Kaiserslautern - Fachbereich Informatik
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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.
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.
GPU Stereo Vision
(2007)
To analyze scenery obstacles in robotics applications depth information is very valuable. Stereo vision is a powerful way to extract dense range information out of two camera images. In order to unload the CPU the intensive computation can be moved to GPU, taking advantage of the parallel processing capabilities of todays consumer level graphics hardware. This work shows how an efficient implementation on the GPU can be realized utilizing the NVIDIA Cuda framework.
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.
Although today’s bipeds are capable of demonstrating impressive locomotion skills, in many aspects, there’s still a big gap compared to the capabilities observed in humans. Partially, this is due to the deployed control paradigms that are mostly based on analytical approaches. The analytical nature of those approaches entails strong model dependencies – regarding the robotic platform as well as the environment – which makes them prone to unknown disturbances. Recently, an increasing number of biologically-inspired control approaches have been presented from which a human-like bipedal gait emerges. Although the control structures only rely on proprioceptive sensory information, the smoothness of the motions and the robustness against external disturbances is impressive. Due to the lack of suitable robotic platforms, until today the controllers have been mostly applied to
simulations.
Therefore, as the first step towards a suitable platform, this thesis presents the Compliant Robotic Leg (CARL) that features mono- as well as biarticular actuation. The design is driven by a set of core-requirements that is primarily derived from the biologically-inspired behavior-based bipedal locomotion control (B4LC) and complemented by further functional aspects from biomechanical research. Throughout the design process, CARL is understood as a unified dynamic system that emerges from the interplay of the mechanics, the electronics, and the control. Thus, having an explicit control approach and the respective gait in mind, the influence of each subsystem on the characteristics of the overall system is considered
carefully.
The result is a planar robotic leg whose three joints are driven by five highly integrated linear SEAs– three mono- and two biarticular actuators – with minimized reflected inertia. The SEAs are encapsulated by FPGA-based embedded nodes that are designed to meet the hard application requirements while enabling the deployment of a full-featured robotic framework. CARL’s foot is implemented using a COTS prosthetic foot; the sensor information is obtained from the deformation of its main structure. Both subsystems are integrated into a leg structure that matches the proportions of a human with a size of 1.7 m.
The functionality of the subsystems, as well as the overall system, is validated experimentally. In particular, the final experiment demonstrates a coordinated walking motion and thereby confirms that CARL can produce the desired behavior – a natural looking, human-like gait is emerging from the interplay of the behavior-based walking control and the mechatronic system. CARL is robust regarding impacts, the redundant actuation system can render the desired joint torques/impedances, and the foot system supports the walking structurally while it provides the necessary sensory information. Considering that there is no movement of the upper trunk, the angle and torque profiles are comparable to the ones found in humans.