Near-optimal gait generations of a two-legged robot on rough terrains using soft computing

A two-legged robot will have to generate its near-optimal gaits after ensuring maximum dynamic balance margin and minimum power consumption, while moving on the rough terrains containing some staircases and sloping surfaces. Moreover, the changes of joint torques should lie below a pre-specified small value to ensure its smooth walking. The balance of the robot and its power consumption are also dependent on hip trajectory and position of the masses on various limbs. Both neural network- and fuzzy logic-based gait planners have been developed for the same, the training of which are provided using a genetic algorithm off-line. Once optimized, the planners are found to generate optimal gaits of the two-legged robot successfully for the test cases.