Composite Control for Path Tracking of an Intelligent Vehicle Base on Particle Swarm Optimization and Bezier Curve

2020 ◽  
Vol 29 (8) ◽  
Author(s):  
Shi Peilong ◽  
Zhao Xuan ◽  
Liu Wentao ◽  
Zhou Wenhui ◽  
Yu Qiang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Path Tracking ◽  
Bézier Curve ◽  
Bezier Curve ◽  
Intelligent Vehicle ◽  
Swarm Optimization ◽  
Composite Control

scholarly journals A new technique to design planar dipole antennas by using Bezier curve and Particle Swarm Optimization

2016 ◽  
Vol 65 (3) ◽  
pp. 513-525 ◽  
Author(s):  
Nuttaka Homsup ◽  
Winyou Silabut ◽  
Vuttichai Kesornpatumanum ◽  
Pravit Boonek ◽  
Waroth Kuhirun
Keyword(s):  
Particle Swarm Optimization ◽  
Ieee 802.11 ◽  
Particle Swarm ◽  
New Technique ◽  
Dual Band ◽  
Bézier Curve ◽  
Bezier Curve ◽  
Swarm Optimization ◽  
A New Technique ◽  
5 Ghz

Abstract This research presents a new technique which includes the principle of a Bezier curve and Particle Swarm Optimization (PSO) together, in order to design the planar dipole antenna for the two different targets. This technique can improve the characteristics of the antennas by modifying copper textures on the antennas with a Bezier curve. However, the time to process an algorithm will be increased due to the expansion of the solution space in optimization process. So as to solve this problem, the suitable initial parameters need to be set. Therefore this research initialized parameters with reference antenna parameters (a reference antenna operates on 2.4 GHz for IEEE 802.11 b/g/n WLAN standards) which resulted in the proposed designs, rapidly converted into the goals. The goal of the first design is to reduce the size of the antenna. As a result, the first antenna is reduced in the substrate size from areas of 5850 mm2 to 2987 mm2 (48.93% approximately) and can also operates at 2.4 GHz (2.37 GHz to 2.51 GHz). The antenna with dual band application is presented in the second design. The second antenna is operated at 2.4 GHz (2.40 GHz to 2.49 GHz) and 5 GHz (5.10 GHz to 5.45 GHz) for IEEE 802.11 a/b/g/n WLAN standards.

scholarly journals The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Gaining Han ◽  
Weiping Fu ◽  
Wen Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Real Time ◽  
Fitness Function ◽  
Particle Swarm ◽  
Intelligent Vehicle ◽  
Vehicle Navigation ◽  
Dynamics Model ◽  
Swarm Optimization ◽  
Behavior Dynamics ◽  
Navigation Path

In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO) algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability.

scholarly journals Obstacle avoidance and multitarget tracking of a super redundant modular manipulator based on Bezier curve and particle swarm optimization

2020 ◽  
Author(s):  
Chen Li ◽  
Ying Ma ◽  
Yu Zhang ◽  
Jinguo Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Obstacle Avoidance ◽  
Trajectory Tracking ◽  
Degrees Of Freedom ◽  
Optimization Method ◽  
Bézier Curve ◽  
Bezier Curve ◽  
Redundant Manipulator ◽  
Swarm Optimization ◽  
Composite Optimization

Abstract A super redundant serpentine manipulator has slender structure and multiple degrees of freedom and can travel through narrow space and move in complex space. This manipulator is composed of many modules that can form different lengths of robot arms for different application sites. The increase in degrees of freedom causes the inverse kinematics of redundant manipulator to be typical and immensely increases the calculation load in the joint space. This paper presents a composite optimization method of path planning for obstacle avoidance and discrete trajectory tracking of a super redundant manipulator. In this composite optimization, path planning is established on a Bezier curve, particle swarm optimization is adopted to adjust the control points of the Bezier curve with the kinematic constraints of manipulator, and a feasible obstacle avoidance path is obtained along with a discrete trajectory tracking using a follow-the-leader strategy. The relative distance between each two discrete path points is limited to reduce the fitting error of the connecting rigid links to the smooth curve. Simulation results show that this composite optimization method can rapidly search for the appropriate trajectory to guide the manipulator in obtaining the target while achieving obstacle avoidance and meeting joint constraints. The proposed algorithm is suitable for 3D space obstacle avoidance and multitarget path tracking.

scholarly journals Obstacle avoidance and multitarget tracking of a super redundant modular manipulator based on Bezier curve and particle swarm optimization

2020 ◽  
Author(s):  
chen li ◽  
Ying Ma ◽  
Yu Zhang ◽  
Jinguo Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Obstacle Avoidance ◽  
Trajectory Tracking ◽  
Degrees Of Freedom ◽  
Optimization Method ◽  
Bézier Curve ◽  
Bezier Curve ◽  
Redundant Manipulator ◽  
Swarm Optimization ◽  
Integrated Optimization

Abstract A super redundant serpentine manipulator has slender structure and multiple degrees of freedom. It can travel through narrow spaces and move in complex spaces. This manipulator is composed of many modules that can form different lengths of robot arms for different application sites. The increase in degrees of freedom causes the inverse kinematics of redundant manipulator to be typical and immensely increases the calculation load in the joint space. This paper presents an integrated optimization method to solve the path planning for obstacle avoidance and discrete trajectory tracking of a super redundant manipulator. In this integrated optimization, path planning is established on a Bezier curve, and particle swarm optimization is adopted to adjust the control points of the Bezier curve with the kinematic constraints of manipulator. A feasible obstacle avoidance path is obtained along with a discrete trajectory tracking by using a follow-the-leader strategy. The relative distance between each two discrete path points is limited to reduce the fitting error of the connecting rigid links to the smooth curve. Simulation results show that this integrated optimization method can rapidly search for the appropriate trajectory to guide the manipulator in obtaining the target while achieving obstacle avoidance and meeting joint constraints. The proposed algorithm is suitable for 3D space obstacle avoidance and multitarget path tracking.

scholarly journals Obstacle Avoidance and Multitarget Tracking of a Super Redundant Modular Manipulator Based on Bezier Curve and Particle Swarm Optimization

2020 ◽  
Vol 33 (1) ◽  
Author(s):  
Li Chen ◽  
Ying Ma ◽  
Yu Zhang ◽  
Jinguo Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Obstacle Avoidance ◽  
Trajectory Tracking ◽  
Degrees Of Freedom ◽  
Optimization Method ◽  
Bézier Curve ◽  
Bezier Curve ◽  
Redundant Manipulator ◽  
Swarm Optimization ◽  
Integrated Optimization

Abstract A super redundant serpentine manipulator has slender structure and multiple degrees of freedom. It can travel through narrow spaces and move in complex spaces. This manipulator is composed of many modules that can form different lengths of robot arms for different application sites. The increase in degrees of freedom causes the inverse kinematics of redundant manipulator to be typical and immensely increases the calculation load in the joint space. This paper presents an integrated optimization method to solve the path planning for obstacle avoidance and discrete trajectory tracking of a super redundant manipulator. In this integrated optimization, path planning is established on a Bezier curve, and particle swarm optimization is adopted to adjust the control points of the Bezier curve with the kinematic constraints of manipulator. A feasible obstacle avoidance path is obtained along with a discrete trajectory tracking by using a follow-the-leader strategy. The relative distance between each two discrete path points is limited to reduce the fitting error of the connecting rigid links to the smooth curve. Simulation results show that this integrated optimization method can rapidly search for the appropriate trajectory to guide the manipulator in obtaining the target while achieving obstacle avoidance and meeting joint constraints. The proposed algorithm is suitable for 3D space obstacle avoidance and multitarget path tracking.

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