Approach toward footstep planning considering the walking period: Optimization‐based fast footstep planning for humanoid robots

Abstract

This paper proposes the necessity of a walking period in footstep planning and details situations in which it should be considered. An optimization‐based fast footstep planner that takes the walking period into consideration is also presented. This footstep planner comprises three stages. A binary search is first used to determine the walking period. The front stride, side stride, and walking direction are then determined using the modified rapidly‐exploring random tree algorithm. Finally, particle swarm optimization (PSO) is performed to ensure feasibility without departing significantly from the results determined in the two stages. The parameters determined in the previous two stages are optimized together through the PSO. Fast footstep planning is essential for coping with dynamic obstacle environments; however, optimization techniques may require a large computation time. The two stages play an important role in limiting the search space in the PSO. This framework enables fast footstep planning without compromising on the benefits of a continuous optimization approach.

Keywords

• continuous footstep planning
• humanoid navigation
• modified rapidly‐exploring random tree algorithm
• particle swarm optimization
• walking period