Abstract
This paper presents the novel design of an ambidextrous robot arm that offers double range of motion as compared to dexterous arms. The arm is unique in terms of design (ambidextrous feature), actuation (use of two different actuators simultaneously: Pneumatic Artificial Muscle (PAM) and Electric Motors)) and control (combined use of Proportional Integral Derivative (PID) with Neural Network (NN) and Multiple Adaptive Neuro-fuzzy Inference System (MANFIS) controller with selector block). In terms of ambidextrous robot arm control, a solution based on forward kinematic and inverse kinematic approach is presented, and results are verified using the derived equation in MATLAB. Since solving inverse kinematics analytically is difficult, Adaptive Neuro Fuzzy Inference system (ANFIS) is developed using ANFIS MATLAB toolbox. When generic ANFIS failed to produce satisfactory results due to ambidextrous feature of the arm, MANFIS with a selector block is proposed. The efficiency of the ambidextrous arm has been tested by comparing its performance with a conventional robot arm. The results obtained from experiments proved the efficiency of the ambidextrous arm when compared with conventional arm in terms of power consumption and stability.
Highlights
A robot arm plays an important role in determining a robot’s capability as most of the tasks require some kind of end-effector to complete the task
Using the Denavit Hardenberg (DH) convention, θi describes joint angle of xi axis relative to xi−1 axis defined according to the right-hand rule about Zi−1 axis, distance from the origin is denoted by di of the i − 1 axes to the intersection of the Zi−1 axis with the xi axis and measured along the Zi−1 axis, ai is minimum distance between Zi−1 and Zi and αi describes an offset angle of Zi axis relative to Zi−1 axis measured about the xi axis using right-hand rule to obtain the forward kinematics transformation matrix Tn0 based on homogenous transformations and DH convention
The inverse kinematic problem was discussed in great detail
Summary
A robot arm plays an important role in determining a robot’s capability as most of the tasks require some kind of end-effector to complete the task. When a robot picks up tools of uncertain lengths, orientations, or gripping points, the overall kinematics becomes uncertain and changes according to different tasks To overcome this problem, a new adaptive Jacobian tracking controller for robots with uncertain kinematics and dynamics is presented, and experimental results justify the performance of the proposed controller in [18]. In [20], the authors investigated the implementation of inverse kinematics and a servo controller for a robot manipulator using a Field Programmer Gate Array (FPGA) They have evaluated the performance of the proposed circuit design through an experimental system that consisted of the FPGAbased motion controller and a Mitsubishi RV-M1 micro-robot and collected the experimental results to evaluate correctness and effectiveness.
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