Abstract
The current market's demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an alternative way to design this kind of system based on decentralized control using distributed, autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually do not consider true adaptation and re-configuration. Understanding how, in nature, complex things are performed in a simple and effective way allows us to mimic nature's insights and develop powerful adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufacturing systems. The paper provides an overview of some of the principles found in nature and biology and analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to solve complex engineering problems, especially in the manufacturing field. An industrial automation case study is used to illustrate a bio-inspired method based on potential fields to dynamically route pallets.

Abstract
This paper describes and compares several approaches applied to compute the inverse kinematics of a 10 degrees of freedom hyper-redundant robot. The proposed approaches are based on an exhaustive method and several error optimization algorithms. The algorithms' performance was evaluated based on two criteria: computing time and final actuator positioning error. The mentioned hyper-redundant robot was projected to be used in biomedical applications. © 2012 IEEE.