A new and systematic approach to machine 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 of the object with respect to an obstacle and are derived from the object image and its features. Therefore, the object is segmented from a standard video frame using a fast segmentation algorithm. Several object features are presented which allow the state recognition of the object while being manipulated by the robot.
The task of handling non-rigid one-dimensional objects by a robot manipulation system is investigated. To distinguish between different non-rigid object behaviors, five classes of deformable objects from a robotic point of view are proposed. Additionally, an enumeration of all possible contact states of one-dimensional objects with polyhedral obstacles is provided. Finally, the qualitative motion behavior of linear objects is analyzed for stable point contacts. Experiments with different materials validate the analytical results.
This paper deals with the robust manipulation of deformable linear objects such as hoses or wires. We propose manipulation based on thequalitative contact state between the deformable workpiece and a rigid environment. First, we give an enumeration of possible contact states and discuss the main characteristics of each state. Second, we investigate the transitions which are possible between the contact states and derive criteria and conditions for each of them. Finally, we apply the concept of contact states and state transitions to the description of a typical assembly task.
This paper deals with the problem of picking-up deformable linear workpieces such as cables or ropes with an industrial robot. First, we give a motivation and problem definition. Based on a brief conceptual discussion of possible approaches we derive an algorithm for picking-up hanging deformable linear objects using two light barriers as sensor system. For this hardware, a skill-based approach is described and the parameters and major influence factors are discussed. In an experi- mental study, the feasibility and reliability under diverse conditions are investigated. The algorithm is found to be very reliable, if certain boundary conditions are met.
In this paper, we investigate the efficient simulation of deformable linear objects. Based on the state of the art, we extend the principle of minimizing the potential energy by considering plastic deformation and describe a novel approach for treating workpiece dynamics. The major influence factors on precision and computation time are identified and investigated experimentally. Finally, we discuss the usage of parallel processing in order to reduce the computation time.
A new problem for the automated off-line programming of industrial robot application is investigated. The Multi-Goal Path Planning is to find the collision-free path connecting a set of goal poses and minimizing e.g. the total path length. Our solution is based on an earlier reported path planner for industrial robot arms with 6 degrees-of-freedom in an on-line given 3D environment. To control the path planner, four different goal selection methods are introduced and compared. While the Random and the Nearest Pair Selection methods can be used with any path planner, the Nearest Goal and the Adaptive Pair Selection method are favorable for our planner. With the latter two goal selection methods, the Multi-Goal Path Planning task can be significantly accelerated, because they are able to automatically solve the simplest path planning problems first. Summarizing, compared to Random or Nearest Pair Selection, this new Multi-Goal Path Planning approach results in a further cost reduction of the programming phase.