Evolutionary Strategies Combined With Novel Binary Hill Climbing Used for Online Walking Pattern Generation in Two Legged Robot

Evolutionary algorithms (EA) has often been proposed as a method for designing system

Genetic Learning Algorithms Combined With Novel Binary Hill Climbing Used for Online Walking-Pattern Generation in Legged Robots

According to Darwin every species on this planet have developed froma small group of simple molecules into all the modern species living among us today. The reason why some species survive and others don’t is what Darwin called Natural Selection, which means that every individual have to fight for its existence. Those who are best fit will survive. This has brought life to the well known saying: "Survival of the Fittest". The best fit will have the best chance to reproduce, to pass its well fitted, surviving qualities on to their offspring. And the offspring of two well-equipped parents will have a high probability of adaptation, and so the circle of life goes on... A set of evolutionary search methods have been extracted from the Darwinian theories of evolution. These have been evolving in computer environments for several decades and have been passing through different areas of computer science, from theoretical tuning problems, algorithm developing, clustering, chip design, and several real world applications have been the foci the last years. In this thesis Genetic Algorithms and Evolvable Hardware is used for evolving gaits in a walking biped robot controller. The focus is fast learning in a real-time environment. An incremental approach combining a genetic algorithm with hill climbing is proposed. This combination interacts in an efficient way to generate precise walking patterns in less than 15 generations. Our proposal is compared to various versions of Genetic Algorithms and stochastic search, and finally tested on a pneumatic biped walking robot

Genetically evolved dynamic control for quadruped walking

The aim of this dissertation is to show that dynamic control of quadruped locomotion is achievable through the use of genetically evolved central pattern generators. This strategy is tested both in simulation and on a walking robot. The design of the walker has been chosen to be statically unstable, so that during motion less than three supporting feet may be in contact with the ground. The control strategy adopted is capable of propelling the artificial walker at a forward locomotion speed of ~1.5 Km/h on rugged terrain and provides for stability of motion. The learning of walking, based on simulated genetic evolution, is carried out in simulation to speed up the process and reduce the amount of damage to the hardware of the walking robot. For this reason a general-purpose fast dynamic simulator has been developed, able to efficiently compute the forward dynamics of tree-like robotic mechanisms. An optimization process to select stable walking patterns is implemented through a purposely designed genetic algorithm, which implements stochastic mutation and cross-over operators. The algorithm has been tailored to address the high cost of evaluation of the optimization function, as well as the characteristics of the parameter space chosen to represent controllers. Experiments carried out on different conditions give clear indications on the potential of the approach adopted. A proof of concept is achieved, that stable dynamic walking can be obtained through a search process which identifies attractors in the dynamics of the motor-control system of an artificial walker

Harnessing Mother Nature: Optimizing Genetic Algorithms for Adaptive Systems

AbstractMany adaptive systems require optimization in real time. Whether it is a robot that must maintain its gait regardless of the terrain or multicore systems needing proper scheduling, optimization is of utmost importance. With hundreds of processes created and evaluated every second, real-time performance optimization is a monumental task. Mother nature has proven that evolution is very effective form of adaptation. Through a stochastic search, i.e. GA, computers harness this power. GAs have been developed to utilize many different parameters, which have a significant effect on the efficiency and effectiveness of a GA. If a GA tasked to optimize these parameters, the result is a rapid and automatic optimization. To test our hypothesis we optimize a GA that solves common optimization functions. The GA's effectiveness is determined by the time it takes to find the solution. Cross validation is utilized, and shows an average 947% performance improvement on training sets and 440% on testing sets. This large improvement in the testing sets shows that an optimized genetic algorithm remains general enough to effectively solve similar problems

Real time evolutionary algorithms in robotic neural control systems.

This thesis describes the use of a Real-Time Evolutionary Algorithm (RTEA) to optimise an Artificial Neural Network (ANN) on-line (in this context on-line means while it is in use). Traditionally, Evolutionary Algorithms (Genetic Algorithms, Evolutionary Strategies and Evolutionary Programming) have been used to train networks before use - that is off-line, as have other learning systems like Back-Propagation and Simulated Annealing. However, this means that the network cannot react to new situations (which were not in its original training set). The system outlined here uses a Simulated Legged Robot as a test-bed and allows it to adapt to a changing Fitness function. An example of this in reality would be a robot walking from a solid surface onto an unknown surface (which might be, for example, rock or sand) while optimising its controlling network in real-time, to adjust its locomotive gait, accordingly. The project initially developed a Central Pattern Generator (CPG) for a Bipedal Robot and used this to explore the basic characteristics of RTEA. The system was then developed to operate on a Quadruped Robot and a test regime set up which provided thousands of real-environment like situations to test the RTEAs ability to control the robot. The programming for the system was done using Borland C++ Builder and no commercial simulation software was used. Through this means, the Evolutionary Operators of the RTEA were examined and their real-time performance evaluated. The results demonstrate that a RTEA can be used successfully to optimise an ANN in real-time. They also show the importance of Neural Functionality and Network Topology in such systems and new models of both neurons and networks were developed as part of the project. Finally, recommendations for a working system are given and other applications reviewed