2020/09/27 by Faruk Ahmed, Ahmed, Faruk, Md Sultan Mahmud +7
Computer Science · Engineering · Neuroscience · #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #Evacuation and Crowd Dynamics #FOS: Computer and information sciences #Tactile and Sensory Interactions #cs.CV
paper · pdf · doi:10.48550/arxiv.2009.12877
Journal, to be submitted
arxiv created 2020/09/27 · openalex publication_date 2020/09/27 · arxiv updated 2020/09/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Finding a path free from obstacles that poses minimal risk is critical for safe navigation. People who are sighted and people who are visually impaired require navigation safety while walking on a sidewalk. In this research we developed an assistive navigation on a sidewalk by integrating sensory inputs using reinforcement learning. We trained a Sidewalk Obstacle Avoidance Agent (SOAA) through reinforcement learning in a simulated robotic environment. A Sidewalk Obstacle Conversational Agent (SOCA) is built by training a natural language conversation agent with real conversation data. The SOAA along with SOCA was integrated in a prototype device called augmented guide (AG). Empirical analysis showed that this prototype improved the obstacle avoidance experience about 5% from a base case of 81.29%