Bipedal humanoid robot walking reference tuning by the use of evolutionary algorithms

Official URL: http://192.168.1.20/record=b1493834 (Table of Contents)

Various aspects of humanoid robotics attracted the attention of researchers in the past four decades. One of the most challenging tasks in this area is the control of bipedal locomotion. The dynamics involved are highly nonlinear and hard to stabilize. A typical fullbody humanoid robot has more than twenty joints and the coupling effects between the links are significant. Reference generation plays a vital role for the success of the walking controller. Stability criteria including the Zero Moment Point (ZMP) criterion are extensively applied for this purpose. However, the stability criteria are usually applied on simplified models like the Linear Inverted Pendulum Model (LIPM) which only partially describes the equations of the motion of the robot. There are also trial and error based techniques and other ad-hoc reference generation techniques as well. This background of complicated dynamics and difficulties in reference generation makes automatic gait (step patterns of legged robots) tuning an interesting area of research. A natural command for a legged robot is the velocity of its locomotion. A number of walk parameters including temporal and spatial variables like stepping period and step size need to be set properly in order to obtain the desired speed. These problems, when considered from kinematics point of view, do not have a unique set of walking parameters as a solution. However, some of the solutions can be more suitable for a stable walk, whereas others may lead to instability and cause robot to fall. This thesis proposes a gait tuning method based on evolutionary methods. A velocity command is given as the input to the system. A ZMP based reference generation method is employed. Walking simulations are performed to assess the fitness of artificial populations. The fitness is measured by the amount of support the simulated bipedal robot received from torsional virtual springs and dampers opposing the changes in body orientation. Cross-over and mutation mechanisms generate new populations. A number of different walking parameters and fitness functions are tested to improve this tuning process. The walking parameters obtained in simulations are applied to the experimental humanoid platform SURALP (Sabanci University ReseArch Labaratory Platform). Experiments verify the merits of the proposed reference tuning method.