Axis manipulation to solve Inverse Kinematics of Hyper-Redundant Robot in 3D Space

Abstract

A solution based on inverse kinematics is required for the robot's end effector, also known as its tip, to reach a target. Current methods for solving the inverse kinematics solution for a hyper-redundant robot in three 3D are generally complex, difficult to visualize, and time-intensive. This requires the development of new algorithms for solving inverse kinematics in a quicker and more efficient manner. In this study, an axis manipulation using a geometrical approach is used. Initially, a general algorithm for a 2 m-link hyper-redundant robot in 3D is generated. The method employed a repetitive basic inverse kinematics solution of a two-link robot on virtual links. The virtual links are generated using a specific geometric proposition. Finally, the 3D solution is generated by rotating about the global z-axis. This method reduces the mathematical complexity required to solve the inverse kinematics solution for a 2-m-link robot. In addition, this method can manage variable link manipulators, thereby eliminating singularity. To demonstrate the effectiveness of the model, numerical simulations of hyper redundant models in 3D are presented. This new geometric approach is anticipated to enhance the performance of hyper-redundant robots, enabling them to be of greater assistance in fields such as medicine, the military, and search and rescue.

Collision avoidance of mobile robot using Alexnet and NVIDIA Jetson Nano B01

“…The torchvision.transform package is used to preprocess the picture data. Torchvision is a PyTorch package that includes CNN and transfer learning models [24] [25]. After the model has been trained, the model's performance is evaluated in a test environment.…”

Solution And Visualization 3D Plane Inverse Kinematics Method

“…The end effector's position is (5, 1, -2) with a unit length of 0.5 at the 16th elbow, and there are also positions of the end effector at (2.5, 1.3, -0.5) with various units of length at the elbows of 4 HRR robots. The picture appears to show that both negative and positive quadrants can be implemented in 3-dimensional space (Bostan et al, 2016), and that simulations performed from various elbows also accommodate down and up elbow configurations (Winston & Jamali, 2022). The test also consistently formed coils at the 4th and 8th elbows on the HRR (Rosheim, 1994).…”