Biologically inspired deoxyribonucleic acid soft computing for inverse kinematics solver of five-DOF robotic manipulators

Huang, Hsu‐Chih; Hsu, Huan-Shiuan

doi:10.1007/s00500-014-1414-6

Cited by 5 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The inverse kinematics problem plays an important role in the research of robot dynamics and robot control, which directly affects the accuracy and rapidity of control. The inverse kinematics problem is to solve the corresponding joint variables according to the known position and posture of the end effector [16], [17]. The manipulator in this paper is designed to mimic the human arm.…”

Section: Inverse Kinematics Solutionmentioning

confidence: 99%

Grasping Control Method of Manipulator Based on Binocular Vision Combining Target Detection and Trajectory Planning

et al. 2019

View full text Add to dashboard Cite

In order to make the manipulator more accurately identify and grasp the target object, this paper proposes a manipulator control method based on binocular vision. First, the rotation relation between lever arms of manipulator is modeled by the D-H method and the model is simplified to improve the efficiency of the rotation operation. Then, apply the Canny edge detection algorithm based on binocular vision to identify and locate the target, and calculate the position of the target in the visual coordinate system. Next, the position of the target in the manipulator coordinate system is obtained according to the coordinate conversion. Finally, plan the movement trajectory of manipulator according to the inverse kinematics solution and motion trajectory planning algorithm, and drive the manipulator to capture the target. The experimental results show that the manipulator can accurately identify and capture the target with proposed control method.

show abstract

Section: Inverse Kinematics Solutionmentioning

confidence: 99%

Grasping Control Method of Manipulator Based on Binocular Vision Combining Target Detection and Trajectory Planning

et al. 2019

View full text Add to dashboard Cite

show abstract

“…[4][5][6] Several researchers have resolved the redundancy of an MMS on flat terrain by optimizing a cost function that maximizes the manipulability ellipsoid. [7][8][9] Several machine learning approaches are becoming increasingly popular in solving problems that are difficult to solve using traditional methods. Biondi and Prati 10 have proposed a method to tune the parameters of an SVM by means of transfer learning.…”

Section: Introductionmentioning

confidence: 99%

Kinematics-based end-effector path control of a mobile manipulator system on an uneven terrain using a two-stage Support Vector Machine

Jangid¹,

Jain²,

Teka³

et al. 2019

Robotica

View full text Add to dashboard Cite

SUMMARYA mobile manipulator system (MMS) consists of a robotic arm mounted on a mobile platform that is used in rescue and relief, space exploration, warehouse automation, etc. As the total system has 14 Degrees of Freedom (DOF), it does not have a closed-form inverse kinematics (IK) solution. A learning-based method is proposed, which uses the forward kinematics data to learn the IK relation for motion of an MMS on a rough terrain, using a one-class support vector machine (SVM) framework. Once trained, the model estimates the joint probability distribution of the MMS configuration and end-effector position. This distribution is used to find the MMS configuration for a given desired end-effector path. Past research using a Kohonen Self organizing map (KSOM) neural network-based open-loop control method has shown that the MMS deviates from its desired path while moving on an uneven terrain due to unknown disturbances such as wheel slip, slide, and terrain deformation. Therefore, a new sequential two-stage SVM-based end-effector path-tracking control scheme is proposed to control the end-effector path. In this scheme, the error in the end-effector path is continuously tracked with the help of a Microsoft Kinect 2.0 (Microsoft Regional Sales, Singapore 119968) and is sent as a feedback to the controller. Once the error reaches a threshold value, the error correction step of the controller gets activated to correct the error until the desired accuracy is reached. The effectiveness of the proposed approach is proved through extensive simulations and experiments conducted on 3D terrain in which it is shown that the end effector can follow the desired path with an average experimental error of around 2 cm between the desired and final corrected path.

show abstract

Implication of MFO for Control of 3-link Robotic Manipulator Used for Casting Process

Jangid,

Kumar,

Singh

2024

Lecture Notes in Mechanical Engineering

View full text Add to dashboard Cite

Biologically inspired deoxyribonucleic acid soft computing for inverse kinematics solver of five-DOF robotic manipulators

Cited by 5 publications

References 15 publications

Grasping Control Method of Manipulator Based on Binocular Vision Combining Target Detection and Trajectory Planning

Grasping Control Method of Manipulator Based on Binocular Vision Combining Target Detection and Trajectory Planning

Kinematics-based end-effector path control of a mobile manipulator system on an uneven terrain using a two-stage Support Vector Machine

Implication of MFO for Control of 3-link Robotic Manipulator Used for Casting Process

Contact Info

Product

Resources

About