Mobile Robots: The Evolutionary ApproachMobile robotic is a recent ?eld that has roots in many engineering and science disciplines such as mechanical, electrical, mechatronics, cognitive and social sciences just to name few. A mobile robot needs e?cient mechanisms of lo- motion, kinematics, sensors data, localization, planning and navigation that enable it to travel throughout its environment. Scientists have been fascinated by conception of mobile robots for many years. Machines have been designed withwheelsandtracksorotherlocomotion devicesand/orlimbs topropelthe unit. When the environment is well ordered these machines can function well. Mobile robots have demonstrated strongly their ability to carry out useful work. Intelligent robots have become the focus of intensive research in the last decade. The ?eld of intelligent mobile robotics involves simulations and re- world implementations of robots which adapt themselves to their partially unknown, unpredictable and sometimes dynamic environments. The design and control of autonomous intelligent mobile robotic systems operatinginunstructuredchangingenvironmentsincludesmanyobjectived- ?culties. There are several studies about the ways in which, robots exhibiting some degree of autonomy, adapt themselves to ?t in their environments. The application and use of bio-inspired techniques such as reinforcement lea- ing, arti?cial neural networks, evolutionary computation, swarm intelligence and fuzzy systems in the design and improvement of robot designs is an em- gentresearchtopic. Researchershaveobtainedrobotsthatdisplayanamazing slew of behaviours and perform a multitude of tasks. |
Contents
Differential Evolution Approach Using Chaotic Sequences | 3 |
1 | 11 |
References | 19 |
2 | 25 |
References | 44 |
3 | 52 |
References | 61 |
2 | 70 |
2 | 91 |
C++ Class EvolvingClassifier | 115 |
Modulebased Autonomous Learning for Mobile Robots | 136 |
References | 157 |
References | 183 |
References | 199 |
References | 218 |
References | 85 |
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Common terms and phrases
AAREACT action adaptive agent aggregate fitness function aggregate selection Aifi applied approach architecture autonomous robot ball behavior blue team chromosome classifier CMAC cognitive layer comparison for Task complex coordination crossover data point defined differential evolution dynamic encoding environment evaluation evolutionary algorithm Evolutionary Computation evolutionary robotics evolved controllers experiment Feature-based fitness function fuzzy rule Fuzzy Variable genetic algorithm goal hybrid IEEE IEEE International Conference implemented incidence matrix initial input landmark recognition learned Policy learning algorithm Machine Learning mapping method mobile robots module motion planning multi-robot mutation navigation neural network Number of Steps object obstacles operators optimization parameters path planning performance Policies comparison population problem Proceedings Q-learning quadtree reactive real robots real-time reinforcement learning representation robot controllers robot of blue robot soccer robotic fish robotic systems Robotics and Automation simulation situation solution space state-based strategy swim pattern tion updated vector