Autonomy and independent navigation are vital to daily life but remain challenging for individuals with blindness. Robotic systems can enhance mobility and confidence by providing intelligent navigation assistance. However, fully autonomous systems may reduce users’ sense of control, even when they wish to remain actively involved. Although collaboration between user and robot has been recognized as important, little is known about how perceptions of this relationship change with repeated use. We present a repeated exposure study with six blind participants who interacted with a navigation-assistive robot in a real-world museum. Participants completed tasks such as navigating crowds, approaching lines, and encountering obstacles. Findings show that participants refined their strategies over time, developing clearer preferences about when to rely on the robot versus act independently. This work provides insights into how strategies and preferences evolve with repeated interaction and offers design implications for robots that adapt to user needs over time.

Group interactions are essential to social functioning, yet effective engagement relies on the ability to recognize and interpret visual cues, making such engagement a significant challenge for blind people. In this paper, we investigate how a mobile robot can support group interactions for blind people. We used the scenario of a guided tour with mixed-visual groups involving blind and sighted visitors. Based on insights from an interview study with blind people (n = 5) and museum experts (n = 5), we designed and prototyped a robotic system that supported blind visitors to join group tours. We conducted a field study in a science museum where each blind participant (n = 8) joined a group tour with one guide and two sighted participants (n = 8). Findings indicated users’ sense of safety from the robot’s navigational support, concerns in the group participation, and preferences for obtaining environmental information. We present design implications for future robotic systems to support blind people’s mixed-visual group participation.

Blind people have limited opportunities to explore an environment based on their interests.While existing navigation systems could provide them with surrounding information while navigating, they have limited scalability as they require preparing prebuilt maps.Thus, to develop a map-less robot that assists blind people in exploring, we first conducted a study with ten blind participants at a shopping mall and science museum to investigate the requirements of the system, which revealed the need for three levels of detail to describe the surroundings based on users' preferences.Then, we developed WanderGuide, with functionalities that allow users to adjust the level of detail in descriptions and verbally interact with the system to ask questions about the environment or to go to points of interest.The study with five blind participants revealed that WanderGuide could provide blind people with the enjoyable experience of wandering around without a specific destination in their minds.

People who are blind perceive the world differently than those who are sighted, which can result in distinct motion characteristics. For instance, when crossing at an intersection, blind individuals may have different patterns of movement, such as veering more from a straight path or using touch-based exploration around curbs and obstacles. These behaviors may appear less predictable to motion models embedded in technologies such as autonomous vehicles. Yet, the ability of 3D motion models to capture such behavior has not been previously studied, as existing datasets for 3D human motion currently lack diversity and are biased toward people who are sighted. In this work, we introduce BlindWays, the first multimodal motion benchmark for pedestrians who are blind. We collect 3D motion data using wearable sensors with 11 blind participants navigating eight different routes in a real-world urban setting. Additionally, we provide rich textual descriptions that capture the distinctive movement characteristics of blind pedestrians and their interactions with both the navigation aid (e.g., a white cane or a guide dog) and the environment. We benchmark state-of-the-art 3D human prediction models, finding poor performance with off-the-shelf and pre-training-based methods for our novel task. To contribute toward safer and more reliable systems that can seamlessly reason over diverse human movements in their environments, our text-and-motion benchmark is available at https://blindways.github.io/.

To enable people with visual impairments (PVI) to explore shopping malls, it is important to provide information for selecting destinations and obtaining information based on the individual's interests. We achieved this through conversational interaction by integrating a large language model (LLM) with a navigation system. ChitChatGuide allows users to plan a tour through contextual conversations, receive personalized descriptions of surroundings based on transit time, and make inquiries during navigation. We conducted a study in a shopping mall with 11 PVI, and the results reveal that the system allowed them to explore the facility with increased enjoyment. The LLM-based conversational interaction, by understanding vague and context-based questions, enabled the participants to explore unfamiliar environments effectively. The personalized and in-situ information generated by the LLM was both useful and enjoyable. Considering the limitations we identified, we discuss the criteria for integrating LLMs into navigation systems to enhance the exploration experiences of PVI.

We present Snap&Nav, a navigation system for blind people in unfamiliar buildings, without prebuilt digital maps. Instead, the system utilizes the floor map as its primary information source for route guidance. The system requires a sighted assistant to capture an image of the floor map, which is analyzed to create a node map containing intersections, destinations, and current positions on the floor. The system provides turn-by-turn navigation instructions while tracking users' positions on the node map by detecting intersections. Additionally, the system estimates the scale difference of the node map to provide distance information. Our system was validated through two user studies with 20 sighted and 12 blind participants. Results showed that sighted participants processed floor map images without being accustomed to the system, while blind participants navigated with increased confidence and lower cognitive load compared to the condition using only cane, appreciating the system's potential for use in various buildings.

Indoor navigation systems with prebuilt maps have shown great potential in navigating blind people even in unfamiliar buildings. However, blind people cannot always benefit from them in every building, as prebuilt maps are expensive to build. This paper explores a map-less navigation system for blind people to reach destinations in unfamiliar buildings, which is implemented on a robot. We first conducted a participatory design with five blind people, which revealed that intersections and signs are the most relevant information in unfamiliar buildings. Then, we prototyped PathFinder, a navigation system that allows blind people to determine their way by detecting and conveying information about intersections and signs. Through a participatory study, we improved the interface of PathFinder, such as the feedback for conveying the detection results. Finally, a study with seven blind participants validated that PathFinder could assist users in navigating unfamiliar buildings with increased confidence compared to their regular aid.

Navigating in an indoor corridor can be challenging for blind people as they have to be aware of obstacles while also having to recognize the intersections that lead to the destination. To aid blind people in such tasks, we propose Corridor-Walker, a smartphone-based system that assists blind people to avoid obstacles and recognize intersections. The system uses a LiDAR sensor equipped with a smartphone to construct a 2D occupancy grid map of the surrounding environment. Then, the system generates an obstacle-avoiding path and detects upcoming intersections on the grid map. Finally, the system navigates the user to trace the generated path and notifies the user of each intersection's existence and the shape using vibration and audio feedback. A user study with 14 blind participants revealed that Corridor-Walker allowed participants to avoid obstacles, rely less on the wall to walk straight, and enable them to recognize intersections.

Standing in line is one of the most common social behaviors in public spaces but can be challenging for blind people. We propose an assistive system named LineChaser, which navigates a blind user to the end of a line and continuously reports the distance and direction to the last person in the line so that they can be followed. LineChaser uses the RGB camera in a smartphone to detect nearby pedestrians, and the built-in infrared depth sensor to estimate their position. Via pedestrian position estimations, LineChaser determines whether nearby pedestrians are standing in line, and uses audio and vibration signals to notify the user when they should start/stop moving forward. In this way, users can stay correctly positioned while maintaining social distance. We have conducted a usability study with 12 blind participants. LineChaser allowed blind participants to successfully navigate lines, significantly increasing their confidence in standing in lines.