A human-aware navigation method for social robot based on multi-layer cost map

Most of the current human-aware navigation methods of service robots focus on improving the reactive navigation of local path planning without considering the global environment. A global path planning method is proposed based on the global scope of pedestrian perception and multi-layer cost-maps. Firstly, personal space and group interaction are modeled as social cost based on pedestrian perception, and then multi-layer dynamic cost maps are generated containing the social cost at different time-steps based on pedestrian trajectory prediction, which provides social constraints for global path planning. Secondly, the global path planner searches for the optimal state with heuristic cost function based on the multi-layer dynamic cost-maps. Considering the huge calculation of heuristic search and the limitation of the length of trajectory prediction duration, the ‘plan-prediction-execution’ cycle is introduced for the dynamic planning, which improves performance in the dynamic environment. Finally, compared with the traditional path planner in the simulation scenes including pedestrian movements and group interaction, the experimental results show that the path length, the execution time is shorter, and the comfort distance of the person/group is more social in our method. Through the actual scene experiments, the advantages of handling situations of planning timeout and adjusting trajectories dynamically after introducing the ‘plan-prediction-execution’ cycle are verified, which can meet the comfort and society of human-aware navigation.