Path Planning for Autonomous Platoon Formation

In the context of automated highway systems (AHS), this work proposes an approach that enables a vehicle to autonomously join a platoon with optimized trajectory in the presence of dynamical traffic obstacles. A notable aspect is the use of Model Predictive Control (MPC) optimization of the planned path, in conjunction with a variant of the Rapidly-exploring Random Trees (RRT*) algorithm for the purpose of platoon formation. This combination efficiently explores the space of possible trajectories, returning trajectories that are smoothened out with respect to the dynamic constraints of the vehicle, while at the same time allowing for real-time implementation. The implementation we propose takes into consideration both localization and mapping through relevant sensors and V2V communication. The complete algorithm is tested over various nominal and worst-case scenarios, qualifying the merits of the proposed methodology.

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Integrated Path Planning and Tracking Control of Autonomous Vehicle for Collision Avoidance based on Model Predictive Control and Potential Field

2020 20th International Conference on Control, Automation and Systems (ICCAS) ◽

10.23919/iccas50221.2020.9268369 ◽

2020 ◽

Author(s):

Chanho. Ko ◽

Seungho. Han ◽

Minseong. Choi ◽

Kyung-Soo. Kim

Keyword(s):

Path Planning ◽

Model Predictive Control ◽

Collision Avoidance ◽

Predictive Control ◽

Potential Field ◽

Tracking Control ◽

Autonomous Vehicle

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On the Distributed Path Planning of Multiple Autonomous Vehicles Under Uncertainty Based on Model-Predictive Control and Convex Optimization

IEEE Systems Journal ◽

10.1109/jsyst.2020.3007795 ◽

2020 ◽

pp. 1-10

Author(s):

Seyed Mohsen Ahmadi Mousavi ◽

Behzad Moshiri ◽

Zainabolhoda Heshmati

Keyword(s):

Convex Optimization ◽

Path Planning ◽

Model Predictive Control ◽

Predictive Control ◽

Autonomous Vehicles

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Collision prediction and avoidance for satellite ultra-close relative motion with zonotope-based reachable sets

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410018810255 ◽

2018 ◽

Vol 233 (11) ◽

pp. 3920-3937

Author(s):

Zhanpeng Xu ◽

Xiaoqian Chen ◽

Yiyong Huang ◽

Yuzhu Bai ◽

Qifeng Chen

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Control Method ◽

Reachable Sets ◽

Close Relative ◽

Worst Case ◽

Reference Orbit ◽

Robust Model Predictive Control ◽

Collision Prediction ◽

Robust Model

Collision prediction and avoidance are critical for satellite proximity operations, and the key is the treatment of satellites' motion uncertainties and shapes, especially for ultra-close autonomous systems. In this paper, the zonotope-based reachable sets are utilized to propagate the uncertainties. For satellites with slender structures (such as solar panels), their shapes are simplified as cuboids which is a special class of zonotopes, instead of the classical sphere approach. The domains in position subspace influenced by the uncertainties and shapes are determined, and the relative distance is estimated to assess the safety of satellites. Moreover, with the approximation of the domains, the worst-case uncertainties for path constraints are determined, and a robust model predictive control method is proposed to deal with the line of sight and obstacle avoidance constraints. With zonotope representations of satellites, the proposed robust model predictive control is capable of handling the shapes of the satellite and obstacle simultaneously. Numerical simulations demonstrate the effectiveness of the proposed methods with an elliptic reference orbit. 1

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Path planning with multiple constraints and path following based on model predictive control for robotic fish

Information Processing in Agriculture ◽

10.1016/j.inpa.2021.12.005 ◽

2021 ◽

Author(s):

Yizhuo Mu ◽

Jingfen Qiao ◽

Jincun Liu ◽

Dong An ◽

Yaoguang Wei

Keyword(s):

Path Planning ◽

Model Predictive Control ◽

Predictive Control ◽

Path Following ◽

Robotic Fish ◽

Multiple Constraints

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A method for avoiding obstacles of a self-driving carin the dynamic environment based on model predictive control

Engineering Journal Science and Innovation ◽

10.18698/2308-6033-2021-5-2082 ◽

2021 ◽

Author(s):

N.P. Demenkov ◽

Kai Zou

Keyword(s):

Path Planning ◽

Model Predictive Control ◽

Urban Environment ◽

Predictive Control ◽

Potential Field ◽

Dynamic Environment ◽

Field Method ◽

Artificial Potential Field ◽

Physical Constraints ◽

Local Path

The paper discusses the problem of obstacle avoidance of a self-driving car in urban road conditions. The artificial potential field method is used to simulate traffic lanes and cars in a road environment. The characteristics of the urban environment, as well as the features and disadvantages of existing methods based on the structure of planning-tracking, are analyzed. A method of local path planning is developed, based on the idea of an artificial potential field and model predictive control in order to unify the process of path planning and tracking to effectively cope with the dynamic urban environment. The potential field functions are introduced into the path planning task as constraints. Based on model predictive control, a path planning controller is developed, combined with the physical constraints of the vehicle, to avoid obstacles and execute the expected commands from the top level as the target for the task. A joint simulation was performed using MATLAB and CarSim programs to test the feasibility of the proposed path planning method. The results show the effectiveness of the proposed method.

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