Reaching for a moving object with YuMi
Marjorie Marie Joséphine Lasson (ME)
The use of multi-arm robotic systems allows for highly complex manipulation of heavy objects that would otherwise be impossible for a single-arm robot. In our work , we propose a unified coordinated control architecture for reaching and grabbing a moving object by a multi-arm robotic system. Due to the complexity of the task and the system, each arm must coordinate not only with the object’s motion but also with the motion of other arms, in both task and joint spaces. At the task-space level, the proposed unified dynamical system coordinates the motion of each arm with the rest of the arms and the resultant motion of the arms with that of the object. At the joint space level, the coordination between the arms is achieved by introducing a centralized inverse kinematics (IK) solver under data-driven self-collision avoidance constraints; formulated as a quadratic programming problem (QP) and solved in real-time.
The aim of this project is to implement the unified framework on YuMi; a dual-arm robotic system developed by ABB. The student will first review the related literatures and familiarize him/herself with the Robot Operating system (ROS) and the provided libraries [2,3,4]. The proposed control architecture will then be implemented in C/C++ in a simulator in Linux environment and, finally, with the real robot for performing a handover scenario for where an operator holds a tray and hands it over to YuMi.
 Mirrazavi Salehian, S. S., Figueroa, N. and Billard, A. (2017) A Unified Framework for Coordinated Multi-Arm Motion Planning. (Under review).
 Mirrazavi Salehian, S. S., Centralized motion generator, https://github.com/sinamr66/Multiarm_ds
 Mirrazavi Salehian, S. S., Centralized IK solver, https://github.com/sinamr66/QP_IK_solver
 Mirrazavi Salehian, S. S., Constructing data set for SCA, https://github.com/sinamr66/SCA_data_construction
Project: Semester Project
Period: 01.01.2018 – 01.08.2018
Section(s): EL IN MA ME MT MX PH
Type: 20% theory 60% software, 20% hardware
Knowledge(s): C++, ROS, Machine learning, Robotics
Subject(s): Motio planning,
Responsible(s): Seyed Sina Mirrazavi Salehian