{"id":90501,"date":"2025-03-04T17:16:08","date_gmt":"2025-03-04T09:16:08","guid":{"rendered":"https:\/\/solomon-3d.mesvr.tw\/?p=90501"},"modified":"2025-07-18T16:42:05","modified_gmt":"2025-07-18T08:42:05","slug":"1-dakikada-bin-picking-ai-ve-3d-gorus-ile","status":"publish","type":"post","link":"https:\/\/solomon-3d.mesvr.tw\/tr\/blog\/1-dakikada-bin-picking-ai-ve-3d-gorus-ile\/","title":{"rendered":"1 Dakikada Bin Picking: AI ve 3D G\u00f6r\u00fc\u015f ile"},"content":{"rendered":"
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1 Dakikada Bin Picking: AI ve 3D G\u00f6r\u00fc\u015f ile<\/h1><\/div>\n\n\n\n
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G\u00f6r\u00fc\u015f Tabanl\u0131 Bin Picking’e Giri\u015f<\/h2>\n\n\n\n
Otomatik end\u00fcstriyel s\u00fcre\u00e7ler giderek daha karma\u015f\u0131k hale geldik\u00e7e, hassas ve verimli nesne i\u015fleme ihtiyac\u0131 her zamankinden daha kritik hale geliyor. Geleneksel 2D g\u00f6r\u00fc\u015f sistemleri, d\u00fczensiz \u015fekiller veya yar\u0131 saydam malzemelerle ba\u015fa \u00e7\u0131kmakta genellikle zorlan\u0131r, bu da etkinliklerini s\u0131n\u0131rlayan bir fakt\u00f6rd\u00fcr. AI ve 3D g\u00f6r\u00fc\u015f teknolojilerinin entegrasyonu, robotlar\u0131n nesne konumland\u0131rmas\u0131 ve y\u00f6nlendirmesini kapsaml\u0131 bir \u015fekilde anlamalar\u0131n\u0131 sa\u011flar. Bu kombinasyon, robotik bin picking\u2019i geli\u015ftirerek do\u011frulu\u011fu ve h\u0131z\u0131 art\u0131r\u0131r. Bu blog, geli\u015fmi\u015f 3D g\u00f6r\u00fc\u015f tekniklerinin ve AI entegrasyonunun robotlara karma\u015f\u0131k picking ve malzeme ta\u015f\u0131ma g\u00f6revlerini kolayl\u0131kla yapabilme g\u00fcc\u00fc sa\u011flad\u0131\u011f\u0131n\u0131 ke\u015ffedecektir.<\/div>\n<\/div>\n<\/div>\n <\/div><\/div>\n\n\n\n
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2D vs 3D Makine G\u00f6r\u00fc\u015f\u00fc<\/h2>\n\n\n\n
2D makine g\u00f6r\u00fc\u015f\u00fc, bir nesnenin d\u00fcz, iki boyutlu bir g\u00f6r\u00fcn\u00fcm\u00fcn\u00fc sa\u011flar ve X ve Y eksenleri boyunca konumuna odaklan\u0131r. Di\u011fer yandan, 3D makine g\u00f6r\u00fc\u015f\u00fc, Z ekseninde derinlik ekleyerek nesnelerin daha kapsaml\u0131, \u00fc\u00e7 boyutlu bir anlay\u0131\u015f\u0131n\u0131 sunar. End\u00fcstri 4.0\u2019\u0131n y\u00fckselmesiyle birlikte, 3D g\u00f6r\u00fc\u015f\u00fcn robotik sistemlerle entegrasyonu giderek daha pop\u00fcler hale gelmi\u015f ve \u00fcretim s\u00fcre\u00e7lerinde \u00f6nemli ilerlemeler sa\u011flam\u0131\u015ft\u0131r. 2D g\u00f6r\u00fcnt\u00fcleme genellikle standart end\u00fcstriyel kameralarla<\/a> yap\u0131l\u0131rken, 3D g\u00f6r\u00fcnt\u00fcleme \u00f6zel optik teknolojiler gerektirir. 3D veri elde etmek i\u00e7in yayg\u0131n olarak kullan\u0131lan teknikler aras\u0131nda Aktif Stereo G\u00f6r\u00fc\u015f\u00fc, Time-of-Flight (TOF) ve Yap\u0131land\u0131r\u0131lm\u0131\u015f I\u015f\u0131k yer almaktad\u0131r.<\/div>\n\n\n\n
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Stereo G\u00f6r\u00fc\u015f<\/h3>\n\n\n\n

Stereo g\u00f6r\u00fc\u015f, bir nesnenin konumunu ve \u00fc\u00e7 boyutlu yap\u0131s\u0131n\u0131 yakalayarak insan derinlik alg\u0131s\u0131n\u0131 taklit eder. Bu, d\u00fc\u015f\u00fck enerji t\u00fcketimi ve maliyet etkinli\u011fi gibi anahtar faydalar sunar, bu da onu end\u00fcstriyel uygulamalar i\u00e7in cazip bir se\u00e7enek haline getirir. Ancak, stereo g\u00f6r\u00fc\u015f gecikmeler ya\u015fayabilir ve d\u00fc\u015f\u00fck \u0131\u015f\u0131k ortamlar\u0131nda etkinli\u011fi azalabilir. Robotikteki kullan\u0131m\u0131n\u0131n yan\u0131 s\u0131ra, stereo g\u00f6r\u00fc\u015f, geli\u015fmi\u015f g\u00f6rsel denetim ve nesne tan\u0131ma i\u00e7in AR + AI sistemlerine<\/a> entegre edilmektedir.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Time-of-Flight<\/h3>\n\n\n\n

Time-of-Flight (TOF) teknolojisi, nesnelere olan mesafeyi, k\u0131z\u0131l\u00f6tesi \u0131\u015f\u0131\u011f\u0131n nesneye gidip geri d\u00f6nmesi i\u00e7in ge\u00e7en zaman\u0131 hesaplayarak \u00f6l\u00e7er. Bu y\u00f6ntem, h\u0131zl\u0131 i\u015fleme h\u0131zlar\u0131 sunar ve parazitlere kar\u015f\u0131 diren\u00e7lidir, bu da onu dinamik ortamlarda uygun hale getirir. Ancak, TOF verimli olsa da, do\u011frulu\u011fu di\u011fer 3D g\u00f6r\u00fc\u015f teknolojilerine g\u00f6re daha d\u00fc\u015f\u00fck olabilir.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Yap\u0131land\u0131r\u0131lm\u0131\u015f I\u015f\u0131k<\/h3>\n\n\n\n

Yap\u0131land\u0131r\u0131lm\u0131\u015f \u0131\u015f\u0131k, nesnelere genellikle siyah-beyaz \u00e7izgilerden olu\u015fan belirgin \u0131\u015f\u0131k desenleri projeksiyon yapar. Bu desenlerin nesnenin y\u00fczeyiyle etkile\u015fime girerken deformasyonu, sistemin nesnenin \u015fekil haritas\u0131n\u0131 \u00e7\u0131karmas\u0131na ve Z-eksenindeki derinlik bilgilerini do\u011fru bir \u015fekilde yakalamas\u0131na olanak tan\u0131r. Bu teknoloji, \u00f6zellikle y\u00fcz tan\u0131ma ve kalite kontrol gibi uygulamalarda, \u00f6zellikle \u00fcretimde Otomatik Optik \u0130nceleme (AOI) <\/a>sistemleriyle yayg\u0131n olarak kullan\u0131l\u0131r.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n <\/div><\/div>\n\n\n\n

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Yapay Zeka ile Robotik Nesne Tan\u0131may\u0131 Geli\u015ftirme<\/h2>\n\n\n\n
3D g\u00f6rsellik de\u011ferli mekansal veriler sa\u011flasa da, karma\u015f\u0131k veya d\u00fczensiz \u015fekilli nesneleri tan\u0131mak i\u00e7in sadece buna g\u00fcvenmek hala zorluklar olu\u015fturabilir. \u00d6rne\u011fin, 3D g\u00f6rsellik, yar\u0131 saydam nesneleri do\u011fru bir \u015fekilde tespit etmekte veya k\u00fc\u00e7\u00fck, karma\u015f\u0131k metal par\u00e7alar\u0131n\u0131 robotlarla hassas bir \u015fekilde i\u015flemekle zorluk ya\u015fayabilir.

Yapay zeka ile 3D g\u00f6rsellik sistemlerinin entegrasyonu, robotlar\u0131n nesneleri daha y\u00fcksek hassasiyetle tan\u0131ma ve manip\u00fcle etme yetene\u011fini art\u0131r\u0131r. Bu kombinasyon, robotlar\u0131n d\u00fczensiz \u015fekilli nesneleri etkin bir \u015fekilde al\u0131p yerle\u015ftirmelerini sa\u011flar, do\u011fru y\u00f6nelim ve pozisyonlama sa\u011flar.

Karma\u015f\u0131k \u015fekillerin s\u00f6z konusu oldu\u011fu durumlarda \u2014\u00f6rne\u011fin, U \u015feklindeki metal par\u00e7alar\u0131n istiflenmesi\u2014 sadece 3D g\u00f6rsellik nesneleri do\u011fru \u015fekilde hizalama ve yerle\u015ftirme konusunda yetersiz kalabilir. Yapay zeka, bu bo\u015flu\u011fu doldurarak, sistemin nesnelerin ger\u00e7ek \u015fekillerini ve y\u00f6nelimlerini anlamas\u0131n\u0131 sa\u011flar, b\u00f6ylece hassas bir \u015fekilde i\u015flenmesini temin eder.<\/div>\n\n\n\n
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\u00c7\u00f6p Kutusu Yar\u0131 Saydam Nesneleri Se\u00e7me<\/a><\/h3>\n<\/div>\n<\/div>\n\n\n\n
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K\u00fc\u00e7\u00fck Metal Bile\u015fenlerin Toplanmas\u0131<\/a><\/h3>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n <\/div><\/div>\n\n\n\n
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60 Saniyede Bir Yapay Zeka Modeli E\u011fitmek<\/h2>\n\n\n\n
Derin \u00f6\u011frenme yapay zek\u00e2s\u0131n\u0131<\/a> 3D g\u00f6rsellik teknolojisiyle<\/a> entegre etmek, yaln\u0131zca nesne tan\u0131may\u0131 de\u011fil, ayn\u0131 zamanda etkili robotik i\u015flemleri sa\u011flamak i\u00e7in kavrama, yol planlama ve hareket kontrol\u00fc gibi s\u00fcre\u00e7lerin sorunsuz bir \u015fekilde koordine edilmesini gerektirir.

Solomon\u2019un derin \u00f6\u011frenme yapay zek\u00e2s\u0131, geli\u015fmi\u015f hareket planlama mod\u00fclleriyle birle\u015ferek robotlar\u0131n nesneleri tan\u0131y\u0131p etkile\u015fimde bulunmay\u0131 sadece 60 saniye gibi k\u0131sa bir s\u00fcrede \u00f6\u011frenmelerini sa\u011flar. Bu, \u00e7\u00f6p toplama sisteminin<\/a>, engellerden ka\u00e7\u0131n\u0131rken kavrama ve yerle\u015ftirme i\u00e7in en uygun yolu h\u0131zl\u0131 bir \u015fekilde belirlemesine olanak tan\u0131r, b\u00f6ylece do\u011fruluk ve verimlilik sa\u011flan\u0131r.

Bu g\u00fc\u00e7l\u00fc entegrasyon, kar\u0131\u015f\u0131k paletten \u00e7\u0131karma<\/a>, kit olu\u015fturma<\/a>, paketleme<\/a> ve rastgele kutu toplama<\/a> gibi karma\u015f\u0131k g\u00f6revler i\u00e7in pratik ve uygun maliyetli bir \u00e7\u00f6z\u00fcm sunarak dinamik end\u00fcstriyel ortamlar i\u00e7in idealdir.<\/div>\n<\/div>\n<\/div>\n <\/div><\/div>\n\n\n\n
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\u00c7\u00f6p toplama g\u00f6revlerinizi d\u00f6n\u00fc\u015ft\u00fcrmeye haz\u0131r m\u0131s\u0131n\u0131z? Bug\u00fcn bize ula\u015f\u0131n<\/span><\/span><\/span><\/a><\/div>\n<\/div>\n<\/div>\n <\/div><\/div>\n\n\n\n
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\"an<\/div>\n<\/div>\n<\/div>\n <\/div><\/div>","protected":false},"excerpt":{"rendered":"

Yava\u015f veya hatal\u0131 \u00e7\u00f6p toplama ile mi m\u00fccadele ediyorsunuz? Yapay zeka destekli 3D g\u00f6r\u00fcnt\u00fclemenin artan verim ve azalan hatalarla \u00fcretimi nas\u0131l d\u00f6n\u00fc\u015ft\u00fcrd\u00fc\u011f\u00fcn\u00fc \u00f6\u011frenin.<\/p>","protected":false},"author":875,"featured_media":105643,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_gspb_post_css":"#gspb_row-id-gsbp-5aebb46{justify-content:space-between;margin-top:0;margin-bottom:0;display:flex;flex-wrap:wrap;min-height:250px;padding:0;overflow:hidden;background-size:cover;background-repeat:no-repeat;background-position:50% 68%;background-image:url(https:\/\/solomon-3d.mesvr.tw\/wp-content\/uploads\/2024\/01\/\u2014Pngtree\u2014technological-sense-geometric-line-simple_932581-1.jpg);filter:brightness(1) contrast(1) grayscale(0)}#gspb_row-id-gsbp-5aebb46>.gspb_row__content{display:flex;justify-content:space-between;margin:0 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