Kaiserslautern - Fachbereich Informatik
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This paper presents a new approach to parallel motion planning for industrial robot arms with six degrees of freedom in an on-line given 3D environment. The method is based on the A*-search algorithm and needs no essential off-line computations. The algorithm works in an implicitly descrete configuration space. Collisions are detected in the cartesian workspace by hierarchical distance computation based on the given CAD model. By decomposing the 6D configuration space into hypercubes and cyclically mapping them onto multiple processing units, a good load distribution can be achieved. We have implemented the parallel motion planner on a workstation cluster with 9 PCs and tested the planner for several benchmark environments. With optimal discretisation, the new approach usually shows linear, and sometimes even superlinear speedups. In on-line provided environments with static obstacles, the parallel planning times are only a few seconds.
We present a parallel path planning method that is able to automatically handle multiple goal configurations as input. There are two basic approaches, goal switching and bi-directional search, which are combined in the end. Goal switching dynamically selects a fa-vourite goal depending on some distance function. The bi-directional search supports the backward search direction from the goal to the start configuration, which is probably faster. The multi-directional search with goal switching combines the advantages of goal switching and bi-directional search. Altogether, the planning system is enabled to select one of the pref-erable goal configuration by itself. All concepts are experimentally validated for a set of benchmark problems consisting of an industrial robot arm with six degrees of freedom in a 3D environment.
Four different initialization methods for parallel Branch-and-bound algorithms are described and compared with reference to several criteria. A formal analysis of their idle times and efficiency follows. It indicates that the efficiency of three methods depends on the branching factor of the search tree. Furthermore, the fourth method offers the best efficiency of the overall algorithm when a centralized OPEN set is used. Experimental results by a PRAM simulation support these statements.
Due to continuously increasing demands in the area of advanced robot control, it became necessary to speed up the computation. One way to reduce the computation time is to distribute the computation onto several processing units. In this survey we present different approaches to parallel computation of robot kinematics and Jacobian. Thereby, we discuss both the forward and the reverse problem. We introduce a classification scheme and classify the references by this scheme.
We present a parallel control architecture for industrial robot cells. It is based on closed functional components arranged in a flat communication hierarchy. The components may be executed by different processing elements, and each component itself may run on multiple processing elements. The system is driven by the instructions of a central cell control component. We set up necessary requirements for industrial robot cells and possible parallelization levels. These are met by the suggested robot control architecture. As an example we present a robot work cell and a component for motion planning, which fits well in this concept.