Application of Perception Technologies for Robotic Manipulation

Robots are highly present in manufacturing processes, being relevant assets for manufacturing companies that develop tasks that increase the value of the product. Those tasks are normally considered repetitive and in some cases hazardous for humans, as could cause boredom and lead to injuries (for example, painting a car) or directly because the tasks that are developed are hazardous (such as welding or the manipulation of toxic elements). The use of robots in manufacturing processes improve both the safety and the efficiency of processes in a company. Nowadays, the range of tasks that are developed by robots is getting wider, and are able to develop tasks that require higher dexterity and precision. To improve the performance of robots, the use of visual perception techniques and the use of Collaborative Robots are two of the main research fields in Robotics, with the purpose of giving robots the possibility to interact with humans without compromising worker´s safety and developing the tools to overcome changes in the environment that could compromise the development of the task. The aim of this thesis is to demonstrate that Cobots can be an adequate solution to work in manufacturing environments developing dexterous and precise tasks that nowadays are developed by humans, by putting together the two main challenges that manufacturing industries are facing nowadays: Cobots and Visual Perception. This demonstration will be done by using a Cobot to assemble a connection panel by using visual perception techniques to detect cables. In addition to the detection of cables, the manipulation of deformable linear objects such as cables is faced in this project. To fulfill the objectives of the thesis, the first part of the document presents a review of the literature that is related to those research fields, highlighting different Visual Perception techniques and the grasp taxonomies. After this literature review, an approach taking into account the recent advances in these fields is exposed to solve the problem. After the project development, it has been proved that by using simple image processing operations high-accuracy (97,14% testing 25 different situations) can be achieved to solve an object-detection problem. Indeed, the time consumed to manipulate the cables is also reduced compared to humans