![]() In industry settings, this problem has been commonly referred to as bin-picking and also historically addressed as one of the greatest robotic challenges in manufacturing automation. Industrial robots, however, require a supplementary cognitive sensing system that can acquire and process information about the environment and guide the robot to grasp arbitrarily placed objects out of the bin. ![]() Traditionally, grasping and sorting randomly positioned objects requires human resources, which is a very monotonous task, lacks creativity and is no longer sustainable in the context of smart manufacturing. As more electric vehicles circulate, the electric harness market is also expected to witness growth, since electric harnesses are used more in electric vehicles than in conventional fossil fuel vehicles. This increase in sales is mainly due to increased regulatory standards imposed by various organizations and governments to limit emissions and promote zero-emission automobiles. By the end of 2026, annual sales of battery-powered electric cars are expected to exceed 7 million and to contribute about 15% of total vehicle sales. In 2020, global sales of plug-in electric cars increased 39% from the previous year to 3.1 million units. According to recent market reports, the rise of electric vehicles is driving the market. The Electric Distribution System (EDS) has to constantly adapt to these changes in terms of concept quality and technological requirements. Vehicles are becoming more comfortable, safer, more efficient and less polluting, but they are also increasingly complex systems with lots of electronics. The automobile industry has always imposed the growth of the cable assembly industry and is characterized by many technological changes in a short period of time. The advantage of this approach over other solutions is the ability to accurately detect and grasp small objects through a low-cost 3D camera even when the image resolution is low, benefiting from the power of machine learning algorithms. Connectors are identified through a 3D vision system, consisting of an Intel RealSense camera for object depth information and the YOLOv5 algorithm for object classification. This article proposes a bin-picking solution for classification, selection and separation, using a two-finger gripper, of these connectors for reuse in a new operation of removal and insertion of seals. These connectors are not trash and need to be reused. Consequently, faulty connectors are dumped into boxes, piling up different types of references. Due to the huge variety of references and connector configurations, layout errors sometimes occur during seal insertion due to changed references or problems with the seal insertion machine. Seals are inserted manually or, more recently, through robotic stations. Holes not connected with wires need to be sealed, mainly to guarantee the tightness of the cable. ![]() Depending on the car model and its feature packs, a connector can interface with a different number of wires, but the connector holes are the same. Traditionally, this sector is based on strong human work manufacturing and the need arises to make the digital transition, supported in the context of Industry 4.0, allowing the automation of processes and freeing operators for other activities with more added value. The automotive sector has always been in a state of constant growth and change, which also implies constant challenges in the wire harnesses sector, and the emerging growth of electric cars is proof of this and represents a challenge for the industry. This paper presents the development of a bin-picking solution based on low-cost vision systems for the manipulation of automotive electrical connectors using machine learning techniques.
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