Autonomous Measurement and Efficient Storage of Industrial Robot Motion Data

工业机器人运动数据的自主测量和高效存储

• 批准号: 515675259
• 负责人: Professor Dr.-Ing. Bernd Kuhlenkötter
• 金额: --
• 依托单位: Lehrstuhl für Produktionssysteme
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/515675259?language=en
• 关键词: Autonomous; Measurement; Efficient; Storage; Industrial

The range of applications for industrial robots is manifold: Industrial robots are used for workpiece handling, as measuring equipment or for manufacturing. Especially in manufacturing (e.g. welding, milling, painting), the robot's motion has a direct influence on the process result. Due to the design and control characteristics of the robot, a realized motion path generally deviates from the planned path. Therefore, a detailed knowledge of the motion deviations is relevant for the realization, improvement and testing of robot-based production processes.Modern measuring systems enable the detection of the motion paths of industrial robots, which opens up a wide range of analysis possibilities to investigate or improve the suitability for certain processes. In practice, only specific measurements are usually carried out in a limited number of cases, and the measurement data on the robot motion behavior is usually not available. Even existing data, e.g. from the data sheets of robot manufacturers, can only be used to a limited extent, since no detailed information about their measurement is known. Therefore, for each individual application, a large number of measurements has to be carried out, which is very time-consuming and cost-intensive. There are no methods to support such measurement processes with automated systems that go beyond the measurement of individual standardized tests (e.g. according to ISO 9283). There are also no general methods for path evaluation. For this purpose, an allocation method for motion deviations - based on the Dynamic Time Warping method and developed in preliminary work - is to be adopted and further developed.By means of available optical measuring systems, an automatic measurement of robot motions is basically possible. Due to the large number of possible settings and boundary conditions for the robot motion, however, the number of possible experiments is theoretically infinite. Therefore, a measurement method needs to be developed, in which the individual test runs are planned and carried out automatically. The system is to decide independently on the basis of previous experiments, in which parameter space further tests are appropriate to obtain necessary information. Since individual specific parameters for describing the robot motion behaviour can only cover a limited range of application scenarios, the robot measurement data are to be stored in a database system. Within the scope of the project, the basis for a measurement database is thus to be created in which the recorded robot motion data are stored. The information contained in the database provides a comprehensive description of the robot motion behaviour. With a detailed database, which can always be extended by new measurement data, a wide range of application scenarios will be supported, in which time-consuming and cost-intensive individual measurements can be avoided.

工业机器人的应用范围是多方面的：工业机器人用于工件搬运、测量设备或制造。特别是在制造领域（如焊接、铣削、喷漆），机器人的运动对工艺结果有直接影响。由于机器人的设计和控制特性，实际的运动路径通常会偏离规划的路径。因此，详细了解运动偏差对于基于机器人的生产过程的实现、改进和测试至关重要。现代测量系统可以检测工业机器人的运动路径，从而为研究或改进某些过程的适用性提供了广泛的分析可能性。在实践中，通常只在有限的情况下进行特定的测量，并且通常无法获得机器人运动行为的测量数据。即使是现有的数据，例如根据机器人制造商的数据表，只能在有限的范围内使用，因为不知道有关其测量的详细信息。因此，对于每个单独的应用，都必须进行大量的测量，这是非常耗时且成本高昂的。除了单个标准化测试的测量（例如根据 ISO 9283）之外，没有任何方法可以使用自动化系统来支持此类测量过程。路径评估也没有通用的方法。为此，将采用并进一步开发一种基于动态时间规整方法并在前期工作中开发的运动偏差分配方法。通过可用的光学测量系统，机器人运动的自动测量基本上是可能的。然而，由于机器人运动有大量可能的设置和边界条件，理论上可能的实验数量是无限的。因此，需要开发一种测量方法，其中可以自动规划和执行各个测试运行。该系统应在先前实验的基础上独立决定，在哪些参数空间中进行适当的进一步测试以获得必要的信息。由于描述机器人运动行为的各个具体参数只能覆盖有限范围的应用场景，因此机器人测量数据需要存储在数据库系统中。在该项目范围内，将创建测量数据库的基础，其中存储记录的机器人运动数据。数据库中包含的信息提供了机器人运动行为的全面描述。详细的数据库可以随时通过新的测量数据进行扩展，从而支持广泛的应用场景，从而避免耗时且成本高昂的单独测量。