Pure Pursuit Guidance for Car-Like Ground Vehicle Trajectory Tracking

2017 ◽  
Author(s):  
Yuanyan Chen ◽  
J. Jim Zhu
Keyword(s):  
Trajectory Tracking ◽  
Degrees Of Freedom ◽  
Tracking Error ◽  
Path Following ◽  
Trajectory Design ◽  
Wheeled Mobile Robots ◽  
Design Algorithm ◽  
Pure Pursuit ◽  
Trajectory Tracking Controller ◽  
Self Driving Cars

Trajectory tracking guidance and control for nonholonomic (car-like) Autonomous Ground Vehicles (AGV), such as self-driving cars and car-like wheeled mobile robots, is a more challenging control problem than path following control, because the latter does not impose a speed requirement on the vehicle motion. The tracking error dynamics along the nominal path are nonlinear and time-varying in nature, which need to be exponentially stabilized. This paper presents a Line-of-Sight (LOS) Pure-Pursuit Guidance (PPG) trajectory design algorithm that generates a three Degrees of Freedom (DOF) spatial trajectory for an AGV equipped with a 3DOF trajectory tracking controller. The LOS PPG can be used for cooperative, passive (neutral) and adversarial tracking tasks, such as, respectively, formation driving, autonomous lane keeping with speed requirement, and chasing an evading vehicle. The algorithm is verified with computer simulations on a 1/6 scale electric car model, and will be further validated on that model car in the near future.

A self-tuning trajectory tracking controller for wheeled mobile robots

2019 ◽  
Vol 46 (6) ◽  
pp. 828-838 ◽  
Author(s):  
Pouya Panahandeh ◽  
Khalil Alipour ◽  
Bahram Tarvirdizadeh ◽  
Alireza Hadi
Keyword(s):  
Neural Networks ◽  
Convergence Rate ◽  
Mobile Robots ◽  
Trajectory Tracking ◽  
Suggested Method ◽  
Wheeled Mobile Robots ◽  
Trajectory Tracking Control ◽  
Content Type ◽  
Tracking Controller ◽  
Trajectory Tracking Controller

Purpose Trajectory tracking is a common problem in the field of mobile robots which has attracted a lot of attention in the past two decades. Therefore, besides the search for new controllers to achieve a better performance, improvement and optimization of existing control rules are necessary. Trajectory tracking control laws usually contain constant gains which affect greatly the robot’s performance. Design/methodology/approach In this paper, a method based on neural networks is introduced to automatically upgrade the gains of a well-known trajectory tracking controller of wheeled mobile robots. The suggested method speeds up the convergence rate of the main controller. Findings Simulations and experiments are performed to assess the ability of the suggested scheme. The obtained results show the effectiveness of the proposed method. Originality/value In this paper, a method based on neural networks is introduced to automatically upgrade the gains of a well-known trajectory tracking controller of wheeled mobile robots. The suggested method speeds up the convergence rate of the main controller.

Path Following for the Soft Origami Crawling Robot

2019 ◽  
Author(s):  
Oyuna Angatkina ◽  
Kimberly Gustafson ◽  
Aimy Wissa ◽  
Andrew Alleyne
Keyword(s):  
Autonomous Navigation ◽  
Degrees Of Freedom ◽  
Kinematic Model ◽  
Path Following ◽  
Search And Rescue ◽  
Soft Robots ◽  
Path Following Control ◽  
Straight Line ◽  
Pure Pursuit ◽  
Extensive Growth

Abstract Extensive growth of the soft robotics field has made possible the application of soft mobile robots for real world tasks such as search and rescue missions. Soft robots provide safer interactions with humans when compared to traditional rigid robots. Additionally, soft robots often contain more degrees of freedom than rigid ones, which can be beneficial for applications where increased mobility is needed. However, the limited number of studies for the autonomous navigation of soft robots currently restricts their application for missions such as search and rescue. This paper presents a path following technique for a compliant origami crawling robot. The path following control adapts the well-known pure pursuit method to account for the geometric and mobility constraints of the robot. The robot motion is described by a kinematic model that transforms the outputs of the pure pursuit into the servo input rotations for the robot. This model consists of two integrated sub-models: a lumped kinematic model and a segmented kinematic model. The performance of the path following approach is demonstrated for a straight-line following simulation with initial offset. Finally, a feedback controller is designed to account for terrain or mission uncertainties.

EXPERIMENTAL VERIFICATION STUDY ON TRAJECTORY TRACKING OF A NOVEL REHABILITATION EXOSKELETON SHOULDER JOINT

2019 ◽  
Vol 19 (07) ◽  
pp. 1940037
Author(s):  
YANYAN CHEN ◽  
LE LIANG ◽  
MAOCHUAN WU ◽  
YUE WANG ◽  
CAIYUN LIU
Keyword(s):  
Trajectory Tracking ◽  
Shoulder Joint ◽  
Inverse Dynamics ◽  
Control Method ◽  
Controller Design ◽  
Tracking Error ◽  
Shoulder Flexion ◽  
Nonlinear Control Processes ◽  
Trajectory Tracking Controller ◽  
Dynamics Models

The trajectory linearization control (TLC) was applied to design an autonomous nonlinear trajectory tracking controller for a novel rehabilitation exoskeleton shoulder joint in this paper. TLC is a relatively new control method which was applied in aircraft and mobile robot, which had good performance on real-time trajectory tracking, anti-jamming and universality. As a new application in the exoskeleton shoulder joint controller design, the controller in this research contained two loops that separately based on the inverse kinematics and pseudo-inverse dynamics models of the exoskeleton shoulder. Two PI controllers as the error regulator can reduce the tracking error. The position and angular velocity error feedback were employed to constitute the closed-loops. Since the controller was based on model and linearization, it can adapt to both linear and nonlinear control processes. The simulation of three different trajectories for single degree of freedom movement of shoulder joint (shoulder flexion and extension, abduction and extension), and the movement of both the two degrees were given. The simulation results showed that the TLC controller can follow the exoskeleton shoulder trajectory steadily and accurately.

Trajectory Tracking Controller Design for Mobile Robots with Bounded Input

2013 ◽  
Vol 427-429 ◽  
pp. 1145-1149
Author(s):  
Juan Wang ◽  
Xiu Feng Zhang
Keyword(s):  
Mobile Robots ◽  
Trajectory Tracking ◽  
Controller Design ◽  
Control Law ◽  
Robust Tracking ◽  
Wheeled Mobile Robots ◽  
Tracking Controller ◽  
Actuator Constraints ◽  
Trajectory Tracking Controller ◽  
Bounded Input

In this paper, the robust trajectory tracking problem has been addressed for nonholonomic wheeled mobile robots with dynamic uncertainties, disturbance and actuator constraints. control theory, LMI theory and principle of MPC are utilized to design robust tracking controller. Simulation is performed to highlight the effectiveness of the proposed control law.

scholarly journals An Adaptive Trajectory Tracking Controller of a Multi-joint Snake Robot Considering Non-holonomic Constraints

2021 ◽  
Author(s):  
Dongfang Li ◽  
Chao Wang ◽  
Hongbin Deng ◽  
Jie Huang
Keyword(s):  
Trajectory Tracking ◽  
Tracking Error ◽  
Environmental Parameters ◽  
Unknown Parameters ◽  
Snake Robot ◽  
Holonomic Constraints ◽  
Tracking Controller ◽  
Trajectory Tracking Controller ◽  
The Stability ◽  
Angle Tracking

Abstract Multi-joint snake robot is a vital reconnaissance, surveillance and attack weapon in national defence and military in the future. To study the trajectory tracking problem of a multi-joint snake robot with high redundancy and multi-degree of freedom in the plane, an adaptive trajectory tracking controller of a multi-joint snake robot considering non-holonomic constraints is proposed in this paper. The adaptive trajectory tracking controller replaces unknown parameters in the environment wi t h estimated values, which effectively solves the negative effects caused by uncertain and time-varying environmental parameters in the process of the robot movement and realizes the stability of the controller. Firstly, a new dynamical model of a multi-joint snake robot is established through coordinate transformation. Secondly, the control objective of the controller of the multi-joint snake robot is established. Thirdly, the proposed controller of the multi-joint snake robot is designed by the Backsteppi n g method to realize the control of the joint angle tracking error, link angle tracking error, actuator torque error and motion speed error of the robot. Then, a suitable Lyapunov function is found to verify the stability of the controller. Finally, through the MATLAB simulation and prototype experiment, the motion process of the multi-joint snake robot is observed, the trajectory tracking performance of the robot is analyzed, and the effectiveness of the adaptive trajectory tracking controller is verified.

scholarly journals Fuzzy swarm trajectory tracking control of unmanned aerial vehicle

2020 ◽  
Vol 7 (4) ◽  
pp. 435-447 ◽  
Author(s):  
Boumediene Selma ◽  
Samira Chouraqui ◽  
Hassane Abouaïssa
Keyword(s):  
Trajectory Tracking ◽  
Degrees Of Freedom ◽  
Fuzzy Inference ◽  
Control Method ◽  
Tracking Error ◽  
Vertical Plane ◽  
Pso Algorithm ◽  
Trajectory Tracking Control ◽  
Inference System ◽  
Quadrotor Uav

Abstract Accurate and precise trajectory tracking is crucial for unmanned aerial vehicles (UAVs) to operate in disturbed environments. This paper presents a novel tracking hybrid controller for a quadrotor UAV that combines the robust adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) algorithm. The ANFIS-PSO controller is implemented to govern the behavior of three degrees of freedom quadrotor UAV. The ANFIS controller allows controlling the movement of UAV to track a given trajectory in a 2D vertical plane. The PSO algorithm provides an automatic adjustment of the ANFIS parameters to reduce tracking error and improve the quality of the controller. The results showed perfect behavior for the control law to control a UAV trajectory tracking task. To show the effectiveness of the intelligent controller, simulation results are given to confirm the advantages of the proposed control method, compared with ANFIS and PID control methods.

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