Summary of references on using UAVs to monitor targets on the ground.
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Article Type: Research Paper
Date of acceptance: November 2023
Date of publication: January 2024
DoI: 10.5772/acrt.29
copyright: ©2024 The Author(s), Licensee IntechOpen, License: CC BY 4.0
A drone-truck combined search-and-rescue operation involves a ground vehicle and a swarm of unmanned aerial vehicles (UAVs), where the UAVs provide surveillance coverage to guide the ground vehicle to navigate through the environment and carry out the search and rescue, and the ground vehicle functions as a service hub for carrying and recharging the UAVs. An effective strategy for providing persistent UAV surveillance coverage around the ground vehicle consists of initially forming the UAV swarm coverage and then controlling the UAV formation to follow the ground vehicle. This paper focuses on the formation of coverage and presents a method for planning an optimal placement of the UAVs to form seamless surveillance coverage around the ground vehicle. The optimization problem is formulated to determine the number and positions of UAVs that minimize the energy consumption in deploying and collecting those UAVs, subject to a set of constraints in UAV positioning, communication, and coverage, specifically the available number of UAVs, allowable range of UAV altitude, allowable energy consumption for deploying and collecting each UAV, communication ranges of UAVs and ground vehicle, safety distance between UAVs for collision and interference avoidance, and seamless coverage. A bi-layer optimization procedure is developed, with an outer layer searching through the allowable numbers of UAVs and an inner layer searching for the optimal positions for each specific number of UAVs. The optimal number and positions of UAVs are chosen by comparing among the solutions for different numbers of UAVs. A simulation study is carried out to validate the proposed optimization formulation and solution approach, where the simulation settings of UAVs, particularly the critical parameters including the UAV energy constants, visibility angle, altitude, and communication range, use the representative values presented in the cited literature. The simulation results show that the proposed approach is effective in planning the optimal number and positions of UAVs to provide seamless surveillance coverage for a ground vehicle. The next step of research will set priorities on comprehending the complexity of the solution space and enhancing the global optimality of the solution.
UAV
UAV swarm
UAV surveillance coverage
UAV placement
UAV deployment
constrained optimization
Author information
This research is oriented to a scenario where a swarm of UAVs provides persistent surveillance coverage for a ground vehicle (Figure 1) in a collaborative search-and-rescue mission. The ground vehicle needs to detect and overcome multiple environmental complications, e.g., road conditions, obstacles, moving objects, and other contingent situations, and to detect and locate rescue targets [1, 2]. If deployed alone, the ground vehicle has a limited sensing capability due to the limited detection range of sensors and obstructions by surrounding objects [1, 2]. Sensing from the air and being able to cover a larger ground area by collaboration [3–5], the UAVs can support the ground mission by conducting continuous surveillance around the ground vehicle to inform road and weather conditions, alert to contingent and dangerous conditions, and detect humans who need help, etc. This can vastly extend the sensing capability of the ground vehicle and largely improve the efficiency and safety of the mission [4, 5]. Meanwhile, the ground vehicle can serve as a supporting unit for the UAVs, carrying them to the regions of interest and functioning as a service hub for recharging and replacing the UAVs. This can largely enhance the persistence of UAV operations [5].
One effective strategy to provide persistent UAV surveillance coverage for the navigating ground vehicle is to initially form a UAV coverage around the ground vehicle at the starting point and then control the UAV formation to follow the moving ground vehicle. The work of this paper focuses on the formation of UAV coverage for the ground vehicle and introduces an approach to determine an optimal placement of UAVs for seamless surveillance coverage around the ground vehicle, with a comprehensive consideration of the energy efficiency, sensing capability, positioning constraint, communication range, and availability of UAVs.
The collaboration between UAVs and ground vehicles has attracted substantial research efforts recently, categorized as drone-truck combined operations (DTCO) [6, 7]. The most researched DTCO application is the delivery of items using a drone-truck delivery system, where the associated research problems are mainly the traveling salesman problem with drones (TSPD) and vehicle routing problem with drones (VRPD) [6, 7]. Research has also been carried out in the context of area coverage. Mathew
References | Optimization problem | Algorithms |
---|---|---|
[12, 13] | Minimization of the number of UAVs and total energy consumption | C-SDLP, K-means, C-MDLP, and L-MDLP |
[14] | Minimization of the number of UAVs, Oriented Line Segment Coverage Problem | Greedy approximation |
[15] | Optimization of the locations | Greedy, reverse greedy, carousel greedy, linear programming, particle swarm optimization, simulated annealing, genetic, and ant colony optimization |
[16] | Minimization of energy consumption, maximization of total coverage, maintenance of connectivity, and minimization of overlaps | Multi-objective artificial bee colony, multi-objective particle swarm optimization, non-dominated sorting genetic algorithm II, strength Pareto evolutionary algorithm II, and non-dominated sorting genetic algorithm III |
[17] | Maximization of the coverage rate, point-level clarity, uniform clarity, and resource utilization | Improved constrained two-archive evolutionary algorithm |
[18] | Maximization of the coverage area | Adaptive multiple pruning search method |
[19] | Tradeoff between the signal coverage and interference | Two-phase evolution algorithm |
[20] | Maximization of the number of covered targets | Particle swarm optimization |
More references on area coverage using UAVs are found in the context of optimizing the placement of UAVs carrying cameras/sensors to monitor targets or cover an area on the ground (Table 1). Pugliese
References | Optimization problem | Algorithms |
---|---|---|
[21, 22] | Maximization of the coverage of users | Graphs of communication connectivity and locations, greedy algorithm |
[23] | Maximization of coverage, fault tolerance, and redundancy | Multi-layout multi-subpopulation genetic algorithm |
[24] | Maximization of the coverage area and density | Circle Packing Theory |
[25] | Maximization of the total amount of data transmitted with a trade-off among flight altitude, energy expense, and travel time | Lagrangian dual relaxation and a heuristic approach using interior-point and subgradient projection |
[26] | Maximization of the total spectral efficiency of the network while maintaining a minimum quality of service requirement | K-means clustering and a stable marriage approach |
[27] | Minimization of the ratio between the number of UAVs and energy efficiency | Genetic algorithm and particle swarm optimization |
[28] | Optimization of the number of UAV-SCs, their placement, associations, and the power allocation, subject to user quality of service, transmit power, and front haul capacity constraints | Particle swarm optimization |
[29] | Maximization of the minimum leftover energy storage | Binary search and dynamic programming |
[30] | Multi-objective optimization considering target coverage, quality of service, and energy consumption | Multi-objective particle swarm optimization, non-dominated sorting genetic algorithm II, strength pareto evolutionary algorithm 2, and pareto envelope-based selection algorithm II |
[31] | Maximization of the number of users served by UAV base stations subject to the constraints of path-loss compensation factor, minimum mean and edge throughput, ABS height, and transmit power budget | Modified K-means |
[32] | Maximization of the system throughput | Mean-shift and successive convex approximation algorithms |
[33] | Maximization of the network throughput subject to the constraints of locations, UAV-device associations, scheduling, communication, and time | Dinkelbach-based algorithm |
[34] | Maximization of the number of served users subject to user data-rate requirements and base station capacity limit | Genetic algorithm |
[35] | Maximization of the user coverage | Particle swarm optimization and virtual repulsive force |
[36] | Maximization of the fair coverage versus energy consumption subject to the backhaul constraints | Proximal stochastic gradient descent based alternating algorithm |
[37] | Minimization of the number of drones subject to the constraints of coverage and service quality | Particle swarm optimization |
[38] | Maximization of the total network throughput | Virtual force field and particle swarm optimization |
The optimal deployment of UAVs is also a major research problem associated with the application of UAVs to provide communication network coverage for users and stations on the ground (Table 2). Huang and Savkin determined the 2D placement of a set of drones that maximized the coverage of users subject to the constraint of communication range, based on the graphs of communication connectivity and locations [21]. Huang
The work of this paper targets a novel application of DTCO search-and-rescue mission and emphasizes continuous and seamless area coverage around a ground vehicle. This imposes new challenges in formulating and solving the research problem of finding an optimal deployment, including both the number and positions, of UAVs to provide surveillance coverage. The cited references on coverage using UAVs are effective in providing solutions to their specific optimization problems associated with their specific applications respectively. However, a vast majority of them deal with covering discrete targets or discretized spaces [12–15, 17–23, 25–28, 30–38], where the ways of formulation and solution provide inspiration but cannot be adopted directly by the work of this paper which deals with continuous and seamless area coverage. Meanwhile, those references which deal with continuous area coverage focus on finding optimal placement of a given number of UAVs [16, 24, 29], while the work of this paper is to find both the optimal number and positions of UAVs to form the coverage. Finding the optimal positions of a given number of UAVs is a fixed-length optimization problem, where the number of input variables is constant during the search process. Most existing optimization algorithms deal with fixed-length problems. Finding both the optimal number and positions of UAVs is a variable-length optimization problem, where the number of input variables is variable during the search process. Such a problem is more challenging to solve. Some studies, which deal with the minimization of the number of UAVs, formulate their optimization problems into fixed-length problems based on the definition of a binary association function between the UAVs and targets [12, 13, 28]. Though this approach works effectively with discrete targets, it is not directly applicable to the continuous area coverage problem of this study.
This study addresses the challenge and develops an effective approach for determining the optimal number and positions of UAVs in order to provide continuous and seamless UAV surveillance coverage around a ground vehicle. The main contributions of this study are summarized as follows:
The research problem of planning the deployment of UAVs, associated with the novel application of DTCO search-and-rescue mission, is modeled as a new constrained variable-length optimization problem. This optimization problem determines the optimal number and 3D positions of UAVs that minimize energy consumption in deploying and collecting those UAVs, subject to a comprehensive set of constraints on the operations of UAVs, at both individual and swarm levels, specifically including the available number of UAVs, permissible range of UAV altitude, allowable energy consumption for deploying and collecting each UAV, communication ranges of UAVs and ground vehicle, safety distance between UAVs for collision and interference avoidance, and seamless coverage.
A bi-layer optimization solution process is proposed to solve the formulated constrained variable-length optimization problem, with an outer layer searching through the allowable numbers of UAVs and an inner layer searching for the optimal positions for each specific number of UAVs. The optimal number and positions of UAVs are chosen by comparing among those solutions for different numbers of UAVs. This bi-layer strategy provides an effective algorithmic framework, which can work with any suitable fixed-length optimization algorithm on the inner layer, for solving the targeted variable-length optimization problem. This study specifically chooses the genetic algorithm (GA) as the inner layer algorithm to work with the bi-layer process. This combination provides an effective optimization algorithm for this constrained variable-length optimization problem.
The proposed optimization formulation and solution approach is validated through a simulation study. The simulation settings, including the UAV energy constants, visibility angle, altitude, and communication range, adopt the representative realistic values from the cited literature, as shown in a later section of this paper.
The remaining parts of this paper are organized as follows. Section 2 presents the formulation of the associated optimization problem. Section 3 discusses the solution procedure of the optimization problem. Section 4 deals with the simulation results. Section 5 concludes the study and discusses the future work.
In order to form an efficient and reliable UAV swarm surveillance coverage around a ground vehicle, an optimization problem is formulated with the objective of finding an optimal deployment of UAVs from the perspective of energy efficiency, subject to the relevant constraints in UAV positioning, communication, and coverage.
The research problem is defined with the following assumptions:
The involved UAVs are homogenous, with the same capabilities of kinematics, sensing, communication, and energy.
The involved UAVs are modeled as point UAVs with omni-directional flight capability, which applies to a wide range of helicopter-like single-rotor and multi-rotor UAVs.
The ground vehicle has sufficient energy capacity to support the ground mission and provide recharging or replacement to UAVs.
Each UAV is fully charged when it leaves the ground vehicle.
The region of interest does not interfere with no-fly zones.
A desirable ground coverage is specified as a circular area centered at the ground vehicle.
In this research, an effective deployment of UAVs means a number of UAVs being placed for seamless surveillance coverage over a ground area with a desirable radius around the ground vehicle. Here, the desirable radius of coverage is denoted by
Each UAV has a finite energy capacity. With less energy spent in deploying and collecting a UAV, more energy is available for the UAV to carry out its service activities including flight, hovering, sensing, and wireless communication. From the perspective of energy efficiency, a deployment of UAVs which requires the minimal amount of energy for the UAVs to fly to their monitoring positions for surveillance and fly back to the ground vehicle for recharging or replacement is considered an optimal deployment. Thus, the optimization problem takes the following objective function
Moreover, the optimization problem is subject to several constraints which can be categorized into the bounds for the input variables and constraints defined upon the input variables. The bound constraints for the input variables include:
The allowable range for the number of UAVs in use,
The allowable range for the position of a UAV,
The constraints defined upon the input variables include:
The constraint on the allowable overhead energy consumption for each UAV,
The constraint on the communication ranges among the UAVs and ground vehicle: Communication connectivity is needed for data transfer and coordination among the UAVs and between the UAVs and ground vehicle. The wireless communication range of a UAV or ground vehicle is always limited. To efficiently use UAVs for surveillance coverage, a networking strategy is adopted, where each UAV forms communication links with only a few closest neighbors which include other UAVs and/or the ground vehicle. In this way, the UAVs do not need to stay within the communication range of the ground vehicle and thus can spread out to form larger coverage, while the communications between the ground vehicle and farther UAVs are accomplished through networking. Accordingly, those UAVs in the neighborhood of the ground vehicle should connect to the ground vehicle, i.e.
The constraint on the distance between UAVs for collision and interference avoidance: In order to avoid collisions, aerodynamic interference, and communication interference among UAVs, a constraint on the horizontal distance between any two neighboring UAVs is considered as
An additional constraint that the optimization problem is subject to is the seamlessness of the resulting surveillance coverage. The ground coverage of a UAV is considered to be related to its altitude [13, 14, 16, 24] as
To summarize, the optimization problem of finding an optimal deployment of UAVs subject to the relevant constraints is defined as
Algorithm 1 proposes a procedure structure to solve the optimization problem formulated in the previous section, for a seamless UAV swarm surveillance coverage around a ground vehicle with the consideration of energy efficiency and the relevant constraints in UAV positioning, communication, and coverage.
The goal of the optimization procedure is to find the optimal number and positions of UAVs seamlessly covering the desirable area around the ground vehicle. Thus, both the number of UAVs and positions of UAVs are input variables to the objective function, as shown in Equation (10). However, because the number of position variables for UAVs is determined by the number of UAVs, the total number of input variables is not pre-defined for the optimization procedure. This situation makes it challenging to use the number of UAVs and associated position variables as a whole set of input variables in a single optimization algorithm, due to the fact that most optimization algorithms only work with a fixed number of input variables. To deal with this situation, the proposed optimization procedure adopts a bi-layer hierarchical structure, consisting of an outer and inner layer. On the outer layer, the procedure iterates through an allowable range of numbers of UAVs, i.e.
On the inner layer of the optimization procedure, the optimal positions of a specific number of UAVs are searched for to minimize the total overhead energy consumption of those UAVs subject to the constraints in Equation (11). Local optimization algorithms including nonlinear programming [41] and pattern search [42] were tested initially. Starting the search from an initial guess of the values of the input variables, these local optimization algorithms turned out to have relatively low time complexity but very often failed to find feasible solutions which satisfy all the constraints. This situation is attributed to the high dimensionality of the solution space and the high complexity of the constraints. In particular, the implementations of those constraints on the communication range (defined by Equations (5) and (6)), collision and interference avoidance distance (defined by Equation (7)), and seamless coverage (defined by Equation (9)) are highly complex. Constraints (5), (6), and (7) depend on the recognition of neighboring UAVs, while Constraint (9) cannot be directly represented as a function of the input variables. This situation increases the complexity of the optimization problem. To deal with this issue, a global optimization algorithm—genetic algorithm (GA) [43] was also tested. Compared to local optimization algorithms, GA starts the search from a population of randomly generated candidate solutions and had much better performance in obtaining feasible solutions which satisfy all the constraints, though with higher time complexity. Thus, in this study, GA is adopted as the optimization algorithm on the inner layer of the proposed optimization procedure.
Moreover, the positions of UAVs resulting from the inner-layer optimization algorithm such as GA may indicate the existence of redundancy in the number of UAVs. This redundancy is reflected by the fact that some UAVs are placed at the lower-bound altitude (i.e.
During the optimization procedure, the implementation of the seamless coverage constraint (Equation (9)) requires to check in each iteration if the union of the ground coverage of UAVs provides a seamless coverage of the desirable area around the ground vehicle. It would be highly challenging to implement, if the desirable coverage and individual UAV coverages are treated as continuous areas, due to the lack of methods to represent the union of continuous areas, which could be a highly irregular shape and/or disconnected, as well as checking the overlap of continuous areas with irregular shapes. To deal with this issue, the proposed optimization procedure discretizes the desirable and individual coverage areas, and checks the seamlessness of the resulting coverage during each iteration by checking if any grid element inside the desirable coverage is not covered by any UAV. This turns out to be an effective and conceptually simple approach. Similarly, during the post-process of the inner layer, the redundancy of a UAV is determined by checking if all the grid elements inside the individual UAV coverage are covered by other UAVs.
The proposed optimization procedure for planning the optimal deployment of UAVs to provide seamless surveillance coverage around a ground vehicle is programmed and tested using MATLAB.
The settings of the simulation include:
UAV energy terms: In accordance with [29, 39], the UAV energy consumption constant for horizontal translation is set to be 𝜂trans = 21. 6 kWh/m, the energy consumption constant for ascending 𝜂asc = 5𝜂trans, and the UAV energy capacity
UAV field of view: In accordance with [16, 30], the visibility angle of UAVs is set to be 𝜃 = 60°.
UAV altitude: In accordance with [25, 27], the lower and upper bounds of UAV altitude are set to be
Communication range of UAVs and ground vehicle: In accordance with Wi-Fi (IEEE 802.11), the communication ranges of the ground vehicle and UAVs are set to be
Safety distance between UAVs: To avoid collisions, signaling interference, and wind effects, the minimum permissible horizontal distance between UAVs is set to be
Target ground coverage: The desirable coverage around the ground vehicle is set to be a circular area centered at the ground vehicle with a radius of
Search range for the number of UAVs: The number of available UAVs is set to be
With the above simulation settings, the optimization procedure iterates through a sequence of
2 | 3 | 4 | 5 | 6 | 7 | 8 | |
NFS | NFS | 3 | NFS | NFS | 5 | 4 | |
𝛴 | NFS | NFS | 19.60 | NFS | NFS | 25.06 | 23.32 |
9 | 10 | 11 | 12 | 13 | 14 | 15 | |
7 | 5 | 8 | 7 | 7 | NFS | 9 | |
𝛴 | 31.21 | 26.11 | 31.83 | 30.58 | 30.32 | NFS | 34.55 |
It is noticeable from Table 3 that, for several iterations, the situation of no feasible solution is reported, which means that GA fails to find an optimal solution for the positions of those
When
More often, when
The situation of convergence to local minima is also observed from those iterations with feasible solutions in Table 3. By comparing among the resulting valid solutions with only non-redundant UAVs and associated total overhead energy consumptions, it is clear that, in this simulation, it is sufficient to use 3 UAVs to provide seamless coverage over the desirable area with a minimum total overhead energy consumption. This solution is obtained when
During each outer-loop iteration of the optimization procedure, GA searches for the optimal positions of a specific number
The ground coverage of a redundant UAV is completely covered by the other UAVs;
A redundant UAV is placed at the lowest permissible altitude
Figures 2 and 3 present two examples of such redundancy for
UAV | 1 |
|
| 4 | 5 | 6 | 7 |
---|---|---|---|---|---|---|---|
17.46 |
|
| 17.26 | 22.78 | 20.26 | 11.56 | |
𝛼 | 125.09 |
|
| 265.81 | 76.71 | 187.07 | 359.29 |
27.05 |
|
| 33.63 | 20.87 | 32.60 | 42.93 | |
𝛴 | 28.57 | 𝛴 | 25.06 |
UAV |
|
|
| 4 | 5 | 6 | 7 | 8 |
|
|
---|---|---|---|---|---|---|---|---|---|---|
|
|
| 18.53 | 13.12 | 23.28 | 16.68 | 20.46 |
|
| |
𝛼 |
|
|
| 267.31 | 78.63 | 184.82 | 356.31 | 133.95 |
|
|
|
|
| 33.00 | 39.09 | 34.96 | 39.30 | 17.59 |
|
| |
𝛴 | 35.64 | 𝛴 | 26.11 |
As indicated in Table 3, in this simulation, the resulting optimal coverage, which has the minimal total overhead energy consumption and satisfies all the involved constraints, is provided by 3 UAVs. Figure 4 shows the resulting ground coverage by these UAVs, and Table 6 presents the resulting optimal positions of the UAVs. Compared with the desirable coverage around the ground vehicle, these UAVs provide seamless coverage, as shown in Figure 4. Moreover, it is verified that the resulting positions of UAVs satisfy all the constraints, as shown in Table 6. This simulation provides a validation of the proposed formulation and solution approach for planning an optimal placement of UAVs to provide seamless surveillance coverage around a ground vehicle.
| | | | |
15.86 | 8.94 | 6.72 | ∀ | |
𝛼 | 214.15 | 330.04 | 95.62 | ∀ |
41.27 | 46.20 | 47.64 | ∀ | |
44.21 | 47.06 | 48.11 | ∀ | |
| | | | |
21.90 | 20.95 | 14.04 | 2 or more | |
21.34 | 19.96 | 13.97 | ∀ | |
𝛴 | 19.60 | 𝛴 |
The problem of optimal placement of UAVs for seamless surveillance coverage around a ground vehicle in a collaborative search-and-rescue operation is being studied. A new constrained variable-length optimization problem is formulated to determine the optimal number and positions of UAVs, with a comprehensive consideration of DTCO, energy efficiency, seamless coverage, and positioning and communication constraints. A novel bi-layer optimization process is introduced to provide an algorithmic framework which works with fixed-length optimization algorithms to solve variable-length optimization problems. The bi-layer process, along with the genetic algorithm provides an effective optimization algorithm to solve the targeted constrained variable-length optimization problem, by checking the optimal placements for different numbers of UAVs and picking the best solution with the best energy efficiency. The proposed optimization formulation and solution approach is validated through a simulation study. In order to carry out the simulation study with realistic settings of UAVs, critical parameters, such as UAV energy constants, visibility angle, altitude, and communication range, use the representative values presented in the well-received literature, as cited in Section 4. The simulation results show that the proposed approach is effective in planning the optimal number and positions of UAVs to provide seamless surveillance coverage for a ground vehicle.
As discussed in Section 1, a vast majority of the cited references on coverage using UAVs deal with discrete targets or discretized spaces [12–15, 17–23, 25–28, 30–38], and the references, which deal with continuous area coverage, focus on finding optimal placement of a given number of UAVs [16, 24, 29]. This study deals with forming continuous and seamless area coverage around a ground vehicle, and the proposed approach finds the optimal number and positions of UAVs to provide continuous area coverage subject to comprehensive constraints. Therefore, compared with the existing works, this study contributes to the optimization formulation as well as solution approach. Moreover, the proposed bi-layer algorithmic framework brings in a novel strategy for using available fixed-length optimization algorithms to solve challenging variable-length optimization problems. This idea can be generalized and applied to a broader scope of optimization problems.
Further research will be carried out in the following aspects:
Priority is to understand the complexity of the solution space and improve the global optimality of the solution.
Another interesting problem is the scalability of the solution algorithm to significantly increased number of UAVs for large area coverage, depending on the need of the targeted application. The general understanding is that the scalability of the bi-layer optimization procedure is determined by the scalability of both the outer layer and inner layer. While the outer layer iterates through the number of UAVs and thus is linearly scalable, the inner layer scalability depends on the adopted inner-layer optimization algorithm. Future study will test the scalability with GA and other different inner-layer algorithms.
The current research is carried out based on the assumption that the involved UAVs are homogenous, with the same capabilities of kinematics, sensing, communication, and energy, and are omni-directional helicopter-like single-rotor or multi-rotor UAVs, in order to lay the foundation. Future study will address this limitation and extend to more generic and heterogeneous situations involving UAVs of different types and capabilities.
Further research will look into the problem of controlling the UAV formation to follow the ground vehicle. Control approaches robust against the time delay, system disturbance, and modeling errors, e.g., like those in [44–46], will be explored. Moreover, the problem of malfunctioning or out-of-power UAVs will be addressed in the phase of real-time operations and control of UAVs. While monitoring the state of each UAV, any malfunctioning or out-of-power UAV will return to the ground vehicle which serves as the service hub for UAVs, and a replacement UAV will fly from the ground vehicle to the corresponding position to fill the gap.
The approach discussed in this paper is well applicable to search-and-rescue operations in different environments including ground, urban, battlefield, as well as water. It can also be applied to other domains beyond search-and-rescue, such as agriculture monitoring, smart agriculture, wildlife tracking, and environmental exploration, where UAVs and ground vehicles collaborate to carry out DTCO operations and UAVs are used to provide area/surveillance coverage.
The authors declare no conflict of interest.
Effort sponsored by the Air Force under PIA FA8750-19-3-1000. The U.S. Government is authorized to reproduce and distribute copies for Governmental purposes notwithstanding any copyright or other restrictive legends.
The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Air Force or the U.S. Government.
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Article Type: Research Paper
Date of acceptance: November 2023
Date of publication: January 2024
DOI: 10.5772/acrt.29
Copyright: The Author(s), Licensee IntechOpen, License: CC BY 4.0
© The Author(s) 2024. Licensee IntechOpen. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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