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This book 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) andcontrol (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.…mehr

Produktbeschreibung
This book 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) andcontrol (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.
Autorenporträt
Dr. Mashood Mukhtar received his Master of Engineering degree from Queen Mary, University of London, MBA (Finance) from University of Gloucestershire, UK and PhD in Robotics and Artificial Intelligence from Brunel University, London.  His research interests include Artificial Intelligence, Robotics and Machine Learning.