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A new, uncertainty-aware cost-model for cost-benefit assessment of robot systems

Thomas Dietz, Andreas Pott, Martin Hägele, Alexander Verl

发表年份
2013
引用次数
7

摘要

This paper introduces a new cost-model for robot systems with cognitive features for the use in small and medium sized enterprises (SME). The model combines approaches from activity-based costing and life-cycle costing to reflect machine and production-centric aspects of the use of robot systems. Key contribution of this paper is the handling of uncertainty in the input parameters of the cost-model by using methods from interval analysis. This approach allows to compute explicitly best and worst case scenarios without manual variation of parameters. The cost-model is tested for an application scenario for robotic de-burring and shows the economic performance of the robot system in best and worst case in an intuitive way.

关键词

Activity-based costingRobotComputer scienceKey (lock)Life cycle costingInterval (graph theory)Variation (astronomy)Cost driverIndustrial engineeringArtificial intelligence

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