Model predictive control for an AUV with dynamic path planning

2015 ◽  
Author(s):  
Chao Shen ◽  
Yang Shi ◽  
Brad Buckham
Keyword(s):  
Path Planning ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Dynamic Path Planning ◽  
Dynamic Path

scholarly journals Hardware-in-the-Loop Simulation of Self-Driving Electric Vehicles by Dynamic Path Planning and Model Predictive Control

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2447
Author(s):  
Yi Chung ◽  
Yee-Pien Yang
Keyword(s):  
Path Planning ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Autonomous Driving ◽  
Steering System ◽  
Graphic User Interface ◽  
Test Field ◽  
Hardware In The Loop ◽  
Dynamic Path Planning ◽  
Dynamic Path

This paper applies a dynamic path planning and model predictive control (MPC) to simulate self-driving and parking for an electric van on a hardware-in-the-loop (HiL) platform. The hardware platform is a simulator which consists of an electric power steering system, accelerator and brake pedals, and an Nvidia drive PX2 with a robot operating system (ROS). The vehicle dynamics model, sensors, controller, and test field map are virtually built with the PreScan simulation platform. Both manual and autonomous driving modes can be simulated, and a graphic user interface allows a test driver to select a target parking space on a display screen. Three scenarios are demonstrated: forward parking, reverse parking, and obstacle avoidance. When the vehicle perceives an obstacle, the map is updated and the route is adaptively planned. The effectiveness of the proposed MPC is verified in experiments and proved to be superior to a traditional proportional–integral–derivative controller with regards to safety, energy-saving, comfort, and agility.

Dynamic path planning and path following control for autonomous vehicle based on the piecewise affine tire model

2020 ◽  
pp. 095440702094172
Author(s):  
Wuwei Chen ◽  
Mingyue Yan ◽  
Qidong Wang ◽  
Kai Xu
Keyword(s):  
Path Planning ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Path Following ◽  
Tire Model ◽  
Sideslip Angle ◽  
Piecewise Affine ◽  
Dynamic Path Planning ◽  
Path Following Control ◽  
Dynamic Path

This paper proposes a novel dynamic path planning and path following control method for collision avoidance, which works based on an improved piecewise affine tire model. The main contribution of this work is the design of a dynamic path planning method based on model predictive control, where it replans a maneuverable path to avoid moving obstacle in real time. A hierarchical control framework contains a high-level path replanning model predictive control and a low-level path following model predictive control. A collision avoidance cost function in the high hierarchies was designed to calculate the relative dynamic distance, which copes with the sudden obstacle. Moreover, the replanning path is the optimized output according to reference trajectory, obstacle, and handling stability. The control objective of the low hierarchies is to accurately track the replanning path, especially for the increased nonlinearity of large tire sideslip angle. For this reason, an improved piecewise affine tire model is designed and used for model predictive control to improve the path following performance and reduce calculated burden. The main improvement of the piecewise affine tire model is that the varied lateral stiffness coefficients adapt to the change of the tire sideslip angle in different tire regions. Based on the CarSim and Simulink platform, the dynamic path planning and path following simulations are designed to test the proposed method. The simulation results demonstrate the effectiveness of the proposed method.

scholarly journals Global Dynamic Path Planning Fusion Algorithm Combining Jump-A* Algorithm and Dynamic Window Approach

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 19632-19638
Author(s):  
Lisang Liu ◽  
Jinxin Yao ◽  
Dongwei He ◽  
Jian Chen ◽  
Jing Huang ◽  
...  
Keyword(s):  
Path Planning ◽  
A Algorithm ◽  
Fusion Algorithm ◽  
Global Dynamic ◽  
Dynamic Path Planning ◽  
Dynamic Path ◽  
Window Approach

Dynamic Path Planning Algorithm Based on Chaos Genetic Vehicle Navigation

2011 ◽  
Vol 142 ◽  
pp. 12-15
Author(s):  
Ping Feng
Keyword(s):  
Path Planning ◽  
Search Algorithm ◽  
Traffic Condition ◽  
Planning Time ◽  
Dynamic Path Planning ◽  
Short Period ◽  
Planning Algorithm ◽  
Dynamic Path ◽  
Path Planning Algorithm ◽  
Chaos Genetic Algorithm

The paper puts forward the dynamic path planning algorithm based on improving chaos genetic algorithm by using genetic algorithms and chaos search algorithm. In the practice of navigation, the algorithm can compute at the best path to meet the needs of the navigation in such a short period of planning time. Furthermore,this algorithm can replan a optimum path of the rest paths after the traffic condition in the sudden.

scholarly journals Local Dynamic Path Planning for an Ambulance Based on Driving Risk and Attraction Field

2021 ◽  
Vol 13 (6) ◽  
pp. 3194
Author(s):  
Fang Zong ◽  
Meng Zeng ◽  
Yang Cao ◽  
Yixuan Liu
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Velocity Potential ◽  
Attractive Potential ◽  
Local Dynamic ◽  
Repulsive Potential ◽  
Ambulance Personnel ◽  
Lane Changing ◽  
Dynamic Path Planning ◽  
Dynamic Path

Path planning is one of the most important aspects for ambulance driving. A local dynamic path planning method based on the potential field theory is presented in this paper. The potential field model includes two components—repulsive potential and attractive potential. Repulsive potential includes road potential, lane potential and obstacle potential. Considering the driving distinction between an ambulance and a regular vehicle, especially in congested traffic, an adaptive potential function for a lane line is constructed in association with traffic conditions. The attractive potential is constructed with target potential, lane-velocity potential and tailgating potential. The design of lane-velocity potential is to characterize the influence of velocity on other lanes so as to prevent unnecessary lane-changing behavior for the sake of time-efficiency. The results obtained from simulation demonstrate that the proposed method yields a good performance for ambulance driving in an urban area, which can provide support for designing an ambulance support system for the ambulance personnel and dispatcher.

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