Performance evaluation of operational space control and visual servoing for complex 3D robotic arm applications

Nowadays a lot of effort is put into trying to reproduce human behavior. Thus, neuroscience know-how can be leveraged to create a variety of robotic applications in which neural substrates of motor skill learning are transferred to artificial machines in terms of perception and dexterity. An important benchmark for testing analogies and differences in learning techniques can be presented through a game. In this project, control systems are investigated to allow a robotic arm to play Jenga. In particular, this master’s thesis aims at comparing motion control in Operational Space and Visual Servoing control techniques. Therefore, keywords are path planning, force interaction, mechanical design and sensor fusion. The control systems development is performed through proprioceptive and exteroceptive sensors. Such information allows building the overall strategy for the game, characterized by an analytical footprint combined with empirical considerations. Forces acting on a generic block are studied from a theoretical viewpoint that includes geometrical dimensions, material properties and physical constraints. Afterward, the results of such analysis are used to provide a tactic for playing Jenga. To this end, a force sensor is used with 3-D printed support, directly mounted on the manipulator to provide real-time measurements. In addition, a RealSense camera is attached to the robot’s end-effector in a well-known configuration, also called eye-in-hand. It allows the construction of control systems based on visual information. Also in this case, the task involves the design of the camera support equipment. In particular, two control techniques are tested: operational space control and visual servoing. The first scheme consists of a priori planned trajectory in Cartesian space which, receiving a 3-D point inside the robot workspace, generates the waypoints to be followed in order to achieve such pose. This control method guarantees the convergence to the desired pose through a PID controller that ensures a small tracking error. For doing this, the functionalities of MoveIt, a planning framework in Robotic Operating System (ROS), are leveraged through a customized inverse kinematic solver and planning adapter. The second control method is a real-time feedback control law, designed to improve task accuracy. Indeed, it has been designed to respond quickly to world noise, lack of measurements and kinematic tolerances. The eye-in-hand configuration provides the feedback information for the position-based visual servoing, a control scheme for actuating the manipulator according to the object pose. In this case, the controller has to be designed to accept the velocity data generated from the visual control loop. The general approach for system validation involves unit testing on individual components. Then, the different modules are combined and tested together. In conclusion, the experimental results are reported in the last chapter, highlighting analogies and discrepancies with respect to similar works. The main differences arise in the type of manipulator employed, Jenga’s tactic and block extraction.