# Socially-Aware Shared Control Navigation for Assistive Mobile Robots in the Built Environment

arXiv: https://arxiv.org/abs/2405.17279 · HTML: https://mlsanity.com/paper/2405.17279/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 60 references that are in the map.
- First posted on arXiv 27 May 2024.

## Builds on (most cited first, 13 of 13)

- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Hydra: A Real-time Spatial Perception System for 3D Scene Graph Construction and Optimization](https://mlsanity.com/paper/2201.13360/) (2022)
- [Core Challenges of Social Robot Navigation: A Survey](https://mlsanity.com/paper/2103.05668/) (2021)
- [Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function](https://mlsanity.com/paper/2007.11718/) (2020)
- [Group-based Motion Prediction for Navigation in Crowded Environments](https://mlsanity.com/paper/2107.11637/) (2021)
- [From Crowd Motion Prediction to Robot Navigation in Crowds](https://mlsanity.com/paper/2303.01424/) (2023)
- [Enhancing Feasibility and Safety of Nonlinear Model Predictive Control with Discrete-Time Control Barrier Functions](https://mlsanity.com/paper/2105.10596/) (2021)
- [Comparative Analysis of Control Barrier Functions and Artificial Potential Fields for Obstacle Avoidance](https://mlsanity.com/paper/2010.09819/) (2020)
- [Dynamic Control Barrier Function-based Model Predictive Control to Safety-Critical Obstacle-Avoidance of Mobile Robot](https://mlsanity.com/paper/2209.08539/) (2022)
- [Online Motion Planning based on Nonlinear Model Predictive Control with Non-Euclidean Rotation Groups](https://mlsanity.com/paper/2006.03534/) (2020)
- [Towards an immersive user interface for waypoint navigation of a mobile robot](https://mlsanity.com/paper/2003.12772/) (2020)
- [Reciprocal Collision Avoidance for General Nonlinear Agents using Reinforcement Learning](https://mlsanity.com/paper/1910.10887/) (2019)

## Built on by (most cited first, 1 of 1)

- [SAATT Nav: a Socially Aware Autonomous Transparent Transportation Navigation Framework for Wheelchairs](https://mlsanity.com/paper/2603.13698/) (2026)

## Abstract (arXiv)

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.