The ACANTO project is developing robotic assistants to aid the mobility and recovery of mobility-impaired and older adults. One key feature of the project's robotic assistants is aiding with navigation in chaotic environments. Prior work has solved this for a single user with a single robot, however for therapeutic outcomes ACANTO supports social groups and group activities. Thus these robotic assistants must be able to efficiently support groups of users walking together. This requires an efficient navigation solution that can handle large numbers of users, maintain (de-facto) group cohesion despite unpredictable behaviours, and operate rapidly on embedded devices. We address these challenges by: using sensor information to develop behavioural traces, clustering traces to determine groups, modeling the groups using the social force model, and finding an optimal navigation solution using statistical model checking. The new components of this solution are validated on the ETH Zürich dataset of pedestrians in an open environment.