Robotic assembly of complex planar parts: An experimental evaluation

In this paper we present an experimental evaluation of automatic robotic assembly of complex planar parts. The torque-controlled DLR light-weight robot, equipped with an on-board camera (eye-in-hand configuration), is committed with the task of looking for given parts on a table, picking them, and inserting them inside the corresponding holes on a movable plate. Visual servoing techniques are used for fine positioning over the selected part/hole, while insertion is based on active compliance control of the robot and robust assembly planning in order to align the parts automatically with the hole. Execution of the complete task is validated through extensive experiments, and performance of humans and robot are compared in terms of overall execution time

Force control of heavy lift manipulators for high precision insertion tasks

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, June 2005."May 2005." Leaf 81 blank.Includes bibliographical references (leaves 67-70).The inherent strength of robotic manipulators can be used to assist humans in performing heavy lifting tasks. These robots reduce manpower, reduce fatigue, and increase productivity. This thesis deals with the development of a control system for a robot being built for this purpose. The task for this robot is to lift heavy payloads while performing complex insertion tasks. This task must be completed on the deck of a naval vessel where possible disturbances include wind, rain, poor visibility, and dynamic loads induced by a swaying deck. The primary objective of the controller being designed here is to allow for insertion of the payload despite tight positioning tolerances and disturbances like surface friction, joint friction, and dynamic loads from ship motions. A control structure designed for intuitive interaction between the robot and operator is analyzed and shown to be stable using an established environment interaction model. The controller is shown to perform within established specifications via numerical simulation based on simple user inputs. An additional objective of this controller design is to prevent part jamming during the insertion task. With a large, powerful manipulator, the chances of a jam occurring is high. Without the use of bilateral force feedback, it will be difficult for the operator feel when these jams will occur and there will be no information about how to prevent them. This thesis analyzes the geometry and mechanics of the jamming problem and derives a control system to assist the user in preventing these jams. These methods can be extended to other insertion tasks simply by specifying the appropriate geometry.by Matthew A. DiCicco.S.M

Robotic Trajectory Tracking: Position- and Force-Control

This thesis employs a bottom-up approach to develop robust and adaptive learning algorithms for trajectory tracking: position and torque control. In a first phase, the focus is put on the following of a freeform surface in a discontinuous manner. Next to resulting switching constraints, disturbances and uncertainties, the case of unknown robot models is addressed. In a second phase, once contact has been established between surface and end effector and the freeform path is followed, a desired force is applied. In order to react to changing circumstances, the manipulator needs to show the features of an intelligent agent, i.e. it needs to learn and adapt its behaviour based on a combination of a constant interaction with its environment and preprogramed goals or preferences. The robotic manipulator mimics the human behaviour based on bio-inspired algorithms. In this way it is taken advantage of the know-how and experience of human operators as their knowledge is translated in robot skills. A selection of promising concepts is explored, developed and combined to extend the application areas of robotic manipulators from monotonous, basic tasks in stiff environments to complex constrained processes. Conventional concepts (Sliding Mode Control, PID) are combined with bio-inspired learning (BELBIC, reinforcement based learning) for robust and adaptive control. Independence of robot parameters is guaranteed through approximated robot functions using a Neural Network with online update laws and model-free algorithms. The performance of the concepts is evaluated through simulations and experiments. In complex freeform trajectory tracking applications, excellent absolute mean position errors (<0.3 rad) are achieved. Position and torque control are combined in a parallel concept with minimized absolute mean torque errors (<0.1 Nm)