Congestion-Aware Robot Tour Planning in Crowded Environments

2026-06-17

AI Summary

This work addresses the challenge of efficiently executing multi-goal patrol tasks for service robots in crowded environments. The authors propose a probabilistic patrol planning approach that explicitly incorporates crowd congestion into path planning for the first time. By leveraging Circular Linear Flow Field (CLiFF) maps to predict human trajectories, the method dynamically constructs and solves a Markov Decision Process (MDP) online, enabling real-time responsiveness and replanning in the presence of moving crowds. Experimental validation on a real-world shopping mall crowd dataset demonstrates the effectiveness of the proposed framework, which achieves strong scalability while maintaining computational efficiency suitable for real-time operation.

Abstract

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.

Problem

• robot tour planning
• crowded environments
• human congestion
• autonomous mobile robots
• collision avoidance

Innovation

• congestion-aware planning
• circular linear flow field (CLiFF)
• Markov decision process
• crowd prediction
• online replanning