scholarly journals Fast nonlinear model predictive planner and control for an unmanned ground vehicle in the presence of disturbances and dynamic obstacles

2021 ◽  
Author(s):  
Subhan Khan ◽  
Jose Guivant
Keyword(s):  
Nonlinear Model ◽  
Optimal Path ◽  
Unmanned Ground Vehicle ◽  
Dijkstra Algorithm ◽  
Nonlinear Disturbance Observer ◽  
Ground Vehicle ◽  
Simulation Based ◽  
Processing Resources ◽  
Path Planner ◽  
And Control

Abstract This paper presents a solution for the tracking control problem, for an unmanned ground vehicle (UGV), under the presence of skid-slip and external disturbances in an environment with static and moving obstacles. To achieve the proposed task, we have used a path-planner which is based on fast nonlinear model predictive control (NMPC); the planner generates feasible trajectories for the kinematic and dynamic controllers to drive the vehicle safely to the goal location. Additionally, the NMPC deals with dynamic and static obstacles in the environment. A kinematic controller (KC) is designed using evolutionary programming (EP), which tunes the gains of the KC. The velocity commands, generated by KC, are then fed to a dynamic controller, which jointly operates with a nonlinear disturbance observer (NDO) to prevent the effects of perturbations. Furthermore, pseudo priority queues (PPQ) based Dijkstra algorithm is combined with NMPC to propose optimal path to perform map-based practical simulation. Finally, simulation based experiments are performed to verify the technique. Results suggest that the proposed method can accurately work, in real-time under limited processing resources.

scholarly journals Modelling and Control of Unmanned Ground Vehicle

2021 ◽  
Author(s):  
Zain UI Abdin ◽  
Taimur Islam Khan ◽  
Mazhar Shabir
Keyword(s):  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
And Control

scholarly journals MODIFIED VIRTUAL SEMI-CIRCLE PATH PLANNING

2016 ◽  
Vol 78 (6-6) ◽  
Author(s):  
R. N. Farah ◽  
Amira Shahirah ◽  
N. Irwan ◽  
R. L. Zuraida
Keyword(s):  
Path Planning ◽  
Path Length ◽  
Optimal Path ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Reactive Navigation ◽  
Optimal Path Planning ◽  
Two Phases

The challenging part of path planning for an Unmanned Ground Vehicle (UGV) is to conduct a reactive navigation. Reactive navigation is implemented to the sensor based UGV. The UGV defined the environment by collecting the information to construct it path planning. The UGV in this research is known as Mobile Guard UGV-Truck for Surveillance (MG-TruckS). Modified Virtual Semi Circle (MVSC) helps the MG-TruckS to reach it predetermined goal point successfully without any collision. MVSC is divided into two phases which are obstacles detection phase and obstacles avoidance phase to compute an optimal path planning. MVSC produces shorter path length, smoothness of velocity and reach it predetermined goal point successfully.

Evalaution and Control of Mirror Sliding Friction of Unmanned Ground Vehicle using Radial Basis Neural Network

2019 ◽  
Vol 11 (5) ◽  
Author(s):  
Xiaohui Yang ◽  
Jian Zhao
Keyword(s):  
Neural Network ◽  
Sliding Friction ◽  
Rbf Neural Network ◽  
Simulation Method ◽  
Vehicle Speed ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Disturbance Term ◽  
And Control ◽  
Msf Model

In order to effectively analyse the mirror sliding friction(MSF) degree of unmanned ground vehicle(UGV) and improve its anti-disturbance performance, a simulation method for MSF degree of UGV based on RBF neural network is proposed. A single-input and double-output RBF neural network is adopted to estimate the uncertain dynamic parameters of the MSF model. The obtained parameters are used to describe the MSF control law based on RBF neural network. An adaptive law based on slow time-varying disturbance characteristics is designed to estimate the total friction disturbance term in the MSF model online. The simulation results show that the proposed method can analyse the MSF degree of unmanned ground vehicle at different speeds and gradients. The influence of gradient on the decline rate of friction degree is greater than that of vehicle speed. The mean error of friction disturbance term calculated by the method is only about 0.9% which has the advantage of low error of friction degree estimation when compared to conventional methods.

Nonlinear Model Predictive Controller for an Unmanned Ground Vehicle on Variable Terrain

2015 ◽  
Author(s):  
Andrew Eick ◽  
David Bevly
Keyword(s):  
Real Time ◽  
Nonlinear Model ◽  
Control Algorithm ◽  
Target Location ◽  
Tire Model ◽  
Unmanned Ground Vehicle ◽  
Model Predictive Controller ◽  
Ground Vehicle ◽  
Rollover Risk ◽  
Predictive Controller

Rough, off-road terrain contains multiple hazards for an unmanned ground vehicle (UGV). In this paper, hazards are classified into three groups: obstacles, rough traversable terrain, and rough untraversable terrain. These three types of hazards create a rollover risk for a UGV. A nonlinear model predictive controller (NMPC) that is capable of navigating a UGV through these hazards is presented. The control algorithm features a nonlinear tire model which more accurately captures the dynamics of the UGV when compared to a linearized tire model, and has a fast enough run time for real time implementation. On an actual vehicle, the UGV is assumed to be equipped with a perception based sensor, such as a Light Detection And Ranging (LiDAR) unit, to provide information of the terrain roughness, grade, and elevation. This information is used by the NMPC to safely control the vehicle to a target location. However, for the purposes of this paper, control inputs and terrain are simulated in Car-Sim [1], and the feasibility of real time implementation is investigated.

Mechatronics Implementation of Inverse Dynamics-Based Controller for an Off-Road UGV

2015 ◽  
Author(s):  
Mostafa Salama ◽  
Vladimir V. Vantsevich
Keyword(s):  
Power Distribution ◽  
Control Algorithm ◽  
Inverse Dynamics ◽  
Dc Motor ◽  
Unmanned Ground Vehicle ◽  
Power Losses ◽  
Wheel Load ◽  
Ground Vehicle ◽  
Dc Motors ◽  
And Control

This paper presents a project developed at the University of Alabama at Birmingham (UAB) aimed to design, implement, and test an off-road Unmanned Ground Vehicle (UGV) with individually controlled four drive wheels that operate in stochastic terrain conditions. An all-wheel drive off-road UGV equipped with individual electric dc motors for each wheel offers tremendous potential to control the torque delivered to each individual wheel in order to maximize UGV slip efficiency by minimizing slip power losses. As previous studies showed, this can be achieved by maintaining all drive wheels slippages the same. Utilizing this approach, an analytical method to control angular velocities of all wheels was developed to provide the same slippages of the four wheels. This model-based method was implemented in an inverse dynamics-based control algorithm of the UGV to overcome stochastic terrain conditions and minimize wheel slip power losses and maintain a given velocity profile. In this paper, mechanical and electrical components and control algorithm of the UGV are described in order to achieve the objective. Optical encoders built-in each dc motor are used to measure the actual angular velocity of each wheel. A fifth wheel rotary encoder sensor is attached to the chassis to measure the distance travel and estimate the longitudinal velocity of the UGV. In addition, the UGV is equipped with four electric current sensors to measure the current draw from each dc motor at various load conditions. Four motor drivers are used to control the dc motors using National Instruments single-board RIO controller. Moreover, power system diagrams and controller pinout connections are presented in detail and thus explain how all these components are integrated in a mechatronic system. The inverse dynamics control algorithm is implemented in real-time to control each dc motors individually. The integrated mechatronics system is distinguished by its robustness to stochastic external disturbances as shown in the previous papers. It also shows a promising adaptability to disturbances in wheel load torques and changes in stochastic terrain properties. The proposed approach, modeling and hardware implementation opens up a new way to the optimization and control of both unmanned ground vehicle dynamics and vehicle energy efficiency by optimizing and controlling individual power distribution to the drive wheels.

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