This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThis thesis presents the novel design of an ambidextrous robot arm that offers
double range of motion as compared to dexterous arms. The proposed arm is
unique in terms of design (ambidextrous feature), actuation (use of two different
actuators simultaneously: Pneumatic Artificial Muscle (PAM) & Electric Motor)) and
control (combined use of Proportional Integral Derivative (PID) with Neural Network
(NN) for the hand and modified Multiple Adaptive Neuro-fuzzy Inference System
(MANFIS) controller for the arm). The primary challenge of the project was to
achieve ambidextrous behavior of the arm. Thus, a feasibility analysis was carried out
to evaluate possible mechanical designs. The secondary aim was to deal with control
issues associated with the ambidextrous design. Due to the ambidextrous nature of
the design, the stability of such a device becomes a challenging task. Conventional
controllers and artificial intelligence-based controllers were explored to find the most
suitable one. Performances of all these controllers have been compared through
experiments, and combined use of PID with NN was found to be the most accurate
controller to drive the ambidextrous robot hand. 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, modified MANFIS 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 a conventional arm in terms of power
consumption and stability
