Several approaches with interesting results have been proposed over the years for robot grasp planning. However, the industry suffers from the lack of an intuitive and reliable system able to automatically estimate grasp poses while also allowing the integration of grasp information from the accumulated knowledge of the end user. In the presented paper it is proposed a non-object-agnostic grasping pipeline motivated by picking use cases from the aerospace industry. The planning system extends the functionality of the simulated annealing optimization algorithm for allowing its application within an industrial use case. Therefore, this paper addresses the first step of the design of a reconfigurable and modular grasping pipeline. The key idea is the creation of an intuitive and functional grasping framework for being used by factory floor operators according to the task demands. This software pipeline is capable of generating grasp solutions in an offline phase, and later on, in the robot operation phase, can choose the best grasp pose by taking into consideration a set of heuristics that try to achieve a successful grasp while also requiring the least effort for the robotic arm. The results are presented in a simulated and a real factory environment, relying on a mobile platform developed for intralogistic tasks. With this architecture, new state-of-art methodologies can be integrated in the future for growing the grasping pipeline and make it more robust and applicable to a wider range of use cases.

多年来，已经提出了几种具有有趣结果的方法来进行机器人抓取计划。然而，该行业遭受缺乏能够自动估计抓握姿势同时还允许集成来自最终用户的累积知识的抓握信息的直观且可靠的系统的困扰。在本文中，提出了一种非目标不可知的抓取管道，该管道是通过从航空航天业中挑选用例来激发的。该计划系统扩展了模拟退火优化算法的功能，使其可以在工业用例中应用。因此，本文着眼于设计可重构和模块化抓取管道的第一步。关键思想是创建一个直观且功能强大的抓取框架，供工厂车间操作人员根据任务要求使用。该软件管道能够在离线阶段生成抓握解决方案，随后在机器人操作阶段，可以通过考虑一组尝试获得成功抓握同时还需要最少抓握的启发式方法来选择最佳抓握姿势机械臂的努力。结果依赖于为内部物流任务开发的移动平台，在模拟的真实工厂环境中呈现。通过这种架构，将来可以集成新的先进方法，以扩大掌握管道的速度，并使之更加健壮并适用于更广泛的用例。该软件管道能够在离线阶段生成抓握解决方案，随后在机器人操作阶段，可以通过考虑一组尝试获得成功抓握同时还需要最少抓握的启发式方法来选择最佳抓握姿势机械臂的努力。结果依赖于为内部物流任务开发的移动平台，在模拟的真实工厂环境中呈现。通过这种架构，将来可以集成新的先进方法，以扩大掌握管道的速度，并使之更加健壮并适用于更广泛的用例。该软件管道能够在离线阶段生成抓握解决方案，随后在机器人操作阶段，可以通过考虑一组尝试获得成功抓握同时还需要最少抓握的启发式方法来选择最佳抓握姿势机械臂的努力。结果依赖于为内部物流任务开发的移动平台，在模拟的真实工厂环境中呈现。有了这种架构，将来就可以集成新的最新方法论，以发展掌握管道，并使之更加健壮并适用于更广泛的用例。可以通过考虑一组试探法来选择最佳的抓握姿势，这些试探法可以成功实现抓握，同时还需要最少的机械臂费力。结果依赖于为内部物流任务开发的移动平台，在模拟的真实工厂环境中呈现。借助这种架构，将来可以集成新的最新方法论，以发展掌握管道，并使之更加健壮并适用于更广泛的用例。可以通过考虑一组试探法来选择最佳的抓握姿势，这些试探法可以成功实现抓握，同时还需要最少的机械臂费力。结果依赖于为内部物流任务开发的移动平台，在模拟的真实工厂环境中呈现。借助这种架构，将来可以集成新的最新方法论，以发展掌握管道，并使之更加健壮并适用于更广泛的用例。