Issue link: https://resources.mouser.com/i/1535833
• Stereo vision uses dual cameras to estimate disparity and infer depth, but can suffer in textureless or low- contrast scenes. • iTOF sensors emit modulated light and calculate distance from the phase shift of the returned signal. It's particularly useful in applications that need compact integration, low latency, and low power. • Direct TOF systems measure photon return time for higher range accuracy but require more power and cost. • Ultrasonic sensors, which measure the time of flight of high-frequency sound waves, are unaffected by visual occlusion or transparency. This makes them effective in detecting soft obstacles, glass surfaces, and fluid boundaries. However, environmental variability introduces substantial complexity into AMR perception. Lighting conditions, surface reflectivity, and structural occlusions can degrade the performance of any single sensor modality. Sensor fusion addresses this challenge by combining raw and pre-processed data from complementary sensors into a unified perception model. This approach effectively improves reliability and robustness in edge cases while also enhancing spatial resolution and obstacle classification. Multi-modal perception via real-time sensor fusion unlocks features like SLAM, dynamic obstacle avoidance, and context-aware path planning. onsemi offers sensing solutions for AMRs, including • Hyperlux LP/LH (Rolling Shutter), Hyperlux SG (Global Shutter) image sensors optimized for object detection and barcode scanning C h a p t e r 2 | A c h i e v i n g a S u s t a i n a b l e F u t u r e w i t h S i C One of the most impactful AMR applications I've worked with is goods-to-person (GTP) picking. Technologies like Simultaneous Localization and Mapping (SLAM), sensor fusion, and multi-modal perception have been key to enabling the level of autonomy GTP requires." Victoria Quinde System Engineering Manager, Dematic C h a p t e r 2 | A d v a n c e d S e n s i n g a n d A I - D r i v e n P e r c e p t i o n 12 Engineering the Future: The Sensors and Systems Powering Modern Mobile Robots