The design and control of autonomous intelligent mobile robotic systems operating in unstructured changing environments includes many objective difficulties. There are several studies about the ways in which, robots exhibiting some degree of autonomy, adapt themselves to fit in their environments. The application and use of bio-inspired and intelligent techniques such as reinforcement learning, artificial neural networks, evolutionary computation and so forth in the design and improvement of robot designs is an emergent research topic. Researchers have obtained robots that display an amazing slew of behaviours and perform a multitude of tasks. These include perception of environment, planning and navigation in rough terrain, pushing boxes, negotiating an obstacle course, etc.
In this context, mobile robots designed using evolutionary computation approaches, usually known as Mobile Evolutionary Robotics, have experienced significant development in the last decade. The fundamental goal of mobile evolutionary robotics is to apply evolutionary computation methods to automate the production of complex behavioural robotic controllers.
This volume offers a wide spectrum of sample works developed in leading research throughout the world about evolutionary mobile robotics and demonstrates the success of the technique in evolving efficient and capable mobile robots.