swarm UA V live-fly competition, designedto inspire new concepts of operations and illuminate new tactics in unmannedsystems employment, specifically in the swarm and counter-swarm roboticsarenas. The competition scenario involves a tournament of "battles"where in each such battle two teams comprising...
This SLR seeks to identify the state-of-the-art object-goal navigation methods using UAVs for an emergency response scenario (Agrawal and Cleland-Huang2021). Two main UAV types were found. The first is small and lightweight, able to fly faster with less noise. The other is large and capab...
Results of path planning: (a) task scenario 1; (b) task scenario 2. Full size image The path planning procedure in task scenario 1 was successfully executed in a rapid 5.875 s, yielding a highly favorable outcome. As clearly depicted in Fig. 2a, this plan adeptly circumvents the threat ...
The grey wolf optimizer is a renowned algorithm within the realm of swarm intelligence. However, it is hindered by a few drawbacks such as slow convergence rate, limited population diversity, and a propensity to fall into local optima in certain scenarios. In this study, we introduce a groundbr...
We study the performance of team theoretic approach for task allocation on a battle field scenario. The performance obtained through team theory is compared with two other methods, namely, limited sensor range but with communication among all the UAVs, and greedy strategy with limited sensor range ...
The combat scenario is set as a two-dimensional area. At the beginning of the battle, there are a set of 𝑁N Vehicles 𝑉={𝑉1,…,𝑉𝑁}V={V1,…,VN} and a set of 𝑀M Targets 𝑇={𝑇1,…,𝑇𝑀}T={T1,…,TM}. Each target 𝑇𝑗Tj contains three subtasks, i...
Swarm agents with limited fuel capacity have additional problems. Attempts at reconnection and prolonged communication at low signal strength might deplete power reserves more quickly, reducing flight range on the mission. Adaptable connectivity protocols are needed in this scenario [41]. Here, a ...
In this paper, we focus on the combat scenario of a UAV swarm saturation attack against hostile surface ships and aim to research the issue of a multi-UAV task assignment. In order to obtain the optimal combat effectiveness, we allocate tasks to heterogeneous UAVs according to the battlefield...
3.1. Modeling Scenario Characterization The SSCM-RT model divides long-distance propagation into two parts, using the ray tracing model for deterministic modeling in the portion of the propagation distance close to the receiver and the spatial statistical channel model in the portion of the propagatio...
Throughout the flight, the UAV swarm actively avoids internal collisions to ensure safety. Figure 3. Formation path planning scenario. The problem is considered as two-dimensional, assuming there are no obstacles in the air. Discussion of scenarios with static obstacles will follow in the section...