Ambient air quality is a complex issue that depends on multiple interacting factors related to emissions coming from energy production and use, transportation, industrial processes, agriculture, and waste and wastewater treatment sectors. It is also impacted by adverse meteorological conditions, pollutants concentrations, their transport and dispersion in the atmosphere, and topographic constraints. Therefore, air pollutants distribution is not uniform and their monitoring at proper temporal and spatial resolution is necessary. Drone-borne analytical instrumentation can fulfill these requirements. Thanks to the rapid development in the drone manufacturing sector as well as in the field of portable detectors construction, applications of unmanned aerial vehicles (UAVs) for atmospheric pollution monitoring are growing. The purpose of this work is to give an overview of this matter. Therefore, this paper contains basic information on UAVs (i.e., description of different types of drones with their advantages and disadvantages) and analytical instrumentation (i.e., low-cost gas sensors, multi-sensor systems, electronic noses, high-accuracy optical analyzers, optical particle counters, radiation detectors) used for the monitoring of airborne pollution. Different ways of payload integration are addressed and examples of commercially available solutions are given. Examples of applications of drone-borne analytical systems for pollution monitoring coming from natural (i.e., volcanoes, thawing permafrost, wildfires) and anthropological (i.e., urbanization and industrialization; extraction, transport and storage of fossil fuels; exploitation of radioactive materials; waste and wastewater treatment; agriculture) sources are also described. Finally, the current limitations and future perspectives are discussed. Although there is a great potential for drones applications in the field of atmospheric pollution monitoring, several limitations should be addressed in the coming years. Future research should focus on improving performances of available analytical instrumentation and solving problems related to insufficient payload capacity and limited flight time of commonly used drones. We predict that applications of drone-assisted measurements will grow in the following years, especially in the field of odor pollution monitoring.

https://www.mdpi.com/2071-1050/14/18/11516/pdf?version=1663320979

Drone-Assisted Monitoring of Atmospheric Pollution—A Comprehensive Review

Marine food chains are highly stressed by aggressive fishing practices and environmental damage. Aquaculture has increasingly become a source of seafood which spares the deleterious impact on wild fisheries. However, continually monitoring water quality to successfully grow and harvest fish is labor intensive. The Hybrid Aerial Underwater Robotic System (HAUCS) is an Internet of Things (IoT) framework for aquaculture farms to relieve the farm operators of one of the most labor-intensive and time-consuming farm operations: water quality monitoring. To this end, HAUCS employs a swarm of unmanned aerial vehicles (UAVs) or drones integrated with underwater measurement devices to collect the in situ water quality data from aquaculture ponds. A critical aspect in HAUCS is to develop an effective path planning algorithm to be able to sample all the ponds on the farm with minimal resources (i.e., the number of UAVs and the power consumption of each UAV). Three methods of path planning for the UAVs are tested, a Graph Attention Model (GAM), the Google Linear Optimization Package (GLOP) and our proposed solution, the HAUCS Path Planning Algorithm (HPP). The designs of these path planning algorithms are discussed, and a simulator is developed to evaluate these methods’ performance. The algorithms are also experimentally validated at Southern Illinois University’s Aquaculture Research Center to demonstrate the feasibility of HAUCS. Based on the simulations and experimental studies, HPP is particularly suited for large farms, while GLOP or GAM is more suited to small or medium-sized farms. 

/pdf/developing-and-field-testing-path-planning-for-robotic-2u1gc5la.pdf

Developing and Field Testing Path Planning for Robotic Aquaculture Water Quality Monitoring

The integration of Artificial Intelligence (AI) tools and techniques has provided a significant advance in drone technology. Besides the military applications, drones are being increasingly used for logistics and cargo transportation, agriculture, construction, security and surveillance, exploration, and mobile wireless communication. The synergy between drones and AI has led to notable progress in the autonomy of drones, which have become capable of completing complex missions without direct human supervision. This study of the state of the art examines the impact of AI on improving drone autonomous behavior, covering from automation to complex real-time decision making. The paper provides detailed examples of the latest developments and applications. Ethical and regulatory challenges are also considered for the future evolution of this field of research, because drones with AI have the potential to greatly change our socioeconomic landscape. 

Artificial Intelligence Applied to Drone Control: A State of the Art

The evolving technologies regarding Unmanned Aerial Vehicles (UAVs) have led to their extended applicability in diverse domains, including surveillance, commerce, military, and smart electric grid monitoring. Modern UAV avionics enable precise aircraft operations through autonomous navigation, obstacle identification, and collision prevention. The structures of avionics are generally complex, and thorough hierarchies and intricate connections exist in between. For a comprehensive understanding of a UAV design, this paper aims to assess and critically review the purpose-classified electronics hardware inside UAVs, each with the corresponding performance metrics thoroughly analyzed. This review includes an exploration of different algorithms used for data processing, flight control, surveillance, navigation, protection, and communication. Consequently, this paper enriches the knowledge base of UAVs, offering an informative background on various UAV design processes, particularly those related to electric smart grid applications. As a future work recommendation, an actual relevant project is openly discussed.

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

The ever-increasing demand for urban delivery services significantly exacerbates the traffic conditions on our already overly congested urban streets. The problem is even worsened by the dearth of appropriate parking facilities available for efficient offloading operations. As a result, there has been a surge of interest in exploring eco-friendly alternatives to traditional truck-based logistics. Drone technology has significant potential to enable widespread home delivery services and reduce the reliance on trucks within urban areas. Both the academy and the industry have shown interest in developing swiftly deployable and cost-effective delivery systems that rely on drones. This work provides a comprehensive review of the current state of Drone-based Delivery Systems. It explores the latest advancements, methodologies, and employed techniques with a specific emphasis on optimizing task scheduling and designing efficient routes for drone deliveries. It critically analyzes the advantages and drawbacks of existing solutions, contributing a thorough classification based on crucial system features, such as fleet composition, degree of device cooperation, and underlying infrastructure.

Drone-Based Delivery Systems: A Survey on Route Planning

Delivery drones have been attracting attention as one of the promising technologies to deliver packages. Several research studies on routing problems specifically for drone delivery scenarios have extended Vehicle Routing Problems (VRPs). Most existing VRPs are based on Traveling Salesman Problems (TSPs) for minimizing the overall distance. On the other hand, VRPs for drone delivery have been aware of energy consumption due to the consideration of battery capacity. Despite hovering motions with loading packages accounting for a large portion of energy consumption since delivery drones need to hover with several packages, little research has been conducted on drone routing problems that aim at the minimization of overall flight times. In addition, flight time is strongly influenced by windy conditions such as headwinds and tailwinds. In this paper, we propose a VRP for drone delivery in which flight time is dependent on the weight of packages in a windy environment, called Flight Speed-aware Vehicle Routing Problem with Load and Wind (FSVRPLW). In this paper, flight speed changes depending on the load and wind. Specifically, a heavier package slows down flight speeds and a lighter package speeds up flight speeds. In addition, a headwind slows down flight speeds and a tailwind speed up flight speeds. We mathematically derived the problem and developed a dynamic programming algorithm to solve the problem. In the experiments, we investigate how much impact both the weight of packages and the wind have on the flight time. The experimental results indicate that taking loads and wind into account is very effective in reducing flight times. Moreover, the results of comparing the effects of load and wind indicate that flight time largely depends on the weight of packages.

https://www.mdpi.com/2504-446X/6/2/50/pdf

Load and Wind Aware Routing of Delivery Drones

Delivery drones have been attracting attention as a means of solving recent logistics issues, and many companies are focusing on their practical applications. Many research studies on delivery drones have been active for several decades. Among them, extended routing problems for drones have been proposed based on the Traveling Salesman Problem (TSP), which is used, for example, in truck vehicle routing problems. In parcel delivery by drones, additional constraints such as battery capacity, payload, and weather conditions need to be considered. This study addresses the routing problem for delivery drones. Most existing studies assume that the drone’s flight speed is constant regardless of the load. On the other hand, some studies assume that the flight speed varies with the load. This routing problem is called the Flight Speed-Aware Traveling Salesman Problem (FSTSP). The complexity of the drone flight speed function in this problem makes it difficult to solve the routing problem using general-purpose mathematical optimization solvers. In this study, the routing problem is reduced to an integer programming problem by using linear and quadratic approximations of the flight speed function. This enables us to solve the problem using general-purpose mathematical optimization solvers. In experiments, we compared the existing and proposed methods in terms of solving time and total flight time. The experimental results show that the proposed method with multiple threads has a shorter solving time than the state-of-the-art method when the number of customers is 17 or more. In terms of total flight time, the proposed methods deteriorate by an average of 0.4% for integer quadratic programming and an average of 1.9% for integer cubic programming compared to state-of-the-art methods. These experimental results show that the quadratic and cubic approximations of the problem have almost no degradation of the solution.

/pdf/an-integer-programming-based-approach-to-delivery-drone-3gnivmg6.pdf

An Integer Programming Based Approach to Delivery Drone Routing under Load-Dependent Flight Speed

This paper proposes an Integer Linear Programming (ILP) -based approach to delivery drone routing under load-dependent flight speed. Drone routing problems are attracting attention due to the increasing importance of drone delivery as a courier service. In general, delivery time is one of the most crucial concerns for both customers and couriers. Experiments demonstrate that the ILP-based approach can efficiently find a flight route in such a way that the overall delivery time is minimized, compared to the state-of-the-art methods.

An ILP-based Approach to Delivery Drone Routing under Load-dependent Flight Speed

This paper proposes an algorithm for a route search problem of traveling multiple sites with the time-varying waiting time. The considering problem is typified when a guest in a crowded theme park visits attractions in a short time. The cost function is defined by three kinds of times: traveling time, service time, and waiting time. The proposed method is inspired by the “insertion and manipulation PSO strategy,” which only considers the relative traveling order. This paper introduces absolute traveling order into the algorithm to consider time-varying waiting time. Numerical experiments with theme park data verify the effectiveness of the proposed algorithm. 

Optimal Route Search Problem for a Visitor in a Theme Park Considering Waiting Time

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Load and Wind Aware Routing of Delivery Drones

An Integer Programming Based Approach to Delivery Drone Routing under Load-Dependent Flight Speed

An ILP-based Approach to Delivery Drone Routing under Load-dependent Flight Speed

Optimal Route Search Problem for a Visitor in a Theme Park Considering Waiting Time