Motion control for autonomous vehicles in outdoor environment

2003 ◽  
Author(s):  
K. Koskinen ◽  
H. Makela ◽  
K. Rintanen ◽  
A. Penttinen ◽  
A. Halme
Keyword(s):  
Motion Control ◽  
Autonomous Vehicles ◽  
Outdoor Environment

scholarly journals Autonomous Navigation of a Team of Unmanned Surface Vehicles for Intercepting Intruders on a Region Boundary

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 297
Author(s):  
Ali Marzoughi ◽  
Andrey V. Savkin
Keyword(s):  
Motion Control ◽  
Autonomous Vehicles ◽  
Autonomous Navigation ◽  
Control Algorithm ◽  
Autonomous Vehicle ◽  
Sufficient Conditions ◽  
Real Life ◽  
Unmanned Surface Vehicles ◽  
Necessary And Sufficient ◽  
See Region

We study problems of intercepting single and multiple invasive intruders on a boundary of a planar region by employing a team of autonomous unmanned surface vehicles. First, the problem of intercepting a single intruder has been studied and then the proposed strategy has been applied to intercepting multiple intruders on the region boundary. Based on the proposed decentralised motion control algorithm and decision making strategy, each autonomous vehicle intercepts any intruder, which tends to leave the region by detecting the most vulnerable point of the boundary. An efficient and simple mathematical rules based control algorithm for navigating the autonomous vehicles on the boundary of the see region is developed. The proposed algorithm is computationally simple and easily implementable in real life intruder interception applications. In this paper, we obtain necessary and sufficient conditions for the existence of a real-time solution to the considered problem of intruder interception. The effectiveness of the proposed method is confirmed by computer simulations with both single and multiple intruders.

scholarly journals Review on Motion Control of Autonomous Vehicles

2020 ◽  
Vol 56 (10) ◽  
pp. 127
Author(s):  
XIONG Lu ◽  
YANG Xing ◽  
ZHUO Guirong ◽  
LENG Bo ◽  
ZHANG Renxie
Keyword(s):  
Motion Control ◽  
Autonomous Vehicles

Motion Control of Autonomous Vehicles with Guaranteed Prescribed Performance

2019 ◽  
Vol 18 (6) ◽  
pp. 1510-1517
Author(s):  
Hongyang Xia ◽  
Jiqing Chen ◽  
Fengchong Lan ◽  
Zhaolin Liu
Keyword(s):  
Motion Control ◽  
Autonomous Vehicles ◽  
Prescribed Performance

scholarly journals Multi-Agent Motion Control of Autonomous Vehicles in 3D Flow Fields

PAMM ◽  
2012 ◽  
Vol 12 (1) ◽  
pp. 733-734 ◽  
Author(s):  
Axel Hackbarth ◽  
Edwin Kreuzer ◽  
Andrew Gray
Keyword(s):  
Motion Control ◽  
Autonomous Vehicles ◽  
Flow Fields ◽  
3D Flow ◽  
Multi Agent

scholarly journals A Seamless Navigation System and Applications for Autonomous Vehicles Using a Tightly Coupled GNSS/UWB/INS/Map Integration Scheme

2021 ◽  
Vol 14 (1) ◽  
pp. 27
Author(s):  
Changqiang Wang ◽  
Aigong Xu ◽  
Xin Sui ◽  
Yushi Hao ◽  
Zhengxu Shi ◽  
...  
Keyword(s):  
High Precision ◽  
Navigation System ◽  
Autonomous Vehicles ◽  
Outdoor Environment ◽  
Precision Positioning ◽  
Outdoor Environments ◽  
Tightly Coupled ◽  
Seamless Positioning ◽  
Indoor And Outdoor Environments ◽  
Indoor And Outdoor

Seamless positioning systems for complex environments have been a popular focus of research on positioning safety for autonomous vehicles (AVs). In particular, the seamless high-precision positioning of AVs indoors and outdoors still poses considerable challenges and requires continuous, reliable, and high-precision positioning information to guarantee the safety of driving. To obtain effective positioning information, multiconstellation global navigation satellite system (multi-GNSS) real-time kinematics (RTK) and an inertial navigation system (INS) have been widely integrated into AVs. However, integrated multi-GNSS and INS applications cannot provide effective and seamless positioning results for AVs in indoor and outdoor environments due to limited satellite availability, multipath effects, frequent signal blockages, and the lack of GNSS signals indoors. In this contribution, multi-GNSS-tightly coupled (TC) RTK/INS technology is developed to solve the positioning problem for a challenging urban outdoor environment. In addition, ultrawideband (UWB)/INS technology is developed to provide accurate and continuous positioning results in indoor environments, and INS and map information are used to identify and eliminate UWB non-line-of-sight (NLOS) errors. Finally, an improved adaptive robust extended Kalman filter (AREKF) algorithm based on a TC integrated single-frequency multi-GNSS-TC RTK/UWB/INS/map system is studied to provide continuous, reliable, high-precision positioning information to AVs in indoor and outdoor environments. Experimental results show that the proposed scheme is capable of seamlessly guaranteeing the positioning accuracy of AVs in complex indoor and outdoor environments involving many measurement outliers and environmental interference effects.

scholarly journals Design, Validation and Comparison of Path Following Controllers for Autonomous Vehicles

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6052
Author(s):  
Xing Yang ◽  
Lu Xiong ◽  
Bo Leng ◽  
Dequan Zeng ◽  
Guirong Zhuo
Keyword(s):  
Motion Control ◽  
Autonomous Vehicles ◽  
Sliding Mode ◽  
Path Following ◽  
Lyapunov Theory ◽  
Longitudinal Motion ◽  
Speed Tracking ◽  
Disturbance Rejection Control ◽  
Pure Pursuit ◽  
Future Direction

As one of the core issues of autonomous vehicles, vehicle motion control directly affects vehicle safety and user experience. Therefore, it is expected to design a simple, reliable, and robust path following the controller that can handle complex situations. To deal with the longitudinal motion control problem, a speed tracking controller based on sliding mode control with nonlinear conditional integrator is proposed, and its stability is proved by the Lyapunov theory. Then, a linear parameter varying model predictive control (LPV-MPC) based lateral controller is formulated that the optimization problem is solved by CVXGEN. The nonlinear active disturbance rejection control (ADRC) method is applied to the second lateral controller that is easy to be implemented and robust to parametric uncertainties and disturbances, and the pure pursuit algorithm serves as a benchmark. Simulation results in different scenarios demonstrate the effectiveness of the proposed control schemes, and a comparison is made to highlight the advantages and drawbacks. It can be concluded that the LPV-MPC has some trouble to handle uncertainties while the nonlinear ADRC performs slight worse tracking but has strong robustness. With the parallel development of the control theory and computing power, robust MPC may be the future direction.

scholarly journals An Analytical Measuring Rectification Algorithm of Monocular Systems in Dynamic Environment

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Deshi Li ◽  
Xiaoliang Wang
Keyword(s):  
Autonomous Vehicles ◽  
Vision System ◽  
Dynamic Environment ◽  
Outdoor Environment ◽  
Safe Distance ◽  
Range Estimation ◽  
Vision Navigation ◽  
Nonholonomic Dynamics ◽  
Yaw Angle ◽  
Range Variation

Range estimation is crucial for maintaining a safe distance, in particular for vision navigation and localization. Monocular autonomous vehicles are appropriate for outdoor environment due to their mobility and operability. However, accurate range estimation using vision system is challenging because of the nonholonomic dynamics and susceptibility of vehicles. In this paper, a measuring rectification algorithm for range estimation under shaking conditions is designed. The proposed method focuses on how to estimate range using monocular vision when a shake occurs and the algorithm only requires the pose variations of the camera to be acquired. Simultaneously, it solves the problem of how to assimilate results from different kinds of sensors. To eliminate measuring errors by shakes, we establish a pose-range variation model. Afterwards, the algebraic relation between distance increment and a camera’s poses variation is formulated. The pose variations are presented in the form of roll, pitch, and yaw angle changes to evaluate the pixel coordinate incensement. To demonstrate the superiority of our proposed algorithm, the approach is validated in a laboratory environment using Pioneer 3-DX robots. The experimental results demonstrate that the proposed approach improves in the range accuracy significantly.

Simulation Models for French Praxitele Project

1996 ◽  
Vol 1521 (1) ◽  
pp. 118-125
Author(s):  
Bruno Arnaldi ◽  
Rémi Cozot ◽  
Stéphane Donikian ◽  
Michel Parent
Keyword(s):  
Urban Environment ◽  
Motion Control ◽  
Autonomous Vehicles ◽  
Geometric Modeling ◽  
Time Synchronization ◽  
Simulation Models ◽  
Large Set ◽  
Cooperative Driving ◽  
Unique System ◽  
Automatic Platooning

The Praxitele project was charged with designing a new kind of transportation in an urban environment, which consisted of a fleet of electric public cars. These public cars are capable of autonomous motion on certain displacements between stations. The realization of such a project requires experimentation regarding the behaviors of autonomous vehicles in the urban environment. Because of the danger connected with these kinds of experiments at a real site, it was necessary to design a virtual urban environment in which simulations could be done. To perform an authentic simulation of a real environment composed of a large set of vehicles (some of which are autonomous and others of which are controlled by the user or by some specific control law), different models need to be implemented: geometric modeling of the environment, mechanical simulation, motion control models, driver models, sensor models, and visualization algorithms. To implement these different models into a unique system, a new simulator system was designed. This simulator takes into account real-time synchronization and communication between cooperative processes implementing the models mentioned earlier. First, the aims and goals of the Praxitele project are presented. The motion control algorithm for automatic platooning of autonomous vehicles is then briefly presented. The focus is on the simulation of a virtual urban environment that includes Praxitele vehicles. The implementation of all of these models is described. Finally, results of a simulation of cooperative driving of the Praxitele vehicles in a virtual urban environment are given.

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