{"content":[{"text":"This paper has been accepted by the Journal of Intelligent Manufacturing. This is a preprint version, not the \n published version. Personal use of this material is permitted. The preprint sharing is subject to the Springer policy.","bbox":{"left":0.064167,"top":0.021168,"width":0.871468,"height":0.034901}},{"text":"Task Allocation and Coordinated Motion Planning for Autonomous \n Multi-Robot Optical Inspection Systems","bbox":{"left":0.098125,"top":0.080789,"width":0.8028489999999999,"height":0.063506}},{"text":"Yinhua Liu1, Wenzheng Zhao1, Tim Lutz2, Xiaowei Yue2,*","bbox":{"left":0.23441,"top":0.159148,"width":0.531076,"height":0.02207999999999999}},{"text":"Abstract: Autonomous multi-robot optical inspection \n systems are increasingly applied for obtaining inline \n measurements in process monitoring and quality control. \n Numerous methods for path planning and robotic \n coordination have been developed for static and dynamic \n environments and applied to different fields. However, \n these approaches may not work for the autonomous multi- \n robot optical inspection system due to fast computation \n requirements of inline optimization, unique \n characteristics on robotic end-effector orientations, and \n complex large-scale free-form product surfaces. This \n paper proposes a novel task allocation methodology for \n coordinated motion planning of multi-robot inspection. \n Specifically, (1) a local robust inspection task allocation \n is proposed to achieve efficient and well-balanced \n measurement assignment among robots; (2) collision-free \n path planning and coordinated motion planning are \n developed via dynamic searching in robotic coordinate \n space and perturbation of probe poses or local paths in the \n conflicting robots. A case study shows that the proposed \n approach can mitigate the risk of collisions between \n robots and environments, resolve conflicts among robots, \n and reduce the inspection cycle time significantly and \n consistently.","bbox":{"left":0.064167,"top":0.224138,"width":0.418648,"height":0.37269099999999994}},{"text":"station. This poses a multi-robot coordination problem. \n To increase the efficiency of quality inspection and \n mitigate conflict risks among multiple robots, we develop \n a rigorous task allocation and multi-robot motion \n planning methodology. The inline optical inspection \n system can accurately measure raw structures, sub- \n assembly parts, and final products in the flexible assembly \n line. Our methodology has the potential to advance the \n autonomy, safety, and efficiency of inline multi-robot \n coordination and real-time quality inspection. \n We start the literature review with collision-free path \n planning for robots. Traditional single-robot collision- \n free motion planning methods include randomized \n kinodynamic planning (Kazemi et al., 2013), \n reinforcement learning (Hua et al., 2021), Neural Network \n (Pashkevich & Kazheunikau, 2005), and sampling-based \n planning method (Jaillet et al., 2010), etc. For a detailed \n review of path planning and optimization of mobile robots, \n we refer the reader to (Zafar & Mohanta, 2018). The \n aforementioned methods address path planning and \n optimization problems in static and dynamic \n environments and have been applied to different fields. \n However, these methods usually consider gently curved \n response surfaces. \n With the advancement of product design and \n manufacturing technologies, large-scale and free-form \n surfaces have become more common, which raises new \n challenges for robotic path planning. For example, the \n latest auto body design may include various features such \n as holes, slots, surfaces with large curvature, edges, and \n threads. The existing methods mainly model the robot and \n its working environment, and then use heuristic \n algorithms such as the Artificial Potential Field method to \n search for a collision-free path of the robot. However, \n given the diverse features in large-scale free-form \n surfaces and a large number of joints in a typical industrial \n robot, it is very challenging for heuristic algorithms to \n find a feasible path without collision in real time. One \n common way to avoid a collision is to define intermediate \n transition points when trying to generate a collision-free \n path. Yet, these transition points are primarily derived \n from engineering experience, which is not captured by","bbox":{"left":0.517413,"top":0.224138,"width":0.42300399999999994,"height":0.650277}},{"text":"Keywords: optical inspection, task allocation, multi- \n robot, coordinated motion planning, quality control","bbox":{"left":0.064167,"top":0.613934,"width":0.38285800000000003,"height":0.03209099999999998}},{"text":"1.","bbox":{"left":0.064167,"top":0.658993,"width":0.013073000000000001,"height":0.017618000000000023}},{"text":"Introduction","bbox":{"left":0.098958,"top":0.658993,"width":0.09435999999999999,"height":0.017618000000000023}},{"text":"In recent years, autonomous robotics-guided optical \n measurement probes have been introduced to improve \n production inspection and inline quality control. Unlike \n traditional offline gauges such as coordinate measuring \n machines, non-contact robotics-guided optical probes \n have enabled inline measurement of highly complex \n surfaces in production. The obtained data can be used for \n real-time process monitoring, fault diagnosis, and in- \n process quality improvement. Due to the complexity of \n large-scale free-form surfaces in some products (e.g., auto \n bodies, aircraft), multiple industrial robots equipped with \n optical probes are usually distributed in the inspection","bbox":{"left":0.064167,"top":0.68857,"width":0.41882600000000003,"height":0.18767899999999993}},{"text":"* Xiaowei Yue \n E-mail: xwy@vt.edu","bbox":{"left":0.064167,"top":0.891774,"width":0.13051,"height":0.02851300000000012}},{"text":"1 School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. \n 2 Virginia Polytechnic Institute and State University, BlacksburgVA 24060, USA.","bbox":{"left":0.064167,"top":0.931467,"width":0.726703,"height":0.02851400000000004}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}