An evolutionary approach to skillful motion control of industrial robots

This thesis addresses issues related to evolution of skillful motions of industrial robots. It is motivated by the increasing need to have more intelligent and autonomous robots to improve the flexibility and robustness of factory automation systems in uncertain work environments. The thesis focuses on two problems: 1) evolutionary optimization in changing fitness landscapes and environments, and 2) synthesis of skillful behavior in robots, based on natural phenomena in motor skill acquisition together with the implications of human morphology to skillful manipulation. A considerable weight is given to learning from nature and its robust dynamics to evolve smart species like humans and human-like manipulation skills. The first chapter gives an introduction of the background, motivation and the overview of the thesis. The second chapter presents a novel evolutionary algorithm to optimize both in static as well as dynamic multi-modal fitness landscapes. The characteristic features of how the nature sustains a robust evolutionary process in changing environmental conditions are discussed and modeled in a simple way. It is shown through mathematical and empirical evidences that the new evolutionary algorithm performs better than conventional methods. Redundant manipulators are best suited for human-like manipulation tasks due to their morphological similarity with the human arm. Yet, identification of dynamics and model-based control of redundant manipulators is a significant problem due to the volume of calculations and the resulting processing burden. In the third chapter, this thesis presents a very effective alternative, based on Runge–Kutta–Gill neural networks (RKGNNs). The generalizability of the adopted design principles makes it very effective in real world applications. Furthermore, the mechanism found in the human motor system to learn complex skills by adaptive combination of motor primitives is treated as a basis to develop a novel model based control method for multi-link manipulators in the fourth chapter. Based on the view that intelligence is an effect of some underlying causes and conditions in the biology of the brain, the thesis hypothesizes that the level of motor skills of a human is as strong as the comprehensiveness of the internal evaluation functions pertaining to state dependant actions. In the fifth chapter, this thesis emphasizes the importance of constructing a comprehensive set of multi-objective evaluation functions to evolve skillful behaviors of a robot. The idea is demonstrated through an application of a mobile robot navigating in an uncertain environment. An important part of intelligence related to human arm motions is that humans make full use of the redundancy of the arm while performing skillful tasks. In the sixth chapter, this thesis presents a novel configuration controller optimized using an evolutionary approach for redundancy resolution of manipulators. A gradually growing multi-objective fuzzy set based objective function is designed so that it prioritizes objectives and starts with high priority objectives and goes on adding auxiliary objectives over the generations of the evolutionary process. This kind of a gradually growing multi-objective problem (MOP) is supposed to be very similar to the natural process of evolving complex systems starting from simple structures. In the seventh chapter, the importance of this approach to extend the dexterity and reliability of teleoperated manipulators is experimentally demonstrated. Finally a discussion on the advantages, limitations and drawbacks of the proposed methods is given in the eighth chapter. The experiments to demonstrate the