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MICHAL PECHOUCEK y , ALES RIHA y *, JIRI VOKRINEK y , VLADIMIR MARIK y and VOJTECH PRAZMA z This paper presents work carried out within the`ExPlanTech' project (IST-1999-20171) funded in part by the European Commission's Information Technologies Programme. The mission of the ExPlanTech technology transfer project is to introduce, customize and exploit the multi-agent production planning technology (ProPlanT multi-agent system research prototype) in two speci®c industrial enterprises. An agent-driven service negotiations and decision process, based on usagecentred knowledge about task requirements, substitutes the traditional production planning activity. We introduce a methodology for integration of the projectdriven production planning based on agent-based engineering within the existing enterprise resource planning system. This novel production planning technology will facilitate optimization of resource utilization and supplier chain while meeting the customer demands. This paper describes a FIPA-compliant implementation of the ExPlanTech technology at the LIAZ Pattern Shop manufacturing company. We describe the structure of the agent community, types of agents, implementation of the planning strategy and its incorporation within the real production environment.

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We consider a system consisting of multiple mobile robots in which the user can at any time issue relocation tasks ordering one of the robots to move from its current location to a given destination location. In this paper, we deal with the problem of finding a trajectory for each such relocation task that avoids collisions with other robots. The chosen robot plans its trajectory so as to avoid collision with other robots executing tasks that were issued earlier. We prove that if the destination of each task is an endpoint in a so-called well-formed infrastructure, then this mechanism is guaranteed to always succeed and provide a trajectory for the robot that reaches the destination without any collisions. The time-complexity of the approach is only quadratic in the number of robots. We demonstrate the applicability of the presented method on several real-world maps and compare its performance against a popular reactive approach that attempts to solve the collisions locally. Besides being dead-lock free, the presented approach generates trajectories that reach the goal significantly faster (up to 48% improvement) than the trajectories resulting from local collision avoidance.

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