The COVID-19 pandemic has motivated research on mobile robot-based disinfection methods to help contain the spread of the virus, including ultraviolet (UV) germicidal inactivation. Recent approaches have focused on formulating autonomous disinfection as a coverage problem. However, the focus so far has been on maximising coverage, rather than scaling solutions to large-scale environments or making solutions robust to environmental uncertainty. Since the intensity of UV light is strongly coupled with the distance to the target surface, localisation errors should be included in the decision making process to synthesise meaningful irradiation durations. Therefore, in this paper we solve a linked path and dosage planning problem, explicitly considering localisation uncertainty in the model. Our model is formulated as a Markov decision process (MDP) which maps localisation uncertainty to dose delivery distributions given radiation and localisation models. We solve this (MDP) over a finite horizon using prioritised value iteration to maximise dose delivery within specified time bounds. Simulation experiments performed on real-world data show successful disinfection, outperforming a rule-based baseline.
