Cooperative Observation of Targets moving over a Planar Graph with Prediction of Positions
This work addresses a cooperative observation problem for UAV teams, but it appears incremental as it builds on prior hill climbing methods with minor adaptations.
The paper tackles the problem of maximizing the total number of observed targets moving on a planar graph using aerial UAVs with limited vision, by leveraging short-term position predictions. It introduces a modified hill climbing algorithm that outperforms previous versions in this setting.
Consider a team with two types of agents: targets and observers. Observers are aerial UAVs that observe targets moving on land with their movements restricted to the paths that form a planar graph on the surface. Observers have limited range of vision and targets do not avoid observers. The objective is to maximize the integral of the number of targets observed in the observation interval. Taking advantage of the fact that the future positions of targets in the short term are predictable, we show in this article a modified hill climbing algorithm that surpasses its previous versions in this new setting of the CTO problem.