PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 22, 2026Communications of the ACM0 citations

SPiDR: Microstructure-Assisted Vision for Ubiquitous Tiny Robots

View Full Paper
YBYang BaiNGNakul GargNRNirupam Roy

Key Points

  • The aim is to develop a power-efficient system for spatial sensing in miniature robots using acoustic signals.
  • Design and implement an acoustic sensor using one speaker and one microphone.
  • Create a 3D-printed stencil to enhance sound interactions.
  • Develop a depth-map reconstruction algorithm using signal reflections.
  • Optimize power consumption to under 10 mW.
  • Successfully generated depth-maps with over 80% structural similarity to real-world scenes.
  • Achieved a low power usage of less than 10 mW during operation.
  • Improved spatial mapping with a single speaker/microphone pair using innovative design.

Abstract

This paper presents the design and implementation of SPiDR , an ultra-low-power spatial sensing system for miniature mobile robots. This acoustic sensor produces a cross-sectional map of the field-of-view using only one speaker/microphone pair. While it is challenging to have enough spatial diversity of signal with a single omnidirectional source, we leverage sound’s interaction with small structures to create a 3D-printed passive filter, called a stencil, that can project spatially coded signals on a region at a fine granularity. The system receives a linear combination of the reflections from nearby objects and applies a novel power-aware depth-map reconstruction algorithm. The algorithm first estimates the approximate locations of the objects in the scene and then iteratively applies fractional multi-resolution inversion. SPiDR consumes only 10 m W of power to generate a depth-map in real-world scenario with over 80% structural similarity score with the scene.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Bai et al. (2026) studied this question.

synapsesocial.com/papers/699a9e00482488d673cd457fhttps://doi.org/10.1145/3772712
Ask AI
Helpful
Bookmark
Share
View Full Paper