{"id":489097,"date":"2026-10-17T05:52:06","date_gmt":"2026-10-17T05:52:06","guid":{"rendered":"http:\/\/6acfd110235b6db768c1b3fe"},"modified":"2026-10-17T05:52:06","modified_gmt":"2026-10-17T05:52:06","slug":"estos-pequenos-drones-vuelan-a-oscuras-gracias-a-unos-bigotes-inspirados-en-los-ratones","status":"publish","type":"post","link":"https:\/\/sarmiento24h.com\/?p=489097","title":{"rendered":"Estos peque\u00f1os drones vuelan a oscuras gracias a unos \u201cbigotes\u201d inspirados en los ratones"},"content":{"rendered":"<div><img decoding=\"async\" src=\"https:\/\/media.es.wired.com\/photos\/6acfd11027329644425869ca\/16:9\/w_1280,c_limit\/2026_Q3_Web_TUDelft_Drones_0182.jpeg\" class=\"ff-og-image-inserted\"><\/div>\n<p>Adem\u00e1s, en el proceso de estimar la distancia a la pared a partir de la se\u00f1al corregida, la predicci\u00f3n calculada a partir del propio movimiento del dron se incorpora a la predicci\u00f3n de la red neuronal mediante un filtro de Kalman (un m\u00e9todo computacional que estabiliza el valor estimado combinando m\u00faltiples datos inciertos).<\/p>\n<p>Como resultado, se ha logrado mejorar el error absoluto medio de uno de los bigotes en paredes con pizarra blanca de 6.40 mm a 4.23 mm, y en paredes de cristal transparente, de 11.33 mm a 5.91 mm. Se ha comprobado que, mientras que los sensores de distancia l\u00e1ser ofrecen resultados inestables a la hora de medir la distancia a una pared de cristal, los sensores t\u00e1ctiles permiten estimar la distancia de forma estable.<\/p>\n<h2>Capaz de detectar paredes invisibles<\/h2>\n<p>El equipo de investigaci\u00f3n realiz\u00f3 pruebas de vuelo con un dron en un entorno con tres paredes transparentes colocadas una al lado de la otra. Cuando el sensor t\u00e1ctil tocaba una pared, el dron segu\u00eda volando a lo largo de ella, manteniendo una distancia de entre 60 y 100 mm. El experimento demostr\u00f3 que el dron alcanz\u00f3 el punto objetivo en los cinco intentos. Tambi\u00e9n lo logr\u00f3 en todos los intentos cuando las paredes estaban colocadas en \u00e1ngulo. Por otro lado, un sensor l\u00e1ser, instalado para comparaci\u00f3n, no pudo medir la distancia si el dron no apuntaba directamente a la pared.<\/p>\n<p>Sin embargo, simplemente moverse a lo largo de las paredes facilita perderse en las esquinas de la habitaci\u00f3n o pasar por alto obst\u00e1culos laterales. Por lo tanto, los investigadores idearon un m\u00e9todo de b\u00fasqueda que prioriza la exploraci\u00f3n de \u00e1reas nuevas mediante un m\u00e9todo probabil\u00edstico para estimar la forma de las paredes (superficie de funci\u00f3n impl\u00edcita de proceso <em>gaussiano<\/em>). Con este m\u00e9todo, lograron encontrar una salida de 80 cm de ancho en una habitaci\u00f3n cuadrada de 2 m con paredes de vidrio en 9 de cada 10 simulaciones y en pruebas reales.<\/p>\n<p>El microcontrolador del dron tiene una capacidad de memoria de 192 KB, pero el algoritmo del sensor t\u00e1ctil de este experimento utiliza solo 34 KB. \u201cQuer\u00edamos demostrar que no es necesario sacrificar el tama\u00f1o ni la potencia de procesamiento del dron para utilizar la retroalimentaci\u00f3n t\u00e1ctil\u201d, se\u00f1ala Chaoshan Ye, estudiante de doctorado de la TU Delft. \u201cIncluso un dron peque\u00f1o puede detectar y reaccionar a su entorno en tiempo real\u201d.<\/p>\n<p>Por otro lado, persisten varios desaf\u00edos. Si el sensor rastrea la pared durante un per\u00edodo prolongado, se acumulan desviaciones de la se\u00f1al, lo que reduce la precisi\u00f3n de la estimaci\u00f3n de la distancia. Adem\u00e1s, debido a las diferencias individuales en los sensores de presi\u00f3n barom\u00e9trica y a ligeras variaciones mec\u00e1nicas, resulta dif\u00edcil aplicar directamente un modelo entrenado a un sensor diferente. Actualmente, para obtener el mejor rendimiento, es necesario utilizar un sistema de medici\u00f3n externo y recalibrar cada sensor rastreando la pared de 5 a 6 veces, lo que lleva entre 15 y 20 minutos.<\/p>\n<p>El equipo de investigaci\u00f3n prev\u00e9 aplicaciones en ayuda humanitaria en casos de desastre, exploraci\u00f3n en entornos peligrosos, vuelo aut\u00f3nomo en espacios confinados donde los sensores convencionales no son utilizables e inspecci\u00f3n de estructuras complejas. Esta investigaci\u00f3n demuestra que, al combinar <em>hardware<\/em> ligero con procesamiento avanzado a bordo, la detecci\u00f3n t\u00e1ctil puede ser un m\u00e9todo de detecci\u00f3n robusto incluso para los robots voladores aut\u00f3nomos m\u00e1s peque\u00f1os.<\/p>\n<p><em>Editado por Daisuke Takimoto<\/em><\/p>\n<p><em>Art\u00edculo originalmente publicado en<\/em> <a href=\"https:\/\/wired.jp\/article\/whisker-based-tactile-flight-tiny-drones\/\">WIRED Jap\u00f3n<\/a><em>. Adaptado por Mauricio Serfatty Godoy.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Adem\u00e1s, en el proceso de estimar la&#46;&#46;&#46;<\/p>\n","protected":false},"author":1,"featured_media":489098,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[46],"tags":[],"class_list":["post-489097","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tecnologia"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.6 - 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