A Hybrid Path Planning Framework for Assistive Wheelchair Navigation in Dynamic Pedestrian Environments

Abstract:Autonomous wheelchairs can improve the quality of life for individuals with mobility impairments by providing greater independence. However, reliable navigation especially in human environments is a major challenge due to factors such as dynamic obstacles, unpredictable pedestrian behavior, and the users' need to complete multiple stops along the way before reaching their final destination. This paper presents a hybrid path planning framework for multi-goal navigation in dynamic environments. The framework integrates the Traveling Salesman Problem (TSP) for global task ordering with the Potential Field (PF) method and heuristic local planners such as Rapidly-exploring Random Tree (RRT) and A-star (A*) to achieve collision-free navigation. Within this hybrid framework, TSP determines the visiting order of intermediate stations, PF transfers global path information, and local planners avoid obstacles while guiding the robot to the stations. The local planners generate feasible paths that allow the robot to follow the global trajectory and reroute to avoid static and dynamic obstacles. For this purpose, two algorithms have been developed: (i) TSP-PF-RRT and (ii) TSP-PF-A*. The performance of the proposed algorithms are evaluated using Monte Carlo simulations in crowded and extra-crowded scenarios that contain heterogeneous dynamic and static obstacles. Experimental results demonstrate that the proposed hybrid framework enables efficient multi-goal navigation while maintaining safe operation in dynamic environments with humans, with TSP-PF-RRT achieving fewer or no collisions and shorter travel distances.

14 pages, 5 figures. Presented as a main-track paper at the International Conference of Social Robotics + ART'26, London, UK, 1-4 July 2026

Robotics (cs.RO); Systems and Control (eess.SY)

arXiv:2610.42049 [cs.RO]

(or arXiv:2610.42049v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.42049 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.