用机器人自动规划维修任务与路径，提升工厂自动化效率。
Efficient task and path planning for maintenance automation using a robot system
- 融合CAD离线数据与RGBD视觉在线数据，降低环境不确定性影响。
- 基于符号化描述和采样方法计算拆卸空间，支持复杂任务规划。
- 自适应调整探索步长，显著缩短路径规划时间，适合工业应用。
智能自动化解决方案是未来工厂的关键突破点。利用自主机器人系统实现维修任务的自动化是一项极具前景但充满挑战的任务。为此，机器人系统需能自主规划各类操作任务及其对应路径。核心要求包括算法计算复杂度低，且能应对环境不确定性。本文提出一种适用于维修自动化的解决方案：通过概率滤波器将来自CAD的离线数据与来自RGBD视觉系统的在线数据相结合，以补偿离线数据的不确定性。任务规划采用基于新型采样方法的符号化描述，用于计算拆卸空间；路径规划则采用当前最先进的全局算法，并引入自适应探索步长调整机制，以减少规划时间。各项方法均经过实验验证与讨论。
原文摘要 · Abstract (English)
The research and development of intelligent automation solutions is a ground-breaking point for the factory of the future. A promising and challenging mission is the use of autonomous robot systems to automate tasks in the field of maintenance. For this purpose, the robot system must be able to plan autonomously the different manipulation tasks and the corresponding paths. Basic requirements are the development of algorithms with a low computational complexity and the possibility to deal with environmental uncertainties. In this work, an approach is presented, which is especially suited to solve the problem of maintenance automation. For this purpose, offline data from CAD is combined with online data from an RGBD vision system via a probabilistic filter, to compensate uncertainties from offline data. For planning the different tasks, a method is explained, which use a symbolic description, founded on a novel sampling-based method to compute the disassembly space. For path planning we use global state-of-the art algorithms with a method that allows the adaption of the exploration stepsize in order to reduce the planning time. Every method is experimentally validated and discussed.
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