Congestion-Aware Robot Tour Planning in Crowded Environments

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.

Formal Modelling for Multi-Robot Systems Under Uncertainty

Purpose of Review: To effectively synthesise and analyse multi-robot behaviour, we require formal task-level models which accurately capture multi-robot execution. In this paper, we review modelling formalisms for multi-robot systems under uncertainty, and discuss how they can be used for planning, reinforcement learning, model checking, and simulation. Recent Findings: Recent work has investigated models which more accurately capture multi-robot execution by considering different forms of uncertainty, such as temporal uncertainty and partial observability, and modelling the effects of robot interactions on action execution. Other strands of work have presented approaches for reducing the size of multi-robot models to admit more efficient solution methods. This can be achieved by decoupling the robots under independence assumptions, or reasoning over higher level macro actions. Summary: Existing multi-robot models demonstrate a trade off between accurately capturing robot dependencies and uncertainty, and being small enough to tractably solve real world problems. Therefore, future research should exploit realistic assumptions over multi-robot behaviour to develop smaller models which retain accurate representations of uncertainty and robot interactions; and exploit the structure of multi-robot problems, such as factored state spaces, to develop scalable solution methods.