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Safe robotic grasping of fruits and vegetables

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  1. Tez No: 771871
  2. Yazar: KADİR DEMİRAĞ
  3. Danışmanlar: DR. ALİ LEYLAVİ SHOUSHTARİ
  4. Tez Türü: Yüksek Lisans
  5. Konular: Bilim ve Teknoloji, Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Biyoloji, Science and Technology, Computer Engineering and Computer Science and Control, Biology
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2021
  8. Dil: İngilizce
  9. Üniversite: Wagenıngen Unıversıteıt
  10. Enstitü: Yurtdışı Enstitü
  11. Ana Bilim Dalı: Belirtilmemiş.
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: Belirtilmemiş.

Özet

The robotic harvesting of fruit and vegetables is still not common because of the unmatured grasping performance of harvesting robots. The main reason for this incapability is the wide variety of crops in terms of color, shape, size, weight, and surface features. The biggest challenge for robots is to deal with these varieties. The objective of this thesis was a safe grasping of fruits and vegetables. Thus, it was aimed to provide the weight and friction coefficient of contact surface information to the robot. Therefore, the robot can determine the grasp force that should be applied for a dexterous grasp of the natural objects. To achieve this objective, the main question was formulated as“to what extent can a safe grasp be achieved using sensors to estimate the weight of agricultural products and the friction coefficient of the contact surface?”For finding an answer to this question, a six-axis Robotiq FT-300 force/torque sensor was mounted on the wrist of an ABB 1200 robotic arm to estimate the weight of objects. Then, a Robotiq 2F-140 gripper was equipped with Interlink FSR 402 force-sensitive resistors (FSR) to acquire normal force data. For the experiments, different kinds of agricultural products(apple, lemon, sweet pepper, tomato, orange, and melon) were grasped and lifted by the robot. Gripper fingertips were covered with a thumb of a rubber glove, and the experiments were repeated. The weight of objects and the normal forces acting on the objects were measured by the sensors and recorded to the computer during this process. For data acquisition, Robotic Operating System (ROS) and Arduino software were used. Consequently, the measured weight and normal forces were used to calculate the friction coefficient values on the sensor-object surface for each object. To evaluate the performance of the measurements, the same kind of natural objects were lifted with the robot that used these friction coefficient values to adjust the grip force. The robot was successful in 23 of 25 manipulation attempts for five natural objects where the gripper was not covered with the rubber. On the other hand, 7 out of 25 attempts slipped or dropped with the rubber coating. No damage was observed on the objects. Thus, the success rate was 92% and 72%, respectively.

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