Conference Paper

Multiple-goal task realization utilizing redundant degrees of freedom of task and tool attachment optimization

Center for Eng., Univ. of Tokyo, Chiba, Japan
DOI: 10.1109/ICRA.2011.5980546 Conference: Robotics and Automation (ICRA), 2011 IEEE International Conference on
Source: IEEE Xplore


Minimizing the task completion time of manipulator systems is essential in order to achieve high productivity. In this paper, this problem is dealt with by utilizing the redundant degrees of freedom (DOF) of a given task and the tool attachment optimization. For example, in a vision-based inspection where a camera is held by a manipulator, the extra DOF can be brought about by allowing the camera to be translated along its approach axis or rotated about this axis when capturing images. Furthermore, the manipulator end-effector position and orientation is optimized by designing an additional linkage at the manipulator end-effector which is called a tool attachment. A 7-DOF manipulator system is used in the simulations to verify the proposed approach. Results showed that this approach can minimize the task completion time by about 17% compared to conducting only motion coordination.

Download full-text


Available from: Jun Ota,
13 Reads
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Today, robotics is an important cornerstone of modern industrial production. Robots are used for numerous reasons including reliability and continuously high quality of work. The main decision factor is the overall efficiency of the robotic system in the production line. One key issue for this is the optimality of the whole set of robotic movements for industrial applications , which typically consist of multiple atomic tasks such as welding seams, drilling holes, etc. Currently, in many industrial scenarios such movements are optimized manually. This is costly and error-prone. Therefore , researchers have been working on algorithms for automatic computation of optimal trajectories for several years. This problem gets even more complicated due to multiple additional factors like redundant kinematics, collision avoidance, possibilities of ambiguous task performance, etc. This survey article summarizes and categorizes the approaches for optimization of the robotic task sequence. It provides an overview of existing combinatorial problems that are applied for robot task sequencing. It also highlights the strengths and the weaknesses of existing approaches as well as the challenges for future research in this domain. The article is meant for both scientists and practitioners. For scientists , it provides an overview on applied algorithmic approaches. For practitioners, it presents existing solutions , which are categorized according to the classes of input and output parameters.
    Journal of Intelligent and Robotic Systems 03/2015; DOI:10.1007/s10846-015-0190-6 · 1.18 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Industrial robots are flexible machines that are currently involved in multiple production domains. Mainly their workflow consists of two alternating stages. The first stage is effective movements that are required to perform a task, e.g., welding a seam. The second stage is supporting movements that are needed to move from one effective task to another, e.g., movements between welding seams. Many effective tasks allow a certain freedom during their execution, e.g., the robot’s tool might have a certain deviation during welding. This freedom is often ignored and robots are programmed manually based on the programmer’s intuition. Nonetheless, this freedom can be used as an extra degree of freedom for robot trajectory optimization. In this thesis, we propose a formalization of this freedom for effective tasks. We refer to an effective task with a formalized freedom of execution as a relaxed effective task. Having an infinite number of ways to execute a task raises several research ques- tions: (i) how to optimize a sequence of entry points for relaxed effective tasks? (ii) how to find starting robot configurations for these tasks? (iii) how to optimize a robot trajectory for a certain relaxed task? We propose a solution concept that decomposes a problem containing all three questions into three components that can be applied in combination with each other or with other state-of-the-art approaches. The first component considers the problem of finding a sequence of effective tasks and their entry points. This problem is modeled as the Traveling Salesman Problem with Neighborhoods (TSPN) where a tour has to be found through a set of areas. We propose a Constricting Insertion Heuristic for constructing a tour and a Constricting 3-Opt for improving the tour. In the second component, the problem of adapting a tour for a robot to execute and searching for starting robot configurations is modeled as a Touring-a-sequence-of-Polygons Problem (TPP) where a tour has to be found through a given sequence of areas. We propose a modification of the Rubber-Band Algorithm (RBA). We refer to this extension as a Nested RBA. Optimization of a robot trajectory in the third component is also represented as a TPP. However, in contrast to the classic RBA where areas are constricted with a polyline, we propose an extension of the RBA called Smoothed RBA where areas are constricted with a smooth curve which leads to a minimal cost robot trajectory.
    07/2015, Degree: Dr.-Ing., Supervisor: Frank Ortmeier

We use cookies to give you the best possible experience on ResearchGate. Read our cookies policy to learn more.