Using genetic algorithms to establish efficient walking gaits for an eight-legged robot

In the design and development of a legged robot, many factors need to be considered. As a consequence, creating a legged robot that can efficiently and autonomously negotiate a wide range of terrains is a challenging task. Many researchers working in the area of legged robotics have traditionally looked towards the natural world for inspiration and solutions, reasoning that these evolutionary solutions are appropriate and effective because they have passed the hard tests for survival over time and generations. This paper reports the use of genetically inspired learning strategies, commonly referred to as genetic algorithms, as an evolutionary design tool for improving the design and performance of an algorithm for controlling the leg stepping sequences of a walking robot. The paper presents a specific case of finding optimal walking gaits for an eight-legged robot called Robug IV and simulated results are provided.