Goal-based navigation in public places is critical for independent mobility and for breaking barriers that exist for blind or visually impaired (BVI) people in a sight-centric society. Through this work we present a proof-of-concept system that autonomously leverages goal-based navigation assistance and perception to identify socially preferred seats and safely guide its user towards them in unknown indoor environments. The robotic system includes a camera, an IMU, vibrational motors, and a white cane, powered via a backpack-mounted laptop. The system combines techniques from computer vision, robotics, and motion planning with insights from psychology to perform 1) SLAM and object localization, 2) goal disambiguation and scoring, and 3) path planning and guidance. We introduce a novel 2-motor haptic feedback system on the cane’s grip for navigation assistance. Through a pilot user study we show that the system is successful in classifying and providing haptic navigation guidance to socially preferred seats, while optimizing for users’ convenience, privacy, and intimacy in addition to increasing their confidence in independent navigation. The implications are encouraging as this technology, with careful design guided by the BVI community, can be adopted and further developed to be used with medical devices enabling the BVI population to better independently engage in socially dynamic situations like seat choice.
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Fast and Safe Path-Following Control using a State-Dependent Directional Metric
This paper considers the problem of fast and safe autonomous navigation in partially known environments. Our main contribution is a control policy design based on ellipsoidal trajectory bounds obtained from a quadratic state-dependent distance metric. The ellipsoidal bounds are used to embed directional preference in the control design, leading to system behavior that is adapted to local environment geometry, carefully considering medial obstacles while paying less attention to lateral ones. We use a virtual reference governor system to adaptively follow a desired navigation path, slowing down when system safety may be violated and speeding up otherwise. The resulting controller is able to navigate complex environments faster than common Euclidean-norm and Lyapunov-function-based designs, while retaining stability and collision avoidance guarantees.
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- Award ID(s):
- 1755568
- NSF-PAR ID:
- 10147501
- Date Published:
- Journal Name:
- IEEE International Conference on Robotics and Automation
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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