A Taillight Matching and Pairing Algorithm for Stereo-Vision-Based Nighttime Vehicle-to-Vehicle Positioning

Active Perception in Non-Visual Recognition Environments by Stretchable Tentacle Sensor Arrays

Smoke fog or other light-interference environments have intrinsic obstruction for visual recognition techniques to explore objects and surroundings. Alternatively, tactile perceptions, rather than visual observations, are commonly used by burrowing or deep-sea animals to communicate with environments. Bio-inspired by this natural wisdom, here, we demonstrate stretchable tentacle sensor arrays, which can recognize surrounding objects located in non-visual conditions such as smoke fog or dark environment. Each tentacle sensor is composed of two functional parts: a retractable tentacle with a magnetic top and an elastomer bottom containing copper coils. Different from traditionally passive tactile sensors, these tentacle sensors can actively stretch under the control of a syringe pump, yielding different electrical signals when in contact with the objects. Analyzing collected sensing signals of those tactile sensor arrays by the feature analysis model, complex morphological information of irregular objects in the smoke fog can be recognized. Our study reveals a fundamental connection between stretchable tactile sensors and feature analysis and demonstrates its practical potential for active perception in a non-visual recognition environment.