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Abstract Line coverage is the task of servicing a given set of one‐dimensional features in an environment. It is important for the inspection of linear infrastructure such as road networks, power lines, and oil and gas pipelines. This paper addresses the single robot line coverage problem for aerial and ground robots by modeling it as an optimization problem on a graph. The problem belongs to the broad class of arc routing problems and is closely related to the rural postman problem (RPP) on asymmetric graphs. The paper presents an integer linear programming formulation with proofs of correctness. Using the minimum cost flow problem, we develop approximation algorithms with guarantees on the solution quality. These guarantees also improve the existing results for the asymmetric RPP. The main algorithm partitions the problem into three cases based on the structure of therequired graph, that is, the graph induced by the features that require servicing. We evaluate our algorithms on road networks from the 50 most populous cities in the world, consisting of up to 730 road segments. The algorithms, augmented with improvement heuristics, run within 3 s and generate solutions that are within 10% of the optimum. We experimentally demonstrate our algorithms with commercial UAVs on the UNC Charlotte campus road network.
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Cascades and Overexposure in Social Networks: The Budgeted Case
Influence maximization (IM) has now been a widely studied topic, but only in recent years have studies considered overexposure. Overexposure is usually measured as the negative cost associated with reaching unintended recipients during an information cascade. A polynomial-time algorithm is known for cascades with overexposure when we can seed as many nodes as we want. This paper focuses on overexposure for the budgeted case of seeding, which has received little to no attention. We show that the problem is NP-hard even for restricted cases. For various special cases, we devise provable approximation algorithms, dynamic programming solutions, linear programming solutions, and heuristics. For the general case, we provide a linear programming solution and several fast and effective heuristics, mostly of the greedy flavor. We perform an extensive experimental study using synthetic and real-world networks. We investigate how network properties and model parameters impact our algorithms. It brings out interesting findings like why a low-quality product needs a smarter algorithm, and why certain algorithms do well on some networks but not others.
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- Award ID(s):
- 1910203
- PAR ID:
- 10358679
- Date Published:
- Journal Name:
- Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems
- Page Range / eLocation ID:
- 642–650
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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