Evolutionary Robotics - Design and Control Strategies

Abstract

Evolutionary Robotics (ER) is a field at the intersection of robotics and evolutionary computation that aims to design and control robots using evolutionary algorithms. This paper provides a comprehensive review of design and control strategies in ER, highlighting the key concepts, methodologies, and applications. We begin by introducing the fundamental principles of ER, including genotype-phenotype mapping, fitness evaluation, and evolutionary algorithms. We then discuss various design strategies, such as modular robotics, morphogenetic robotics, and soft robotics, and their applications in different domains. Next, we delve into control strategies, including neural networks, genetic programming, and direct encoding, focusing on their implementation and effectiveness in evolving robot behaviors. Finally, we examine current challenges and future directions in ER, including scalability, robustness, and real-world deployment. This paper aims to provide researchers and practitioners with a comprehensive understanding of design and control strategies in ER, fostering further advancements in this exciting field.