  
Chentong Bian ◽  
Nan Chen
Keyword(s):  
Control System ◽  
Model Predictive Control ◽  
Motion Planning ◽  
Predictive Control ◽  
Electric Vehicles ◽  
Autonomous Driving ◽  
Planning System ◽  
Time Interval ◽  
Physical Constraints ◽  
Autonomous Electric Vehicles

This paper introduces the development of an autonomous driving system in autonomous electric vehicles, which consists of a simplified motion-planning program and a Model-Predictive-Control-Based (MPC-based) control system. The motion-planning system is based on polynomial parameterization, which computes a path toward the expected longitudinal and lateral positions within required time interval in real scenarios. Then the MPC-based control system cooperates the front steering and individual wheel torques to track the planned trajectories, while fulfilling the physical constraints of actuators. The proposed system is evaluated through simulation, using a seven-degrees-offreedom vehicle model with a ‘magic formula’ tire model. The simulations and validation through CarSim show that the proposed planner algorithm and controller are feasible and can achieve requirements of autonomous driving in normal scenarios.

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