Energy-based optimal step planning for humanoids

Step planning is becoming an increasingly important research topic for humanoid robots. Most cost functions for step planning in the literature are designed based on terrain information. The energy cost to perform each step action is usually ignored. In walking, energy consumption depends on gait features such as step length and width. In this paper, we use three simple and intuitive energy cost functions for different step lengths, widths, and the turning angle. These functions are inspired by literature on human walking energy analysis, and the function parameters are tuned to match computed costs for optimal humanoid walking motions obtained by simulation. The energy cost and the terrain cost are combined to obtain an optimal step planning sequence using A* search.