This paper deals with the handling of deformable linear objects (DLOs), such as hoses, wires or leaf springs. It investigates the a priori knowledge about the 6-dimensional force/torque signal for a changing contact situation between a DLO and a rigid polyhedral obstacle. The result is a complete list, containing for each contact change the most significant combination of force/torque signal components together with a description of the expected signal curve. This knowledge enables the reliable detection of changes in the DLO contact situation and with it the implementation of sensor-based manipulation skills for all possible contact changes.
Manipulating Deformable Linear Objects: Manipulation Skill for Active Damping of Oscillations
(2002)
While handling deformable linear objects (DLOs), such as hoses, wires or leaf springs, with an industrial robot at high speed, unintended and undesired oscillations that delay further operations may occur. This paper analyzes oscillations based on a simple model with one degree of freedom (DOF) and presents a method for active open-loop damping. Different ways to interpret an oscillating DLO as a system with 1 DOF lead to translational and rotational adjustment motions. Both were implemented as a manipulation skill with a sepa-rate program that can be executed immediately after any robot motion. We showed how these manipulation skills can generate the needed adjustment motions automatically based on the readings of a wrist-mounted force/torque sensor. Experiments demonstrated the effectiveness under various conditions.
Handhabung deformierbarer linearer Objekte: Programmierung mit verschiedenen Manipulation-Skills
(2002)
Diese Arbeit beschreibt verschiedene Bewegungsprimitive zur Lösung einiger häufig auftre-tender Probleme bei der Handhabung von deformierbaren linearen Objekten. Anhand der beispielhaften Montage einer Feder wird die Nützlichkeit der verschiedenen Manipulation-Skills im einzelnen, aber auch deren Kombination dargestellt.
A new and systematic basic approach to force- and vision-based robot manipulation of deformable (non-rigid) linear objects is introduced. This approach reduces the computational needs by using a simple state-oriented model of the objects. These states describe the relation between the deformable and rigid obstacles, and are derived from the object image and its features. We give an enumeration of possible contact states and discuss the main characteristics of each state. We investigate the performance of robust transitions between the contact states and derive criteria and conditions for each of the states and for two sensor systems, i.e. a vision sensor and a force/torque sensor. This results in a new and task-independent approach in regarding the handling of deformable objects and in a sensor-based implementation of manipulation primitives for industrial robots. Thus, the usage of sensor processing is an appropriate solution for our problem. Finally, we apply the concept of contact states and state transitions to the description of a typical assembly task. Experimental results show the feasibility of our approach: A robot performs several contact state transitions which can be combined for solving a more complex task.
In this chapter, the quantitative numerical simulation of the behavior of deformable linear objects, such as hoses, wires and leaf springs is studied. We first give a short review of the physical approach and the basic solution principle. Then, we give a more detailed description of some key aspects: We introduce a novel approach concerning dynamics based on an algorithm very similar to the one used for (quasi-) static computation. Then, we look at the plastic workpiece deformation, involving a modified computation algorithm and a special representation of the workpiece shape. Then, we give alternative solutions for two key aspects of the algorithm, and investigate the problem of performing the workpiece simulation efficiently, i.e., with desired precision in a short time. In the end, we introduce the inverse modeling problem which must be solved when the gripper trajectory for a given task shall be generated.