Belief-based Risk-Aware Anticipatory Navigation: Modeling Intention and Position Uncertainty for Safe and Efficient Robot Navigation

The increasing deployment of Automated Mobile Vehicles (AMVs) in warehouses requires efficient navigation in dynamic environments shared with human workers. Traditional safety strategies, such as stopping or slowing down, often reduce efficiency and do not account for human-robot interaction. To address this, interaction-aware methods have been developed. However, traditional interaction models assume fixed parameters, rely on a single forecast, or use intent labels that are not updated during the interaction, and thus do not explicitly represent intention uncertainty, that is, variability in how people react to robots. When combined with position uncertainty introduced by sensing noise, this unmodeled intention uncertainty can make motion prediction and navigation decisions unreliable, leading to conservative behavior, inefficiency, or collisions in human–robot interactions. To bridge this gap, we propose Belief-based Risk-Aware Anticipatory Navigation (BRAN), a framework that jointly models intention and position uncertainty. BRAN maintains probabilistic beliefs over an agent's position and intention to explicitly represent uncertainty in both quantities. Position uncertainty is represented via sampled observations, and the intent belief is updated online through Bayesian inference so that predictions evolve with the interaction. These beliefs are propagated through the Social Force Model (SFM) to generate predictions of possible agent motion under uncertainty. We then compute collision risk from the predicted motion distribution and incorporate it into model predictive control as a constraint. This constrains predicted collision risk while optimizing motion toward the goal. Experimental results validate the proposed design in controlled settings, demonstrate its effectiveness in scenarios with multiple pedestrians, and confirm its feasibility on a real robot with onboard sensing and actuation.