9 October 1998 Resource-based action planning for multiagent systems
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Proceedings Volume 3523, Sensor Fusion and Decentralized Control in Robotic Systems; (1998) https://doi.org/10.1117/12.326998
Event: Photonics East (ISAM, VVDC, IEMB), 1998, Boston, MA, United States
Abstract
With the use of multi-robot-systems new chances and perspectives are revealed for industrial, space- and underwater-applications. At the IRF, a versatile multi- robot-control, which fully exploits the inherent flexibility of a multi-robot-system, has been developed. In order to guarantee optimized system-throughput and increased autonomy, the system builds on a new resource-based action planning approach to coordinate the different robot manipulators and other automation components in the workcell. An important issue of the realized action planning component to be applicable to real world problems is that it is realized as an integral part of the hierarchical multi- robot control structure IRCS (Intelligent Robot Control System). The IRCS is the multi-robot control that was chosen by the German Space Agency (DLR) for major space automation projects. In this structure the resource-based action planning component is tightly integrated with components for coordinated task execution and collision avoidance to guarantee save operation of all agents in the multi-robot system. As the action planning component `understands' task descriptions on a high level of abstraction it is also the perfect counterpart for a Projective Virtual Reality (VR) system. The paper will describe the mechanism of resource based action planning, the practical experiences gained from the implementation for the IRCS as well as its services to support VR-based man machine interfaces.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Eckhard Freund, Katharina Hoffmann, Juergen Rossmann, "Resource-based action planning for multiagent systems", Proc. SPIE 3523, Sensor Fusion and Decentralized Control in Robotic Systems, (9 October 1998); doi: 10.1117/12.326998; https://doi.org/10.1117/12.326998
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