Real time $A^$ adaptive action set footstep planning with human locomotion energy approximations considering angle difference for heuristic function

Abstract

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 bipedal robot’s movement. 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.