UAVs Path Planning under a Bi-Objective Optimization Framework for Smart Cities

Saha, Subrata; Vasegaard, Alex Elkjær; Nielsen, Izabela; Hapka, Aneta; Budzisz, Henryk

doi:10.3390/electronics10101193

Cited by 19 publications

(12 citation statements)

References 68 publications

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“…Journal of Function Spaces UAV, the target can be photographed or tracked in the surveillance dead corner that cannot be observed by people [18]. The picture information stored inside is automatically sent to the computer terminal through the network to achieve the work purpose and complete the layout task.…”

Section: Development Status Of Reinforcement Learning Uav In Various ...mentioning

confidence: 99%

UAV Mission Path Planning Based on Reinforcement Learning in Dynamic Environment

Gao

Liu

et al. 2023

Journal of Function Spaces

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With the rapid development of information technology, various products used in information technology are also constantly optimized. Among them, the task and path planning of UAV in the high-end robot industry has always been the focus of relevant researchers. In the high-end robot industry, in addition to the research and development of UAVs, they also continue to learn and strengthen the task and path planning of UAVs. Nowadays, using unmanned aerial vehicles for real-time shooting has become the trend of this era. Drones have brought great convenience to people’s lives, and more and more people are willing to use drones. Based on the above situation, this paper studies the task and path planning of UAV based on reinforcement learning in dynamic environment. In the case of unpredictable scene parameters, reinforcement learning method can be established by value function. Thus, a more reasonable path can be given to realize the reconnaissance and detection of points of interest. MATLAB simulation experiments show that the algorithm can effectively detect targets in complex terrain composed of terrain restricted areas, and return to the designated end point to complete communication. Firstly, the development of unmanned aerial vehicles in various countries and the social status of unmanned aerial vehicles are discussed. By making UAV build threat model and task allocation in dynamic environment. The path planning and optimization of UAV in dynamic environment is studied, and the path planning algorithm and Hungarian algorithm are added. The optimized UAV has the fastest data transmission and calculation speed, while the other two types of UAVs have slower data transmission and calculation speed. In particular, ordinary UAVs also have data transmission failures, resulting in incomplete experimental results. The results show that the optimized UAV system is better in data calculation and transmission, which also shows that the UAV can quickly plan and process flight paths, which is suitable for practical applications.

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Section: Development Status Of Reinforcement Learning Uav In Various ...mentioning

confidence: 99%

UAV Mission Path Planning Based on Reinforcement Learning in Dynamic Environment

Gao

Liu

et al. 2023

Journal of Function Spaces

View full text Add to dashboard Cite

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“…As a continuation of this, how to integrate the different preferences is considered greatly in multi-objective (MO) formulations of the UAV problem [ 13 ]. Whether preferences are introduce a priori or a posteriori through scoring or by estimating a Pareto front, respectively [ 14 ]. Estimating Pareto fronts is very time consuming, so to accommodate this, Atencia et al [ 15 ] developed a MO evolutionary algorithm integrating a weight generator to narrow the solution space.…”

Section: Literature Reviewmentioning

confidence: 99%

A GRASP-Based Approach for Planning UAV-Assisted Search and Rescue Missions

Pedersen

Nielsen

Rosenkrands

et al. 2021

Sensors

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Search and Rescue (SAR) missions aim to search and provide first aid to persons in distress or danger. Due to the urgency of these situations, it is important to possess a system able to take fast action and effectively and efficiently utilise the available resources to conduct the mission. In addition, the potential complexity of the search such as the ruggedness of terrain or large size of the search region should be considered. Such issues can be tackled by using Unmanned Aerial Vehicles (UAVs) equipped with optical sensors. This can ensure the efficiency in terms of speed, coverage and flexibility required to conduct this type of time-sensitive missions. This paper centres on designing a fast solution approach for planning UAV-assisted SAR missions. The challenge is to cover an area where targets (people in distress after a hurricane or earthquake, lost vessels in sea, missing persons in mountainous area, etc.) can be potentially found with a variable likelihood. The search area is modelled using a scoring map to support the choice of the search sub-areas, where the scores represent the likelihood of finding a target. The goal of this paper is to propose a heuristic approach to automate the search process using scarce heterogeneous resources in the most efficient manner.

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“…The research community is now turning its attention to different areas such as optimization and prediction [1][2][3][4][5]. As evidenced in references [2,3,5], which have analyzed travel time data to evaluate the performance of a public transport system.…”

Section: The Present Issuementioning

confidence: 99%

“…Others have focused on the demand for different modes of transportation and interaction among them, including a proposal for minimizing the passengers' waiting times and maximizing the vehicles' occupancy ratios. The use of unmanned aerial vehicles for emergency situations is extensively described in [1] for search and rescue operations, surveillance, disaster monitoring, response to terrorist attacks. Finally, ref.…”

Section: The Present Issuementioning

confidence: 99%