Early detection of armed threats is crucial in reducing accidents and deaths resulting from armed conflicts and terrorist attacks. The most significant application of weapon detection systems would be found in public areas such as airports, stadiums, central squares, and on the battlefield in urban or rural conditions. Modern surveillance and control systems of closed-circuit television cameras apply deep learning and machine learning algorithms for weapons detection on the base of cloud architecture. However, cloud computing is inefficient for network bandwidth, data privacy and slow decision-making. To address these issues, edge computing can be applied, using Raspberry Pi as an edge device with the EfficientDet model for developing the weapons detection system. The image processing results are transmitted as a text report to the cloud platform for further analysis by the operator. Soldiers can equip themselves with the suggested edge node and headphones for armed threat notifications, plugged into augmented reality glasses for visual data output. As a result, the application of edge computing makes it possible to ensure data safety, increase the network bandwidth and provide the device operation without the internet. Thus, an independent weapon detection system was developed that identifies weapons in 1.48 seconds without the Internet.

Maintaining a specific geometric formation during the movement is crucial for multiagent systems of mobile robots in various applications. Proper coordination can lead to reduced system costs, increased reliability and efficiency, and system adaptability and flexibility. This research proposes a novel movement coordination method for self-governing multiagent systems of intelligent mobile robots. The proposed method uses a leader-follower technique with a virtual leader to maintain a specific geometric structure. Additionally, the epsilon greedy algorithm is utilized to avoid loops. To reduce power consumption, it is proposed to turn on only a few robots' lidars at a time. They could drive all the robots in the group, allowing them to reach the goal without colliding with obstacles. Experiments on a complex map with nine robots were conducted to test the method's effectiveness. The success rate of the swarm reaching the target position and the number of steps needed were evaluated. Testing varied angular velocities of 1 to 20 degrees and linear velocities of 0.1 to 5.5 m/s. Results show the method effectively guides the robots without collisions. This method enables a group of self-governing multiagent systems of intelligent mobile robots to maintain a desired formation while avoiding obstacles and reducing power consumption. The results of the experimental study demonstrate the method's potential to be implemented in real-world missions and traffic management systems to increase efficiency and reduce costs. The proposed method can be utilized in military missions and traffic management systems, where maintaining a specific geometric formation is crucial. The method's ability to avoid obstacles and reduce power consumption can also lead to reduced costs and increased efficiency.