Human-Robot Interaction for D2RA

Year: 2018-2022
Project leader: Henriette Bier
Project team RB: Henriette Bier, Sina Mostafavi, Yu-Chou Chiang, Arwin Hidding, Vera Laszlo, Amir Amani and MSc students from TUD and DIA
Collaborators / Partners: DIA, DRI

In a first phase, the Design-to-Robotic-Assembly project showcased an integrative approach for stacking architectural elements with varied sizes in multiple directions. Several processes of parametrization, structural analysis, and robotic assembly were algorithmically integrated into a Design-to-Robotic-Production method. This method was informed by the systematic control of density, dimensionality, and directionality of the elements. It was tested by building a one-to-one prototype, involving development and implementation of computational design workflow coupled with robotic kinematic simulation that is enabling the materialization of a multidirectional and multidimensional assembly system. The assembly was human assisted.

In a second phase, human-robot interaction is being developed. The robotic arm using ROS, ML, and computer vision techniques, such as OpenCV and DNN will be developed, to find location of nodes, detect the related linear elements, pick them with a gripper and transfer them to the intended location (in the next proximity of the node) one by one, while considering obstacle avoidance (human safety). Finally, the human will navigate the arm with his/her hand, and in order to move it to its final location (placed in the node). For that the following steps are considered: (1) Localization by creating a map of the environment (including nodes, linear elements, and human location); (2) Robot's location by object detections (using OpenCV and ROS to detect the correct node, and do the corresponding action); (3) Controlling and navigating the gripping toward the objects in order to pick up the objects; (4) Human action involving controlling the gripper manually to insert the linear element in the node.

The object detection has been implemented using the GUI provided by the ROS package for object recognition, in which objects can be 'marked' and saved for future detection. The detector will identify the objects in camera images and publish the details of the object through a topic. Using a 3D sensor, it can estimate the depth and orientation of the object.