War game simulations are decision-making tools that may provide quantitative data about the scenario analyzed by stakeholders. They are widely used to develop tactics and doctrines in the military context. Recently, unmanned air vehicles (UAVs) have become a relevant element in these simulations because of their prominent role in contemporary conflicts, surveillance missions, and search and rescue missions. For instance, it is possible to admit aircraft losses from a tactical formation in favor of the victory of a squadron in a given combat scenario. The optimization of the position of UAVs in beyond visual range (BVR) combat has attracted attention in the literature, considering that the distribution of UAVs can be a determining factor in this scenario. This work aims to optimize UAV tactical formations considering enemy uncertainties such as firing distance and position using six metaheuristics and a high-fidelity simulator. A tactical formation often employed by air forces called line abreast was chosen for the RED swarm for a case study. The objective of the optimization is to obtain a tactical formation of the BLUE swarm that wins the BVR combat against the RED swarm. A procedure to confirm the robustness of the optimization is employed, varying the position of each UAV of the RED swarm up to 8 km from its initial configuration and using the war game approach. A tactical analysis is performed to confirm whether the formations found in the optimization are applicable.

As Unmanned Aerial Vehicles (UAVs) are becoming crucial in modern warfare, research on autonomous path planning is becoming increasingly important. The conflicting nature of the optimization objectives characterizes path planning as a multi-objective optimization problem. Current research has predominantly focused on developing new optimization algorithms. Although being able to find the mathematical optimum is important, one also needs to ensure this optimum aligns with the decision-maker’s (DM’s) most preferred solution (MPS). In particular, to align these, one needs to handle the DM’s preferences on the relative importance of each optimization objective. This paper provides a comprehensive overview of all preference handling techniques employed in the military UAV path planning literature over the last two decades. It shows that most of the literature handles preferences by the overly simplistic method of scalarization via weighted sum. Additionally, the current literature neglects to evaluate the performance (e.g., cognitive validity and modeling accuracy) of the chosen preference handling technique. To aid future researchers handle preferences, we discuss each employed preference handling technique, their implications, advantages, and disadvantages in detail. Finally, we identify several directions for future research, mainly related to aligning the mathematical optimum to the MPS.

An operational planning procedure for a time-critical maritime unmanned aerial vehicle (UAV) search mission is introduced and evaluated. The mission is the fast identification of a target vessel. The triggering report only contains information regarding the category and displacement of a vessel carrying out a prohibited activity, resembling operational situations. A neural network trained to classify vessels is combined with vessel clustering to reduce waypoints in the flight plan. The UAV’s onboard sensors provide input for the neural network regarding each vessel in the search area, resulting in a prioritization of vessels to be visited. As the accuracy of the classification and the possibilities for clustering depend on several operational factors as well as on the UAV’s sensor degradation, we investigate three methodologies to identify which planning procedure to use in various operational situations. The results show that our robust and agile approach can help a UAV find the unknown target vessel as soon as possible.

As Unmanned Aerial Vehicles (UAVs) are becoming crucial in modern warfare, research on autonomous path planning is becoming increasingly important. The conflicting nature of the optimization objectives characterizes path planning as a multi-objective optimization problem. Current research has predominantly focused on developing new optimization algorithms. Although being able to find the mathematical optimum is important, one also needs to ensure this optimum aligns with the decision-maker's (DM's) most preferred solution (MPS). In particular, to align these, one needs to handle the DM's preferences on the relative importance of each optimization objective. This paper provides a comprehensive overview of all preference handling techniques employed in the military UAV path planning literature over the last two decades. It shows that most of the literature handles preferences by the overly simplistic method of scalarization via weighted sum. Additionally, the current literature neglects to evaluate the performance (e.g. cognitive validity and modeling accuracy) of the chosen preference handling technique. To aid future researchers handle preferences, we discuss each employed preference handling technique, their implications, advantages, and disadvantages in detail. Finally, we identify several directions for future research, mainly related to aligning the mathematical optimum to the MPS.

Wireless sensor network (WSN) is a network of low-size and low-complexity devices that sense the environment and communicate the gathered data through wireless channels. The sensors sense the environment, and send data to control unit for processing and decisions. The data is forwarded via multiple hops or is connected to other networks through a gateway. WSNs have a wide range of applications from monitoring environment and surveillance, to precision agriculture, and from biomedical to structural and infrastructure health monitoring. Technological advances in the past decades have resulted in small, inexpensive and powerful sensors with embedded processing and radio networking capability. Distributed cooperative smart sensor devices networked through the radio link and deployed in large numbers provide enormous opportunities. Cooperation between nodes will increase the performance of wireless sensor network in fulfilling its task. Game theory is used to describe how and why coalitions of sensor nodes form, using the trade-off between the advantage of cooperation (in terms of better performance) and the costs of cooperation (in terms of bandwidth, transmitting information), using the topology of the network. A general game-theoretic framework for WSN is presented, and illustrated by means of an example.