UAV routing with genetic algorithm based matheuristic for border security missions

Abstract

In recent years, Unmanned Aerial Vehicles (UAVs) are a good alternative for the problem of ensuring the security of the borders of the countries. UAVs are preferred because of their speed, ease of use, being able to observe many points at the same time, and being more cost-effective in total compared to other security tools. This study is dealt with the problem of the use of UAVs for the security of the Turkey-Syria borderline which becomes sensitive in recent years and the problem is modeled as a UAV routing problem. To solve the problem, a Genetic Algorithm Based Matheuristic (GABM) approach has been developed and 12 scenarios have been created covering the departure bases, daily patrol numbers, and ranges of UAVs. GABM finds the minimum number of UAVs to use in scenarios with the help of a GA run first and tries to find the optimal routes for these UAVs. If GABM can find an optimal route for the determined UAV number, it decreases the UAV number and tries to solve the problem again. GABM proposes a hybrid approach in which a metaheuristic with a mathematical model works together and the metaheuristic sets an upper limit for the number of UAVs in the model. In computational studies, when compared GA with GABM it is seen that GABM has obtained good results and decreased the utilized number of UAVs (up to 400%) and their flight distances (up to 85.99%) for the problem in very short CPU times (max. 122.17 s. for GA and max. 46.39 s. for GABM in addition to GA).

Single-drone energy efficient coverage path planning with multiple charging stations for surveillance

“…Drone technologies were studied for different application areas such as logistics [8][9], manufacturing [10], surveillance [11], intralogistics [12][13], disaster management [14], inventory management [15][16], and agriculture [17][18]. Ozkan and Kaya [11] studied UAV path planning for border security and patrolling missions and solved the problem using a Genetic Algorithm-based Matheuristic for different scenarios based on departure basis, daily patrol numbers, and ranges of UAVs. Besides, Otto et al [6] performed a comprehensive review study of the optimization approaches for civil applications of drones/UAVs.…”

Deep Reinforcement Learning for Green UAV-Assisted Data Collection

“…Unmanned aerial vehicles (UAVs) have rapidly evolved as an emerging technology, finding profound applications in both military and civilian sectors [1]- [4]. They are critical for real-time sensing in scenarios like traffic monitoring [5], border security [6], disaster management [7] and forest fire surveillance [8], all of which demand continuous data transmission. However, a major limitation of UAVs in such persistent applications is their restricted operational time due to limited battery capacity.…”

Optimizing Task Waiting Times in Dynamic Vehicle Routing

“…T HE Dynamic Vehicle Routing Problem is an important and long-studied challenge in assigning autonomous vehicles (or robots) to tasks as they appear in the environment. Applications include pickup-and-delivery [1], [2], mobility-on-demand transportation systems [3], [4], [5], sensor networks [6], [7], surveillance [8], [9], personal care [10], [11], and environmental monitoring [12], [13], [14].…”

“…For instance, in autonomous mobility on demand [3], [4], [5] it is desired to minimize the average response time to the incoming requests, while also limiting maximum waiting times. Other applications include the deployment of autonomous drones as mobile sensor networks [6], [7], for surveillance [8], [9], search and rescue [35], and in environmental monitoring [12], [13], [14].…”