Dynamic Image-Based Visual Servoing of Monocular Camera Mounted Omnidirectional Mobile Robots Considering Actuators and Target Motion via Fuzzy Integral Sliding Mode Control

Lee, Sanghyob; Chwa, Dongkyoung

doi:10.1109/tfuzz.2020.2985931

Cited by 19 publications

(5 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…12 IBVS uses the error to calculate the joint angle, and the control information of the joint angle and the feedback information of the arm are used to calculate the control torque and drive the manipulator to the desired position. IBVS is robust and can be combined with various control methods, such as fuzzy control, 13 sliding mode control, 14 neural networks 15 and reinforcement learning. 16,17 IBVS also has some limitations, and it has also been studied in depth by researchers.…”

Section: Introductionmentioning

confidence: 99%

Image-based visual servoing with Kalman filter and swarm intelligence optimisation algorithm

Dong,

Li,

Wang

2024

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

The article proposes a new Kalman depth estimation and an improved swarm intelligence optimisation algorithm for adaptive tuning of servo gain for image-based visual servo control. First, a Kalman depth estimation model is established from the principle of image-based visual servoing, and two state equations are designed for depth estimation based on the number of state quantities. Second, the improved sparrow search algorithm is proposed to tune the servo gain adaptively to improve the convergence speed and stability. To verify the effectiveness of the proposed method, the conventional image-based visual servoing and conventional Kalman estimation are reproduced and compared with the proposed method, and the simulation is completed on the Simulink simulation platform for verification. Finally, the experiments are completed in the robotic arm experimental platform. Both the simulation and experimental results show the effectiveness of the proposed method, which reduces the redundancy of the camera and shortens the convergence time.

show abstract

Section: Introductionmentioning

confidence: 99%

Image-based visual servoing with Kalman filter and swarm intelligence optimisation algorithm

Dong,

Li,

Wang

2024

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

show abstract

“…Mobile robots have different configurations depending on the type of wheels used (drive, passive and omnidirectional) and design. For example, mobile robots with differential drive [1], [2], [3], [4], [5], [6] Ackermann steering drive [7], [8], [9], [10] and omnidirectional drive [11], [12], [13], [15], [16] [14].…”

Section: Introductionmentioning

confidence: 99%

ROS-based Controller for a Two-Wheeled Self-Balancing Robot

Díaz-Téllez,

García-Ramírez,

Pérez-Pérez

et al. 2023

Journal of Robotics and Control

View full text Add to dashboard Cite

In this article, a controller based on a Robot Operating System (ROS) for a two-wheeled self-balancing robot is designed. The proposed ROS architecture is open, allowing the integration of different sensors, actuators, and processing units. The low-cost robot was designed for educational purposes. It used an ESP32 microcontroller as the central unit, an MPU6050 Inertial Measurement Unit sensor, DC motors with encoders, and an L298N integrated circuit as a power stage. The mathematical model is analyzed through Newton-Euler and linearized around an equilibrium point. The control objective is to self-balance the robot to the vertical axis in the presence of disturbances. The proposed control is based on a bounded saturation, which is lightweight and easy to implement in embedded systems with low computational resources. Experimental results are performed in real-time under regulation, conditions far from the equilibrium point, and rejection of external disturbances. The results show a good performance, thus validating the mechanical design, the embedded system, and the control scheme. The proposed ROS architecture allows the incorporation of different modules, such as mapping, autonomous navigation, and manipulation, which contribute to studying robotics, control, and embedded systems.

show abstract

“…The second group focuses on the details of each task and aims at improving the robots' speed, accuracy, and stability [9][10][11]. Significant studies in the latter one have been published, in which several robot control algorithms were proposed, for instance: simultaneous localization and mapping (SLAM) [12], particle filter (PF) [13], Kalman filter (KF) [13,14], proportional integral derivative (PID) controller [15,16], and sliding mode [17]. Those algorithms are considered the backbone of robot rescue performance theory these days.…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

Geng

Chen

Nguyen

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

This paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

show abstract

Dynamic Image-Based Visual Servoing of Monocular Camera Mounted Omnidirectional Mobile Robots Considering Actuators and Target Motion via Fuzzy Integral Sliding Mode Control

Cited by 19 publications

References 35 publications

Image-based visual servoing with Kalman filter and swarm intelligence optimisation algorithm

Image-based visual servoing with Kalman filter and swarm intelligence optimisation algorithm

ROS-based Controller for a Two-Wheeled Self-Balancing Robot

Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

Contact Info

Product

Resources

About