SAGL: A New Heuristic for Multi-Robot Routing with Complex Tasks
Hong Xu, T. K. Satish Kumar, Dylan Johnke, Nora Ayanian, and Sven Koenig.
SAGL: a new heuristic for multi-robot routing with complex tasks.
In Proceedings of the 28th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 530–535. 2016.
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In this paper, we study the Complex Routing Problem (CRP), where several homogeneous robots need to visit given task locations to accomplish complex tasks in a cooperative setting. Each task location hosts a task. The complexity level of a task is defined as the number of robots that need to be simultaneously present at its location to accomplish it. The robots need to be routed so that all tasks get accomplished with minimal makespan. We present a new centralized algorithm, called SAGL, for solving the CRP heuristically. SAGL is inspired by the application of linear programming duality to the Steiner Forest Problem. It makes less restrictive assumptions than the state-of-the-art distributed Approach with Reaction Functions and scales better in both the complexity levels of tasks and the number of complex tasks (whose complexity levels are greater than one), although it results in somewhat larger makespans.