Robotic technologies help to improve productivity and quality in the industry. For instance, robots can collaborate with humans in the transportation of objects.
Human-robot collaboration is possible in various tasks, not just industrial. Image credit: Pxhere, CC0 Public Domain
This task poses several challenges. Firstly, the robot should share the load with a human effectively. Also, rotations and translations must be controlled independently. Moreover, co-transportation of deformable objects is problematic. A recent paper on arXiv.org aims to tackle these challenges.
The researchers propose an adaptive framework that merges haptic and human movement information. That way, a mobile manipulator can co-transport objects with unknown deformability during collaboration with a human. Also, a novel algorithm is developed to enable human partners to communicate their intention of rotating an object by using the torso and hand movements.
Experiments confirmed the effectiveness of the presented framework against the admittance controller.
In this work, we introduce an adaptive control framework for human-robot collaborative transportation of objects with unknown deformation behaviour. The proposed framework takes as input the haptic information transmitted through the object, and the kinematic information of the human body obtained from a motion capture system to create reactive whole-body motions on a mobile collaborative robot. Moreover, the designed framework delivers an intuitive way to rotate the object by processing the human torso and hand movements. In order to validate our framework experimentally, we compared its performance with an admittance controller during a co-transportation task of a partially deformable object. We additionally demonstrate the potential of the framework while co-transporting rigid (aluminum rod) and deformable (rope) objects. A mobile manipulator which consists of an Omni-directional mobile base, a collaborative robotic arm, and a robotic hand is used as the robotic partner in the experiments. Quantitative and qualitative results of a 12-subjects experiment show that the proposed framework can effectively deal with objects of unknown deformability and provides intuitive assistance to human partners.
Research paper: Sirintuna, D., Giammarino, A., and Ajoudani, A., “Human-Robot Collaborative Carrying of Objects with Unknown Deformation Characteristics”, 2022 . Link to the article: https://arxiv.org/abs/2201.10392