Decision-Theoretical Navigation of Service Robots Using POMDPs with Human-Robot Co-Occurrence Prediction

Abstract

To improve the natural human-avoidance skills of service robots, a human motion predictive navigation method is proposed, namely PN-POMDP. A human-robot motion co-occurrence estimation algorithm is proposed which incorporates long-term and short-term human motion prediction. To improve the reliability of probabilistic and predictive navigation, the POMDP model is utilized to generate navigation control policies through theoretically optimal decisions. A layered motion control structure is proposed that combines global path planning and reactive avoidance. Multiple comity policies are integrated with a decision-making module that generates efficient and human-compliant navigational behaviours for robots. Experimental results illustrate the effectiveness and reliability of the predictive navigation method.