A scheduling method for multi-robot assembly of aircraft structures with soft task precedence constraints

The use of multiple cooperating robotic manipulators to assemble large aircraft structures entails the scheduling of many discrete tasks such as drilling holes and installing fasteners. Since the tasks have different tool requirements, it is desirable to minimize tool changes that incur significant...

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Bibliographic Details
Published in:Robotics and computer-integrated manufacturing 2021-10, Vol.71, p.102154, Article 102154
Main Authors: Tereshchuk, Veniamin, Bykov, Nikolay, Pedigo, Samuel, Devasia, Santosh, Banerjee, Ashis G.
Format: Article
Language:English
Subjects:
Aircraft
Aircraft structures
Computing costs
Fasteners
Flexible manufacturing
Heuristic
Iterative methods
Machine learning
Multi-robot systems
Multiple robots
Policies
Precedence constraints
Robot arms
Robots
Schedules
Scheduling
Task scheduling
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Summary:The use of multiple cooperating robotic manipulators to assemble large aircraft structures entails the scheduling of many discrete tasks such as drilling holes and installing fasteners. Since the tasks have different tool requirements, it is desirable to minimize tool changes that incur significant time costs. We approach this problem as a multi-robot task allocation problem with precedence constraints, where the constraints are loosely enforced in terms of prioritizing the tasks to avoid unnecessary tool changes. To avoid the computational burden of searching over all possible task prioritization options, our main contribution is to develop a two-step, data-driven approach to automatically select suitable precedence relations. The first step is to adapt an iterative auction-based method to encode the precedence relations using scheduling heuristics. The second step is to develop a robust machine learning method to generate policies for automatically selecting efficient scheduling heuristics based on the problem characteristics. Experimental results show that the top performing heuristics yield schedules that are more efficient than those of a baseline partition-based scheduler by almost 17%–19%, depending on the robot failure profiles. The learned policies are also able to select heuristics that perform better than greedy selection without incurring additional computational costs. •Multi-robot assembly requires scheduling a large number of tasks with tool changes.•We model the tool change requirements as soft task precedence constraints.•We propose a two-step approach for robust task scheduling.•Our approach consists of prioritized iterative auction followed by machine learning.•Results show improvements over a scheduler that does not model the tool changes.
ISSN:0736-5845
1879-2537
DOI:10.1016/j.rcim.2021.102154