An Intelligent Color Image Recognition and Mobile Control System for Robotic Arm

Yao, Albert Wen Long; Chen, H. C.

doi:10.31763/ijrcs.v2i1.557

Cited by 8 publications

(7 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given this kinematics, an optimization algorithm can then be applied to accurately determine the location of the robot's S/P axis to help the foot hold its own natural motion during I/E. One common method for obtaining this kinematics is to use motion capture systems [42], [43].…”

Section: B the Proposed Robotmentioning

confidence: 99%

A New 4-DOF Robot for Rehabilitation of Knee and Ankle-Foot Complex: Simulation and Experiment

Alipour¹,

Mahjoob

Fakhari³

et al. 2022

Journal of Robotics and Control

View full text Add to dashboard Cite

Stationary robotic trainers are lower limb rehab robots which often incorporate an exoskeleton attached to a stationary base. The aim is to recover range of motion, increase muscles strength and reduce joint stiffness. The issue observed in the stationery trainers which involve knee and ankle-foot complex joints simultaneously is that they restrict the natural motion of ankle-foot in rehab trainings due to the insufficient Degrees of Freedom (DOFs) of these trainers. This restriction makes the joints deviate from their natural motion patterns, exerting potentially harmful forces and strain the joints. In this work, we propose a new stationary knee-ankle-foot rehab robot with all necessary DOFs including knee flexion/extension in addition to ankle plantarflexion/dorsiflexion, ankle-foot abduction-adduction and foot supination/pronation. Axes of motions are determined based on the kinematics of the joints. A typical rehab training exercise has been considered to evaluate the system performance. The chosen training (i.e., passive assistance) was first implemented in simulation to synthesize the control loop and to extract the parameters required for selecting the robot’s components. The robot was then fabricated and tested on a healthy subject. Results showed that all natural motions of the ankle-foot complex were generated during the training. Therefore, the proposed system fulfils the desired aim properly, so that it can be utilized in the design of rehab robots.

show abstract

Section: B the Proposed Robotmentioning

confidence: 99%

A New 4-DOF Robot for Rehabilitation of Knee and Ankle-Foot Complex: Simulation and Experiment

Alipour¹,

Mahjoob

Fakhari³

et al. 2022

Journal of Robotics and Control

View full text Add to dashboard Cite

show abstract

“…Smart home adoption has a number of noteworthy benefits that can be realized. Some of these benefits can be combined with the ensuing arguments [35][36][37][38][39][40][41][42].…”

Section: A Benefit From Smart Homesmentioning

confidence: 99%

Design and Development of Control and Management Systems for Smart Homes

Baballe¹

2022

IJRMEE

View full text Add to dashboard Cite

The smart home and management system in this study are powered by D.C. batteries. Before running out of power, a fully charged battery can last for two to three days. When you turn on the system, the maximum temperature and the name of the study are shown on the LCD. Additionally, it will demonstrate that no one is in the house. As more individuals enter the house after you click the push button to enter, it will show on the Liquid Crystal Display (LCD) that you are welcome and that there are now more people inside. You get to decide how many people you let inside the residence. If you set the limit to 12, the door won't open again until someone quits the building by pressing the exit button before another person is allowed to enter. You can pick when you want the fan to turn on automatically by setting the maximum temperature. The fan in the house is used to lower the temperature if the weather is hot. The fan won't turn on until the maximum temperature of 30 °C has been reached, for example. Additionally, if everyone leaves the house, the fan and light will turn off automatically because nobody is inside. For safety purposes, there is a light in front of the house. It will automatically turn on if it becomes dark outside at night. The second LDR outside the home is in charge of controlling this light. The exterior light will turn off during the day if the LDR detects light, and it will turn on automatically at night if it detects dark. This is done for security reasons. Within the residence is the first LDR. It will switch on the lights when someone enters the house. No one will be able to exit the house before the light is turned off.

show abstract

“…At this time, humans or robots are considered movable obstacles that form the dynamic environment. Since then, the solution for the "Navigation" problem, which is based on camera application [11][12][13][14][15][16], has attracted much interest as it can surmount the limitations of tradition line detecting method and also provides the ability to optimize the path. Moreover, the working area is usually a warehouse or limited area, so the robot's ability to operate must be flexible and can run according to the predetermined trajectory and avoid moving obstacles that may appear without leaving from safe planning trajectory [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Path Following and Avoiding Obstacle for Mobile Robot Under Dynamic Environments Using Reinforcement Learning

Hanh

Cong

2023

Journal of Robotics and Control

View full text Add to dashboard Cite

Obstacle avoidance for mobile robot to reach the desired target from a start location is one of the most interesting research topics. However, until now, few works discuss about working of mobile robot in the dynamic and continuously changing environment. So, this issue is still the research challenge for mobile robots. Traditional algorithm for obstacle avoidance in the dynamic, complex environment had many drawbacks. As known that Q-learning, the type of reinforcement learning, has been successfully applied in computer games. However, it is still rarely used in real world applications. This research presents an effectively method for real time dynamic obstacle avoidance based on Q-learning in the real world by using three-wheeled mobile robot. The position of obstacles including many static and dynamic obstacles and the mobile robot are recognized by fixed camera installed above the working space. The input for the robot is the 2D data from the camera. The output is an action for the robot (velocities, linear and angular parameters). Firstly, the simulation is performed for Q-learning algorithm then based on trained data, The Q-table value is implemented to the real mobile robot to perform the task in the real scene. The results are compared with intelligent control method for both static and dynamic obstacles cases. Through implement experiments, the results show that, after training in dynamic environments and testing in a new environment, the mobile robot is able to reach the target position successfully and have better performance comparing with fuzzy controller.

show abstract

An Intelligent Color Image Recognition and Mobile Control System for Robotic Arm

Cited by 8 publications

References 29 publications

A New 4-DOF Robot for Rehabilitation of Knee and Ankle-Foot Complex: Simulation and Experiment

A New 4-DOF Robot for Rehabilitation of Knee and Ankle-Foot Complex: Simulation and Experiment

Design and Development of Control and Management Systems for Smart Homes

Path Following and Avoiding Obstacle for Mobile Robot Under Dynamic Environments Using Reinforcement Learning

Contact Info

Product

Resources

About