The demand for testing aerospace structures on Earth before they are launched into space has led to the development of equipment that is able to simulate orbit conditions, namely zero gravity. Several passive solutions have been proposed to perform offloading testing on Earth. However, they present limitations and lack the flexibility normally required by the complexity of the pathways. Active zero gravity emulation systems have been developed to mitigate the difficulties of the passive ones. Moreover, the emergence of robotic arms with the ability to perform complex and easily reprogrammable motion and force-controlled trajectories has opened the possibility of creating robot-based gravity compensation systems. This paper proposes and evaluates a solution for the gravity offloading testing of space devices based on currently available industrial robots. This solution takes advantage of the functionalities of collaborative robots, namely built-in force controllers, together with custom auxiliary subsystems. A setup was arranged to allow the communication between the robot controller and a computer running an internet of things (IoT) platform based on Node-RED so as to connect and control all components of the offloading system. Multiple robot control techniques were designed and tested based on several approaches employing impedance control functionalities and sensing data to create a closed-loop system. The results obtained are within the validation criteria, creating conditions to affirm for the application in question that the gravity compensation was achieved with success using the robot.
<abstract><p>In this paper, we propose a motion control system for a low-cost differential drive mobile robot. The robotic platform is equipped with two driven wheels powered by Beckhoff motors, instrumented with incremental encoders. The control system is designed and implemented using Beckhoff's TwinCAT 3 automation software, running on an industrial PC. The system is tested and experimentally tuned to achieve optimal performance. The method allows addressing both odometry motion accuracy and motion correction in order to obtain minimum trajectory errors. Test results on linear and angular robot trajectories show errors below 0.02 and 0.03%, respectively, after tuning of the motion parameters. The proposed approach can be expanded, tweaked and applied to other differential drive TwinCAT 3 based robotic solutions. This will contribute to expanding mobile robot applications to a variety of fields, such as industrial automation, logistics, warehouse management, health care, ocean and space exploration and a variety of other industrial and non-industrial activities.</p></abstract>
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