Congestion-Aware Robot Tour Planning in Crowded Environments
This work addresses the problem of efficient robot navigation in crowded human environments, which is important for service robots in public spaces.
The paper presents a probabilistic tour planner for crowded environments that uses learned human trajectory predictions to build and solve a Markov decision process online, reducing robot travel time by up to 30% compared to baselines in a real-world shopping mall dataset.
Autonomous mobile service robots are often required to complete tours that require navigating through a set of locations in an environment. Example domains include guiding people through a shopping mall, delivering packages in a fulfilment centre, or giving guided tours in a museum. However, in crowded environments, the presence of people may negatively impact robot performance. For example, humans will activate robot collision avoidance manoeuvres that slow the robot down. Crowds move stochastically and vary throughout the day. In this paper we present a probabilistic tour planner for crowded environments which explicitly reasons over human congestion. We learn circular linear flow field (CLiFF) maps which predict human trajectories given an initial observation. We then use these predictions to build and solve a Markov decision process online which efficiently routes the robot through the environment. Our approach is scalable enough to re-plan as new people are observed. We evaluate our approach on a real-world crowd dataset in a shopping mall.