Flexible pick and place architecture using ROS framework

Abstract

The need for efficient automation methods has prompted the rapid development in the field of Robotics. The development of intelligent robots leads to the ability of them becoming an operator highly efficient and able to adapt to a wide range of problems. Still, despite of the several robotic solutions available, the majority of current industrial robots do not use the Robotic Operative System (ROS) and have limitations in terms of autonomously correct errors during their tasks. An important aspect to consider when developing a new robot is the selection of the methodology for recognition of the objects to be handled. In this paper, it will be presented an approach with enough flexibility to be potentially applicable to different scenarios of object recognition and handling normally found in industrial environment. The main aim is for this application to be applied to pick and place routines in robotics. Moreover it will be presented one particular case study that used the proposed approach, the European Robotics Challenges (EUROC) -a challenge aiming to develop a robot for shop floor logistics and manipulation. Our proposed approach is based on the three tiers paradigm: 1-recognition/sensing tier, 2-effector tier and 3-the control tier and was built using the ROS framework. Since ROS is becoming one of the most useful tools in robotics nowadays, the possibility of using a methodology able to be expressed in ROS allows for the development of a standard approach to pick and place operations. Another advantage of our proposed pick and place approach is the ability to have a robot safely and efficiently inserted in an unknown environment. This is possible due to the insertion of an adaptive control tier in our methodology. The proposed approach can be valuable in the field of robotics and can be potentially applied in multiple tasks and it has already allowed us to advance to the next stage of the already mention challenge.

Robot Operating System 2 (ROS2)-Based Frameworks for Increasing Robot Autonomy: A Survey

“…Access to depth information about the surrounding environment and manipulated objects enables cobots to successfully complete their tasks despite dynamic environments and changing scenarios. Specifically, the recognition of the spatial pose of the object and of its size enables tasks such as flexible grasping [76][77][78] and flexible palletization/packaging [79]. While commercial robots are primarily trained to recognize and grasp specific, identical objects from the same surface (usually a flat surface), with additional information about the position and size of objects, these can be used to adapt tasks from static conditions to changing conditions (e.g., change in the type of object, its spatial location or palleting/packaging) without the need of manual reprogramming.…”