Proportional Navigation and Model Predictive Control of an Unmanned Autonomous Vehicle for Obstacle Avoidance

2018 ◽  
Author(s):  
Ryan P. Shaw ◽  
David M. Bevly
Keyword(s):  
Obstacle Avoidance ◽  
Autonomous Vehicle ◽  
Integrated System ◽  
Reference Trajectory ◽  
Ground Vehicles ◽  
Proportional Navigation ◽  
Predictive Controller ◽  
Proportional Navigation Law ◽  
Vehicle Dynamic Model ◽  
And Control

This paper presents a new approach for the guidance and control of a UGV (Unmanned Ground Vehicle). An obstacle avoidance algorithm was developed using an integrated system involving proportional navigation (PN) and a nonlinear model predictive controller (NMPC). An obstacle avoidance variant of the classical proportional navigation law generates command lateral accelerations to avoid obstacles, while the NMPC is used to track the reference trajectory given by the PN. The NMPC utilizes a lateral vehicle dynamic model. Obstacle avoidance has become a popular area of research for both unmanned aerial vehicles and unmanned ground vehicles. In this application an obstacle avoidance algorithm can take over the control of a vehicle until the obstacle is no longer a threat. The performance of the obstacle avoidance algorithm is evaluated through simulation. Simulation results show a promising approach to conditionally implemented obstacle avoidance.

scholarly journals Formation Control for a Fleet of Autonomous Ground Vehicles: A Survey

Robotics ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 67 ◽  
Author(s):  
Aakash Soni ◽  
Huosheng Hu
Keyword(s):  
Autonomous Vehicles ◽  
Formation Control ◽  
State Of The Art ◽  
Autonomous Vehicle ◽  
Ground Vehicles ◽  
Implementation Challenges ◽  
Autonomous Ground Vehicles ◽  
Vehicle Platooning ◽  
And Control

Autonomous/unmanned driving is the major state-of-the-art step that has a potential to fundamentally transform the mobility of individuals and goods. At present, most of the developments target standalone autonomous vehicles, which can sense the surroundings and control the vehicle based on this perception, with limited or no driver intervention. This paper focuses on the next step in autonomous vehicle research, which is the collaboration between autonomous vehicles, mainly vehicle formation control or vehicle platooning. To gain a deeper understanding in this area, a large number of the existing published papers have been reviewed systemically. In other words, many distributed and decentralized approaches of vehicle formation control are studied and their implementations are discussed. Finally, both technical and implementation challenges for formation control are summarized.

scholarly journals Canonical instabilities of autonomous vehicle systems

2016 ◽  
Author(s):  
Rodrick Wallace
Keyword(s):  
Traffic Control ◽  
Large Scale ◽  
Autonomous Vehicle ◽  
Power Network ◽  
Ground Vehicles ◽  
The Road ◽  
Control Information ◽  
Formal Argument ◽  
Vehicle Systems ◽  
And Control

Formal argument suggests that command, communication and control systems can remain stable in the sense of the Data Rate Theorem that mandates the minimum rate of control information required to stabilize inherently unstable 'plants', but may nonetheless, under fog-of-war demands, collapse into dysfunctional modes at variance with their fundamental mission. We apply the theory to autonomous ground vehicles under intelligent traffic control in which swarms of interacting, self-driving devices are inherently unstable as a consequence of the basic irregularity of the road network. It appears that such 'V2V/V2I' systems will experience large-scale failures analogous to the vast propagating fronts of power network blackouts, and possibly less benign, but more subtle patterns of `psychopathology' at various scales.

Navigation and Obstacle Avoidance of Snake-Robot Guided by a Co-Robot UAV Visual Servoing

2020 ◽  
Author(s):  
Mahdi Haghshenas-Jaryani ◽  
Hakki Erhan Sevil ◽  
Liang Sun
Keyword(s):  
Obstacle Avoidance ◽  
Autonomous Navigation ◽  
Visual Servoing ◽  
Integrated System ◽  
Ground Vehicles ◽  
Snake Robot ◽  
Cluttered Environments ◽  
Localization And Mapping ◽  
Path Planner ◽  
Snake Robots

Abstract This paper presents the concept of teaming up snake-robots, as unmanned ground vehicles (UGVs), and unmanned aerial vehicles (UAVs) for autonomous navigation and obstacle avoidance. Snake robots navigate in cluttered environments based on visual servoing of a co-robot UAV. It is assumed that snake-robots do not have any means to map the surrounding environment, detect obstacles, or self-localize, and these tasks are allocated to the UAV, which uses visual sensors to track the UGVs. The obtained images were used for the geo-localization and mapping the environment. Computer vision methods were utilized for the detection of obstacles, finding obstacle clusters, and then, mapping based on Probabilistic Threat Exposure Map (PTEM) construction. A path planner module determines the heading direction and velocity of the snake robot. A combined heading-velocity controller was used for the snake robot to follow the desired trajectories using the lateral undulatory gait. A series of simulations were carried out for analyzing the snake-robot’s maneuverability and proof-of-concept by navigating the snake robot in an environment with two obstacles based on the UAV visual servoing. The results showed the feasibility of the concept and effectiveness of the integrated system for navigation.

Autonomous obstacle avoidance maneuvering of thrust-vectored airship with neural network control

2019 ◽  
Vol 234 (3) ◽  
pp. 689-708
Author(s):  
Amardeep Mishra
Keyword(s):  
Neural Network ◽  
Obstacle Avoidance ◽  
Sliding Mode ◽  
Network Control ◽  
Reference Trajectory ◽  
Scientific Exploration ◽  
Operational Profile ◽  
Control Framework ◽  
Equivalent Control ◽  
And Control

There has been a renewed interest in recent times in airship technology owing to its potential usage for applications ranging from defense, scientific exploration, advertising to even remote monitoring. For airships to expand operational profile, further enhancement of configurational features and control development for full autonomy are key technologies gaining attention. In this paper, beginning with the mathematical modeling of a thrust-vectored airship, the integrated motion planning and controller development for vehicle autonomy, taking into account various uncertainties, are dealt with. A rapidly exploring random tree-based obstacle avoidance path planning exercise is carried out to chart out a trajectory in the presence of obstacles. Then, a neural network-based sliding mode controller is subsequently designed that learns the unknown equivalent control in sliding mode control framework to track the reference trajectory. Simulation results presented at the end demonstrate the effectiveness of the approach.

scholarly journals Pursuer Navigation Based on Proportional Navigation and Optimal Information Fusion

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shulin Feng ◽  
Zhanxin Li ◽  
Li Liu ◽  
Hongyong Yang ◽  
Yuanhua Yang ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Information Fusion ◽  
Three Dimensional ◽  
Proportional Navigation ◽  
Kinematics Model ◽  
Application Range ◽  
Environment Simulation ◽  
Optimal Information ◽  
Point To Point ◽  
Proportional Navigation Law

Pursuer navigation is proposed based on the three-dimensional proportional navigation law, and this method presents a family of navigation laws resulting in a rich behavior for different parameters. Firstly, the kinematics model for the pursuer and the target is established. Secondly, the proportional navigation law is deduced through the kinematics model. Based on point-to-point navigation, obstacle avoidance is implemented by adjusting the control parameters, and the combination can enrich the application range of obstacle avoidance and guidance laws. Thirdly, information fusion weighted by diagonal matrices is used for decreasing the tracking precision. Finally, simulations are conducted in the MATLAB environment. Simulation results verify the availability of the proposed navigation law.

scholarly journals Canonical instabilities of autonomous vehicle systems

2016 ◽  
Author(s):  
Rodrick Wallace
Keyword(s):  
Traffic Control ◽  
Large Scale ◽  
Autonomous Vehicle ◽  
Power Network ◽  
Ground Vehicles ◽  
The Road ◽  
Control Information ◽  
Formal Argument ◽  
Vehicle Systems ◽  
And Control

Formal argument suggests that command, communication and control systems can remain stable in the sense of the Data Rate Theorem that mandates the minimum rate of control information required to stabilize inherently unstable 'plants', but may nonetheless, under fog-of-war demands, collapse into dysfunctional modes at variance with their fundamental mission. We apply the theory to autonomous ground vehicles under intelligent traffic control in which swarms of interacting, self-driving devices are inherently unstable as a consequence of the basic irregularity of the road network. It appears that such 'V2V/V2I' systems will experience large-scale failures analogous to the vast propagating fronts of power network blackouts, and possibly less benign, but more subtle patterns of `psychopathology' at various scales.

Concept and principles of building an integrated system of management of activities of regional gas supply system entities Kaliningrad region

2019 ◽  
pp. 64-72
Author(s):  
G.G. Arunyants
Keyword(s):  
Control System ◽  
Natural Gas ◽  
Supply System ◽  
Integrated System ◽  
Software Complex ◽  
Geographically Distributed ◽  
Basic Solutions ◽  
Kaliningrad Region ◽  
And Control ◽  
Gas Supply

The results of analysis of problems of regulation of gas supply complex of Kaliningrad region and main ways to increase its efficiency, as well as basic solutions for creation of a software complex Т-GAZ-2 automated calculation of natural gas tariffs for ACS of gas supply system subjects, geographically distributed and information connected to the regional automated information and control system (RAIS).

Modeling and Control of Hybrid Propulsion System for Ground Vehicles

2018 ◽  
Author(s):  
Yuan Zou ◽  
Junqiu Li ◽  
Xiaosong Hu ◽  
Yann Chamaillard
Keyword(s):  
Propulsion System ◽  
Ground Vehicles ◽  
Hybrid Propulsion ◽  
Modeling And Control ◽  
And Control
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