  As the number of Persons with Disabilities (PWD), particularly those with one
or more physical impairments, increases, there is an increasing demand for
assistive robotic technologies that can support independent mobility in the
built environment and reduce the burden on caregivers. Current assistive
mobility platforms (e.g., robotic wheelchairs) often fail to incorporate user
preferences and control, leading to reduced trust and efficiency. Existing
shared control algorithms do not allow the incorporation of the user control
preferences inside the navigation framework or the path planning algorithm. In
addition, existing dynamic local planner algorithms for robotic wheelchairs do
not take into account the social spaces of people, potentially leading such
platforms to infringe upon these areas and cause discomfort. To address these
concerns, this work introduces a novel socially-aware shared autonomy-based
navigation system for assistive mobile robotic platforms.
  Our navigation framework comprises a Global Planner and a Local Planner. To
implement the Global Planner, the proposed approach introduces a novel User
Preference Field (UPF) theory within its global planning framework, explicitly
acknowledging user preferences to adeptly navigate away from congested areas.
For the Local Planner, we propose a Socially-aware Shared Control-based Model
Predictive Control with Dynamic Control Barrier Function (SS-MPC-DCBF) to
adjust movements in real-time, integrating user preferences for safer, more
autonomous navigation. Evaluation results show that our Global Planner aligns
closely with user preferences compared to baselines, and our Local Planner
demonstrates enhanced safety and efficiency in dynamic and static scenarios.
This integrated approach fosters trust and autonomy, crucial for the acceptance
of assistive mobility technologies in the built environment.
