Abstract
We propose the Data-collecting Robot Problem, where robots collect data as they visit nodes in a graph, and algorithms to solve it. There are two variations of the problem: the delayed-reward problem, in which robots must travel back to the base station to deliver the data collected and to receive rewards; and the immediate-reward problem, in which the reward is immediately given to the robots as they visit each node. The delayed-reward problem is discussed in one of the authors’ work. This paper focuses on the immediate-reward problem. The solution structure has a clustering step and a tour-building step. We propose Progressive Gain-aware Clustering that finds good quality solutions with efficient time complexity. Among the six proposed tour-building heuristics, Greedy Insertion and Total-Loss algorithms perform best when data rewards are different.
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Notes
- 1.
The reward function in our problem does not satisfy the Diminishing Marginal Gain property, so the performance guarantee of SGA doesn’t hold.
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Acknowledgement
We thank Mahmuda Rahman and Jeff Hudack for reviewing drafts of this paper and providing valuable suggestions.
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Xing, Z., Oh, J.C. (2016). Heuristics on the Data-Collecting Robot Problem with Immediate Rewards. In: Baldoni, M., Chopra, A., Son, T., Hirayama, K., Torroni, P. (eds) PRIMA 2016: Principles and Practice of Multi-Agent Systems. PRIMA 2016. Lecture Notes in Computer Science(), vol 9862. Springer, Cham. https://doi.org/10.1007/978-3-319-44832-9_8
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