The first work package was aimed at developing a planning algorithm which, given the description of a manufacturing task, plans the actions of all robots in a human-robot team to perform the task. Below are the three main tasks, which correspond to different versions of the planner:
1. Planning stable grasps: The first task in this work package was the development of an algorithm that can plan stable grasps individually for a sequence of operations. The algorithm particularly addressed the problem of stability checking, given a multi-manipulator grasp on the object, and an external force. This basic algorithm was presented at the 2nd UK Robot Manipulation Workshop, and the associated abstract is attached in the final report.
2. Planning to transfer between grasps: The second task in this work package was to improve the planning algorithm, to enable the robot to smoothly transition between sequential forceful operations. The paper presenting this algorithm was submitted, accepted, and presented at the IEEE/RSJ IROS 2018 conference. This paper is attached in the final report.
3. Using environmental surfaces: Finally, the planning algorithm was extended to work not only using the grippers of the robot manipulators, but also using environmental contacts, e.g. a shared table surface between the robot and the human. This extended algorithm has been submitted to the IEEE/RSJ IROS 2019 conference and is currently under review. The submitted manuscript is attached to the final report.
The second work package in this project was about the development of models that represent the human choices/characteristics:
1. We developed a way to measure the amount of space between the human and the robot during the collaboration, and we provided an optimization framework to maximize this distance while still holding the object at a pose appropriate for the collaboration. The results were submitted, accepted, and presented at the IEEE-RAS Humanoids 2018 conference. This paper is attached to the final report.
2. We developed a model to capture the forces applied by the human during the forceful operations. Take the example of drilling and cutting, where the uncertainty in the drilling and cutting directions can vary as the human applies these operations. Our planner was supposed to generate robot grasps that can resist all possible forces and torques within this distribution. We proposed a conic model such that if we check the stability at all the edges of this polyhedral cone, then we can guarantee stability inside of the cone. This model is part of a journal paper that we are finalizing.
The final work package focused on integrating the planning algorithm with a real robot system:
1. The first task was to bring together the planning algorithm implemented in Python programming language, with a kinematic robot modelling and planning software OpenRAVE (
http://openrave.org/(se abrirá en una nueva ventana)) a Baxter robot with two manipulators and grippers, and a three-dimensional planning model (called a URDF model) of the robot. We made use of external libraries such as or_urdf (
https://github.com/personalrobotics/or_urdf(se abrirá en una nueva ventana)) to be able to instantiate a Baxter model within OpenRAVE. We used Robot Operating System (ROS) to tie these components together and have a complete system. We used this system for our experiments and for demonstrations that we performed to visitors to our laboratory. A video of this system can be seen in:
https://www.youtube.com/watch?v=IHti307yGFY(se abrirá en una nueva ventana)2. To perform the force modelling task, we needed to collect accurate time-based force-torque data during human-robot interaction. The Baxter robot we used as the main system did not provide such sensory accuracy. Therefore we used an existing UR5 robot arm that we fit with a Robotiq FT100 force torque sensor. We connected these components with the rest of the system using ROS, which enabled us to collect real-time and accurate force-torque measurements at 100 Hz. This data was then used in building the actual conic force models mentioned above in Work Package 2.
3. The final task in this work package was the development of a graphical user interface. The graphical interface enabled a user to approach the robot, select new operations on an object, and send these to the planner for the robot to then execute, streamlining the complete interaction between the human and the robot.
