Please do not disturb! Minimum interference coverage for social robots

Abstract

In this paper we address the problem of human-aware coverage planning.We first present an approach to learn and model human activity events in a probabilistic spatio-temporal map using spatial Poisson processes.We then propose a coverage planner for paths that minimize the interference probability with people.To this end, we pose the coverage problem as an asymmetric traveling salesman problem with timedependent costs (ATDTSP) derived from the information in the map.The approach enables a noisy robotic vacuum in a home scenario, for instance, to learn to avoid busy places at certain times of the day such as the kitchen at lunch time.We evaluate the planner using a simulator of people in a home environment to generate typical weekday activity patterns.In the experiments with a regular TSP planner and two modified TSP heuristics, the proposed coverage planner significantly reduces interference with people in terms of number of disturbed persons and overall disturbance time.