Design, analysis, and simulation of a humanoid robotic arm applied to catching

• Main Author: Yesmunt, Garrett Scot
• Other Authors: Wasfy, Tamer
• Language: en_US
• Published: 2015
• Subjects: robot; humanoid; robotic catching; ball catching; robotic arm; Androids > Research; Autonomy > Research; Intellect > Research; Biomimicry > Research > Methodology; Artificial intelligence > Biological applications; Biologically-inspired computing > Research > Methodology; Robotics > Design and construction > Research > Methodology; Robotics > Human factors; Robots > Kinematics; Automatic control; Robots > Programming; Catching (Baseball); Mechatronics > Computer simulation; Autonomous robots; Control theory > Data processing; Dynamics > Data processing; Dynamics > Computer simulation; Human-robot interaction > Research > Methodology
• Online Access: http://hdl.handle.net/1805/5610

Indiana University-Purdue University Indianapolis (IUPUI) === There have been many endeavors to design humanoid robots that have human characteristics such as dexterity, autonomy and intelligence. Humanoid robots are intended to cooperate with humans and perform useful work that humans can perform. The main advantage of humanoid robots over other machines is that they are flexible and multi-purpose. In this thesis, a human-like robotic arm is designed and used in a task which is typically performed by humans, namely, catching a ball. The robotic arm was designed to closely resemble a human arm, based on anthropometric studies. A rigid multibody dynamics software was used to create a virtual model of the robotic arm, perform experiments, and collect data. The inverse kinematics of the robotic arm was solved using a Newton-Raphson numerical method with a numerically calculated Jacobian. The system was validated by testing its ability to find a kinematic solution for the catch position and successfully catch the ball within the robot's workspace. The tests were conducted by throwing the ball such that its path intersects different target points within the robot's workspace. The method used for determining the catch location consists of finding the intersection of the ball's trajectory with a virtual catch plane. The hand orientation was set so that the normal vector to the palm of the hand is parallel to the trajectory of the ball at the intersection point and a vector perpendicular to this normal vector remains in a constant orientation during the catch. It was found that this catch orientation approach was reliable within a 0.35 x 0.4 meter window in the robot's workspace. For all tests within this window, the robotic arm successfully caught and dropped the ball in a bin. Also, for the tests within this window, the maximum position and orientation (Euler angle) tracking errors were 13.6 mm and 4.3 degrees, respectively. The average position and orientation tracking errors were 3.5 mm and 0.3 degrees, respectively. The work presented in this study can be applied to humanoid robots in industrial assembly lines and hazardous environment recovery tasks, amongst other applications.