User preferred behaviors for robot navigation exploiting previous experiences

Abstract

Industry demands flexible robots that are able to accomplish different tasks at different locations such as navigation and mobile manipulation. Operators often require mobile robots operating on factory floors to follow definite and predictable behaviors. This becomes particularly important when a robot shares the workspace with other moving entities. In this paper, we present a system for robot navigation that exploits previous experiences to generate predictable behaviors that meet user’s preferences. Preferences are not explicitly formulated but implicitly extracted from robot experiences and automatically considered to plan paths for the successive tasks without requiring experts to hard-code rules or strategies. Our system aims at accomplishing navigation behaviors that follow user’s preferences also to avoid dynamic obstacles. We achieve this by considering a probabilistic approach for modeling uncertain trajectories of the moving entities that share the workspace with the robot. We implemented and thoroughly tested our system both in simulation and on a real mobile robot. The extensive experiments presented in this paper demonstrate that our approach allows a robot for successfully navigating while performing predictable behaviors and meeting user’s preferences.