A Hybrid Path Planning Framework for Assistive Wheelchair Navigation in Dynamic Pedestrian Environments
Overview
This research presents a hybrid path planning framework designed for autonomous wheelchairs navigating dynamic pedestrian environments. The system integrates the Traveling Salesman Problem (TSP) for global task ordering with Potential Field methods and local planners like RRT and A* to ensure collision-free navigation. Two specific algorithms, TSP-PF-RRT and TSP-PF-A*, were developed to handle multiple stops while avoiding static and dynamic obstacles. Performance was evaluated using Monte Carlo simulations in crowded scenarios containing heterogeneous obstacles. Results indicate that the proposed framework enables efficient multi-goal navigation with fewer or no collisions and shorter travel distances compared to baseline approaches.
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Environment: simulation
“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.”
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Control: reported autonomous
“arXiv:2610.42049v1 Announce Type: new 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 the”
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- arXiv Robotics — research abstractsSignalA Hybrid Path Planning Framework for Assistive Wheelchair Navigation in Dynamic Pedestrian Environments
This research presents a hybrid path planning framework designed for autonomous wheelchairs navigating dynamic pedestrian environments. The system integrates the Traveling Salesman Problem (TSP) for global task ordering with Potential Field methods and local planners like RRT and A* to ensure collision-free navigation. Two specific algorithms, TSP-PF-RRT and TSP-PF-A*, were developed to handle multiple stops while avoiding static and dynamic obstacles. Performance was evaluated using Monte Carlo simulations in crowded scenarios containing heterogeneous obstacles. Results indicate that the proposed framework enables efficient multi-goal navigation with fewer or no collisions and shorter travel distances compared to baseline approaches.
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