  The problem of navigating a bipedal robot to a desired destination in various
environments is very important. However, it is very difficult to solve the
navigation problem in real time because the computation time is very long due
to the nature of the biped robot having a high degree of freedom. In order to
overcome this, many scientists suggested navigation through the footstep
planning. Usually footstep planning use the shortest distance or angles as the
objective function based on the A * algorithm. Recently, the energy required
for human walking, which is widely used in human dynamics, approximated by a
polynomial function is proposed as a better cost function that explains the
movement of the bipedal robot. In addition, for the real time navigation, using
the action set of the A * algorithm not fixed, but the number changing
according to the situation, so that the computation time does not increase much
and the methods of considering the collision with the external environment are
suggested as a practical method. In this thesis, polynomial function
approximating the energy required for human walking is adopted as a cost
function, and heuristic function considering the angular difference between the
robot and the destination which is not shown in the previous studies is newly
proposed and proved. In addition, a new method to integrate the adaptive
behavior set and energy related to human walking is proposed. Furthermore,
efficient collision avoidance method and a method to reduce the local minimum
problem is proposed in this framework. Finally, footstep planning algorithm
with all of these features into the mapping algorithm and the walking algorithm
to solve the navigation problem is validated with simulation and real robot.
