{"id":117272,"date":"2024-07-10T15:44:06","date_gmt":"2024-07-10T13:44:06","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/roboekskavator-obuchili-priczelno-shvyryat-kamni"},"modified":"2024-07-10T15:44:06","modified_gmt":"2024-07-10T13:44:06","slug":"roboekskavator-obuchili-priczelno-shvyryat-kamni","status":"publish","type":"post","link":"https:\/\/prohoster.info\/hu\/blog\/news\/roboekskavator-obuchili-priczelno-shvyryat-kamni","title":{"rendered":"A robot excavator has been trained to precisely throw stones.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Experienced excavator operators are familiar with a trick that allows them to send the bucket's contents beyond the reach of the arm. The neural network-equipped robot excavator has proven to be a diligent student, who has also managed to master<\/a><\/noindex> the accurate throwing of stones beyond the reach of the arm. Next up is throwing bulk materials and increasing accuracy for bucket work at different heights.<\/p>\n

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A sv\u00e1jci ETH Z\u00fcrich kutat\u00f3i sz\u00e1moltak be a robottal f\u0171t\u00f6tt munkaer\u0151 tanul\u00e1si folyamat\u00e1r\u00f3l, amelynek c\u00e9lja a kan\u00e1l tartalm\u00e1nak prec\u00edz manipul\u00e1l\u00e1sa. A meger\u0151s\u00edt\u00e9ses tanul\u00e1son alapul\u00f3 neur\u00e1lis h\u00e1l\u00f3zatot arra k\u00e9pezt\u00e9k ki, hogy labd\u00e1kat \u00e9s k\u00f6veket dobjon egy meghat\u00e1rozott pontra, amely a kar\u00e1nak el\u00e9rhet\u0151s\u00e9ge (7,5 m) t\u00e1vols\u00e1g\u00e1n t\u00fal (ak\u00e1r 9,5 m-re) volt. Az ilyen m\u0171veletek seg\u00edthetnek a robottechnik\u00e1nak sz\u00e9lesebb k\u00f6r\u0171 feladatok elv\u00e9gz\u00e9s\u00e9ben, mik\u00f6zben cs\u00f6kkentik a mozg\u00e1shoz sz\u00fcks\u00e9ges energiafogyaszt\u00e1st, \u00e9s biztons\u00e1gosabb\u00e1 teszik a munk\u00e1t.<\/p>\n

A kotr\u00f3g\u00e9p k\u00e9t szabads\u00e1gi fokkal v\u00e9gezte a kan\u00e1llal val\u00f3 megragad\u00e1st \u00e9s dob\u00e1st, amely nem volt szil\u00e1rd r\u00f6gz\u00edtve a karra. A dob\u00e1sok egyenes vonalban t\u00f6rt\u00e9ntek, amikor csak a kar volt m\u0171k\u00f6d\u00e9sben, valamint a f\u00fclke elforgat\u00e1s\u00e1val. Az ut\u00f3bbi esetben a pontoss\u00e1g egy kicsit alacsonyabb volt, de b\u00e1rmelyik esetben a l\u00f6ved\u00e9k az ir\u00e1nyz\u00e1si pontt\u00f3l legfeljebb 30\u201340 cm-re t\u00e9rt el. <\/p>\n

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Researchers trained the neural network using an upgraded 12-wheel excavator Menzi Muck M545. Previously, they had taught the excavator a number of non-trivial operations, for example, how to build a stable wall from unprepared stone blocks. The excavator assessed the balance of the stones itself and constructed a solid stone enclosure. For precise autonomous work on site, the excavator uses sensors mounted on it to build a model of the surrounding space in which it performs the assigned tasks.<\/p>\n

Forr\u00e1s:<\/strong><\/p>\n