Semester project 2015

Implementation of the singularity free inverse kinematic solution for redundant robots
Ben Hattar Benjamin Henri (PH)

Redundant manipulators have been designed to improve manipulability, flexibility and dexterity. One of the most fundamental issues regarding redundant manipulators is solving the inverse kinematics problem; i.e. finding a feasible joint trajectory given the end-effector’s trajectory. Solutions to the inverse kinematics problem can be formulated to handle joint physical limits, singularities, obstacle avoidance and to optimize various performance criteria, while conducting the primary end-effector motion task. The aim of this project is to implement a singularity-free inverse kinematics solution for a 7 Degree of Freedom –DoF– KUKA LWR, subject to equality and inequality/bound constraints such as joint limits, angular velocity and acceleration limits. The student will first implement this in C/C++ in a simulator in linux environment and then with the real robot, a 7 DOF arm from KUKA.
Project: Semester Project
Period: 15.06.2015 – 15.12.2015
Section(s): EL IN ME MT
Type: 20% theory, 50% software, 30% experiments,
Knowledge(s): C++, Linux
Subject(s): Robotics, Inverse Dynamic, Dynamical system
Responsible(s): Seyed Sina Mirrazavi Salehian, Nadia Figueroa

Studying the arms motions in the catching a flying object scenario
Alberto Arrighi (MT)

Dynamic motions such as catching, hitting and throwing require accurate trajectory and motor control generation. Humans can instantly execute these motions with excellent accuracy and speed. For example, the problem of catching an object requires ?getting the hands to the right place at the right time?. To successfully catch an object, humans move their hands in coordination to intercept and stop the object. The aim of this project is to study the motion of human hands when catching a large object with both hands. This project consists of two main phases. In the first phase, the student should record the kinematic of hand motion and build a data set of typical postures of arms, hands and fingers and the object. The motions are recorded by using the Optitrack motion capture system, available in the lab. In the second phase, the data are analyzed to find potential correlations between the relative postures of the arms & hands and the position of the object in the person’s workspace.
Project: Semester Project
Period: 01.06.2015 – 01.12.2015
Section(s): EL IN ME MT
Type: 20% theory, 10% software, 70% experiments,
Knowledge(s): MATLAB, C++, machine learning background
Subject(s): Motion study, Machine learning
Responsible(s): Seyed Sina Mirrazavi Salehian, Edouard Lagrue