Planning humanlike actions in blending spaces

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 time-dependent 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.