**4. Swarm challenges**

#### **4.1 Swarm control**

The basis of a UAV swarm is to control all the individual UAVs during the planned path. To solve the reconstruction, anti-collision, search, and tracking issues in the swarm formations the development of proper control system frameworks and controllers is required [28]. Centralized and distributed are the two major control platforms for the automation-equipped clusters. The main advantage of the centralized platform is achieving higher quality in outputs but with the limitation of limited scalability. Whereas the main contribution of the decentralized platform is its enhanced scalability, which is less complex. The network of the UAV swarm guarantees the nodes' connectivity and simplifies the application designs. Sensor inputs with the environmental and target's prior knowledge are the essentials for the traditional models.

Various research overcome these issues using multi-layer distributed control frameworks. The designing of the controller is crucial in the process design of the UAVs. Many studies suggest using the ANFIS controller for the learning error reduction and quality improvement of the controller. During the movement of UAVs following a specific path, the target tracking performance is directly affected by the control of the airborne gimbal system. Some studies propose the nonlinear Hammerstein block structure for modeling gimbal systems to enhance the efficiency of the model predictive controller (MPC). This also improves the performance of the target tracking under external interference in real-time. Other approaches for formation control are leader-follower strategy, consensus theory, virtual structure method,

**Figure 1.** *Distributed guidance model using leader-follower controller.*

behavior method, etc. **Figure 1** represents the concept of distributed guidance model using a leader-follower controller as given in [29]. The leader guidance algorithm is given in the first column of this figure, whereas the other two columns represent the followers. The preassigned topology in this model cannot be altered.

## **4.2 Swarm path planning**

The path planning of a UAV swarm is quite challenging [30]. To solve this NP-hard problem many studies suggest path-planning algorithms. These algorithms are categorized into classic algorithms and meta-heuristic algorithms as shown in **Figure 2**. Classic algorithms require environmental information while meta-heuristic algorithms require information on the real-time position and measured environmental elements. Road map algorithm (RMA), A\* algorithm, and artificial potential field (APF) method

are some examples of classic algorithms as presented in **Figure 2**. Particle swarm optimization (PSO), pigeon-inspired optimization algorithm (PIO), fruit fly optimization algorithm (FOA), and gray wolf optimization algorithm (GWO) are some examples of meta-heuristic algorithms as given in **Figure 2**.

The swarm path planning can be categorized into dynamic path planning, 3D path planning, area coverage path planning, and optimal path planning [31]. Dynamic path planning is essential for the task performance of a UAV swarm in a complex environment. To ensure dynamic path planning many researchers suggest using collision probability with Kalman Filter, the artificial potential field (APF) with the wall-follow method (WFM) method, trail detection, scene-understanding frameworks, and so on. All these methods provide better direction estimation, better performance, and avoid path conflicts. 3D path planning is complicated, but many studies apply meta-heuristic algorithms for dealing with it. Like the GWO algorithm realizes the feasible flight trajectory, the FOA algorithm performs local optimization and PIO optimizes the initial path.

All these algorithms work efficiently for 3D path planning of UAV swarms under threats and emergencies. Path planning in which UAVs can move at all the areas of interest points is area coverage path planning. Many studies suggest a five-state Markov chain model, improved potential game theory, and a cyber-physical system for it. For optimal path planning battery capacity of UAVs, matching performance, and energy consumption are serious considerations. Studies suggest a coupled and distributed planning strategy, mobile crowd perception system (MCS), and energyefficient data collection frameworks for optimal path planning.

#### **4.3 Swarm architecture**

For swarm implementations, the architecture of UAVs is of much importance [32]. Architecture is a combination of design, management, and optimization techniques. Swarm architecture can be based on communication, mission doctrine, control, etc. Communication-based swarm architecture has two forms. Ad-hoc network-based architecture and infrastructure-based swarm architecture. Both are promising architectures and perform well under complex environments.

Considering the operational mission for designing a swarm architecture is also important. Studies consider it imprudent if the mission doctrine is not considered. Current approaches include bottom-up modeling approaches and top-down design approaches for designing swarm systems. Similarly, control-based architectures are also beneficial for the swarm. **Figure 3** gives a mission-based architecture for swarm composability (MASC) as presented in [33]. This framework focuses on the phases, tactics, plays, and algorithms. According to this figure, mission explains the entire task, phases evaluate specific periods, tactics are the individuals' usage in a particular order for task performance, the play describes the swarm behavior and algorithms are the procedures. Moreover, linking distributed behavior control methods with centralized coordination can efficiently work for swarm aerial missions. The aerospace architecture can perform the thinking task, execution task, reaction task, and socialization task efficiently. Moreover, the Internet of Things (IoT) supports swarm architectures and facilitates interactions as well.

#### **4.4 Swarm monitoring and tracking**

Another prime challenge for a swarm is monitoring and tracking. All the UAVs' positions, status, and the external environment change concerning time during

**Figure 3.** *MASC framework.*

a swarm's operation. Moreover, the swarm adapts to these changes and adjusts its behavior accordingly. For this, continuous monitoring and tracking are essential. Many researchers propose different control models, simulation models, and simulation tools for solving monitoring and tracking challenge. Dynamic Data-Driven Application System (DDDAS) is a solution, which assists in the environment and the mission's adaptation [34].

Target searching requires consideration of effective methods and control strategies. If the target knows about the mobility and position of the searcher, then the searching complexity will be enhanced. The distributed strategy also provides solutions to the Automatic Target Recognition (ATR) issue. Many researchers suggest layered detection solutions, learning-edge software, and optimal technology for tracking UAVs in a swarm. **Figure 4** represents spatial distribution using an improved bean optimization algorithm (BOA) that is based on the population evolution model as developed in [35]. In this figure, the swarm space is distributed into three layers, a temporary dispatch layer, an individual layer, and a parent layer. BOA shows effective target search capabilities, emerging group intelligence, and distributed collaborative interaction. The individuals' distribution using BOA can be given as,

$$X\_{\neq}(t+1) = X\_{\,\,i}(t), \,\,\text{if } X\_{\neq}(t+1) \text{ is a parent} \tag{1}$$

$$G\left(X\_i(t)\right), \text{if } X\_\neq(t+1) \text{ is not a parent} \tag{2}$$

**Figure 4.** *Spatial distribution of individual UAVs.*

Here, the parent *i* generates the position of individual *j* and is denoted by *X t+ ij*( 1) , the *X t <sup>i</sup>*( ) denotes the parent *i*, and *GX t* ( *<sup>i</sup>*( )) gives the distributed function.

#### **4.5 Swarm communication**

Communication is one of the prime challenges for UAV swarms [36]. Under a noisy and complex environment, a swarm requires accurate and efficient data communication for the task executions. Data communication depends upon an appropriate structured network. **Figure 5** shows that wireless ad-hoc networks are capable to provide efficient communications as presented in [37]. A base station is connected with two UAVs in this figure. Both of these UAVs are further connected to a different group of UAVs. The intraconnection of UAVs is independent but the interconnection is dependent on the base station. Three forms of networks include Flying Ad-hoc Network (FANET), Mobile Adhoc Network (MANET), and Vehicle Adhoc Networks (VANET). FANET network provides a network for communication between a few UAVs with GCS, while the rest of the UAVs communicate with each other. FANET enhances the range of communication as well as the connectivity in areas with limited cellular infrastructure and obstacles. Whereas MANET and VANET are interlinked with FANET. Therefore, FANET possesses similar features to both the other forms except a few ones like mobility, better connectivity, energy constraints,

**Figure 5.** *Ad-hoc network for multi-group UAV.*

etc. MANET does not require any support from the infrastructure of the internet and is formed with a required number of mobile devices. Whereas the VANET consists of terrestrial vehicles.

For quick deployment UAVs act as aerial base stations in a swarm to support the infrastructure of the communication. This wireless networking is implemented successfully between UAV and Internet of Things (UAV-IoT), UAV and cellular unloading (UAV-CO), UAV and emergency communications (UAV-EC), and others. These improve transmission efficiency and reduce response delays. Moreover, efficient communication can also solve other challenges like cooperation, control, and path planning. Hence, the foundation of a UAV swarm is effective communication.

#### **4.6 Swarm safe distance protocol**

In UAV swarm collaboration, the self-organization behavior becomes essential for each UAV. Transfer of data and communication take place among all the UAVs for appropriate decision-making during self-organizing swarm flights. But there is a risk of collision among UAVs in complex flight conditions. Hence, one of the key challenges is to provide a collision avoidance protocol for safe flights [38]. These protocols are necessary because of the continuous mobility of UAVs, limited resources, and air links instability. All the UAV members of a swarm must know each other's positions using a multi-hop connection. Most of these require a global positioning system (GPS) and in the absence of GPS, the location of a UAV can be estimated using the Euclidean distance formula with three nodes of known positions. Several kinds of research provide safe flight protocols using goose swarm algorithms, Reynolds rule, and pigeon flock algorithm. Other than this, many optimization algorithms can promote the UAV swarm consensus. Reynolds protocol uses three flocking behavioral rules. First is the separation rule in which a UAV attempts to move away from neighboring UAVs in a swarm. Second is the alignment rule in which UAV attempts to align the velocity with the neighboring UAV to avoid collisions. The third is the cohesion rule-following which the UAV tries to share the same position by coming closer to the neighboring UAVs to form clusters. A self-organized flight model using Reynolds Rules is given using the idea of [39]. All these rules are summarized in the following equation,

$$J\_i = V\left(||\left|s\_{\psi}\left(t\right)\right|\right) + \sum\_{j \in \text{Ni}\{t\}} ||\dot{\kappa}\left(t\right)||^2\tag{3}$$

Here N shows the number of UAVs in a swarm, *ij s* is the position of two UAVs i, and j in time t and ∈ N () with represents an attractive–repulsive potential function with a local minimum. These rules provide a proper safe flight protocol among the UAV swarm but still have limitations, which should be improved to achieve safer trajectory planning.

#### **5. Related survey**

Successful motion planning of UAV swarms requires significant optimization algorithms with relevant infrastructures or models. **Table 1** provides a comprehensive exploration of techniques and models applied for the motion planning of a swarm of UAVs. This review will provide a detailed and better understanding of appropriate techniques for challenges faced by UAV flocks used in previous and current studies.

Kim et al. [40] considered the Kalman filter with Covariance Intersection (CI) algorithm and smoothing, and string-matching methodologies to observe the airborne monitoring using a swarm of UAVs. The researchers employed the hidden Markov model (HMM) for path planning and achieved an increment in the tracking accuracy and a reduction in the tracking error. Oh et al. [41] suggested a vector field guidance approach to track the moving objects. The study further introduced a two-phase approach; K-means clustering with Fisher information matrix (FIM) and cooperative standoff tracking method for this purpose. The results showed standoff group tracking successfully, allowed local replanning, and kept all the targets of interest within the sensor's field-of-view (FOV). Sampedro et al. [42] presented Global Mission Planner (GMP) and Agent Mission Planner (AMP) for a UAV swarm. Their proposal gave a complete operative, robust, scalable, and flexible framework that automatically performed many high-level missions.

Yang et al. [43] analyzed eleven swarm intelligence (SI) algorithms for UAV swarm. This research explained the features and principles of these algorithms and analyzed different algorithm combinations and task assignments for multiple UAVs. Hocraffer and Nam [44] performed a meta-examination of the human-system interface concerning human factors. The analysis provided a basis to start research, enhanced situation awareness (SA), and yielded efficient results. Lee and Kim [45] studied multirotor dynamic models with linear and nonlinear controllers for trajectory tracking control of multi-UAVs. The study showed that linear controllers were easily applicable, robust, and provide optimality and some nonlinear controllers were also easily applicable, intuitive, and gave global stability. Yang et al. [46] linked an orthogonal multi-swarm cooperative particle swarm optimization algorithm with a knowledge base model (MCPSO-K). This technique converged faster, avoided premature convergence, lessened the computational costs, and ensured the uniform distribution of particles.









#### **Table 1.**

*A comprehensive review of the motion planning of the swarm of UAVs applying various techniques and models.*

Guastella et al. [47] considered operating space as a 3-directional (3D) grid and applied the modified A\* algorithm for path planning of multi-UAVs. The researchers found a reduction in computational time, improvement in planned trajectories, and automatic redistribution of targets. Duan et al. [48] gave a novel hybrid metaheuristic approach by linking memetic algorithm (MA) with variable neighborhood descend (VND) algorithm for path planning of multiple UAVs. The results yielded an optimization in routes, gave highly effective results, and solved capacity vehicle routing problems (CVRP) and even Non-deterministic Polynomial-time hard (NP-hard) problems efficiently. Koohifar et al. [49] applied the extended Kalman filter (EKF) with recursive Bayesian estimator, and Cramer-Rao lower bound (CRLB) path planning for UAV swarms. The analysis showed that the proposed method planned the future tracking trajectory successfully. Moreover, CRLB outperformed and enhanced the performance as well.

Shao et al. [50] combined a robust integral of the sign of the error (RISE) feedback controller with an extended state observer (ESO) and used residual estimation error. This strategy tackled the lumped disturbance issues and achieved tracking accuracy, effectiveness, and superiority. Campion et al. [51] studied cellular mobile infrastructure, machine learning and distributed control algorithms, machine-tomachine (M2M) communication, and 5th generation (5G) networks for UAV swarm. This study showed that the applied techniques alleviated limiting factors for previous studies and enhanced the efficiency of the swarm and commercial usage. Shao et al. [52] proposed extended state observer (ESO)-based robust controllers with dynamic surface control (DSC) design and disturbance observer-based (DOB) control techniques. This proposal showed effective and superior results in tracking with increased anti-disturbance capability. Mammarella et al. [53] applied sample-based stochastic model predictive control (SMPC) and guidance algorithm for tracking control of UAV swarm. The applied algorithms dealt efficiently with noise and parametric uncertainty and guaranteed real-time tracking and performance with good stability.

Huang and Fie [54] introduced the global best path with a competitive approach to particle swarm optimization (GBPSO). This developed strategy improved the ability to search, avoided the local minimum, and provided the feasible optimal path with superior quality and speed. Ghazzai et al. [55] suggested applications of bandwidthhungry and delay-tolerant and exploited typical microwave (μ-Wave) and the highrate millimeter wave bands (mm-Wave) for trajectory optimization. Further, the research also implemented a hierarchical iterative approach. The dual-band increased the stopping locations and minimized the service time of multi-UAVs. Liu et al. [56] implemented distributed formation control algorithm with a fast model predictive control method and disturbance estimation method. This strategy was convenient for the formations of arbitrary, time-varying prescribed shapes and achieved a balanced configuration on a prescribed 2-directional (2D) or 3D shape.

Xuan-Mung et al. [57] used a robust saturated tracking backstepping controller (RAS-BSC) and Lyapunov theory. The researchers found that the proposed mechanisms provided the stability of the closed-loop system and bounded the tracking errors and extended state observer (ESO) errors. Moreover, it was rapid and robust in the uncertainties and gave a superior performance. Fabra et al. [58] suggested a Mission-based UAV Swarm Coordination Protocol (MUSCOP) for a swarm of UAVs. This study achieved swarm cohesion with a high degree under multiple conditions and allowed the least synchronization delays with low position offset errors. Causa et al. [59] employed a multi-global navigation satellite system (multi-GNSS) constellation approach and edge cost estimation method for path planning of multiple UAVs. These approaches decreased the computation time and entire mission time providing a rapid solution to the task assignment issue and planning for offline and in near real-time scenarios.

Brown and Anderson [60] applied the Quintic polynomials trajectory generation method, multi-objective particle swarm optimization (OMOPSO) and area search radar model to optimize the trajectories for the UAV swarm. This combination gave a maximum number of better trajectories, reduced the time to revisit and fuel consumption, and enhanced the detection probability. Mehiar et al. [61] developed Quantum Robot Darwinian particle swarm optimization (QRDPSO) for UAV flocks. This optimization algorithm provided a more stable, efficient, and quick optimal solution, avoided obstacles, and overcome communication constraints. Moreover, it reached the global best for search and rescue operations. Wang et al. [62] suggested a Leaderfollowing model, Routh–Hurwitz criterion, a consensus protocol, and a model predictive controller for multiple UAVs. The applied approaches predicted the changes in the leader's state, reduced the consensus achievement time, and kept the formation shape.

Altan [63] proposed metaheuristic optimization algorithms, Harris Hawks Optimization (HHO), and Particle Swarm Optimization (PSO) for UAV swarm. His suggested methods performed the best for multiple geometric paths and quickly determined the controller parameters. HHO outperformed, overcome the stabilization issues, and gave the least settling, peak time, and overshoot. Wang et al. [64] developed Neural Relational Inference (NRI) model along with a Mapping Table between the UAV swarm and the spring particles. The results of the developed method were able to improve the position detection performance. Moreover, it projected the motion in 3D space into a 2D plane and the designed algorithm predicted the trajectory and gave high accuracy. Rubí et al. [65] employed four PF algorithms namely, backstepping (BS) and feedback linearization (FL) algorithms, Non-Linear Guidance Law (NLGL) algorithm, and Carrot-Chasing (CC) geometric algorithms for UAV swarms. In comparing, the results of path following BS outperformed for yaw error and path distance and the CC algorithm needed fewer data and proved to be easily applicable for any path type. Selma et al. [66] used a hybrid controller, adaptive neuro-fuzzy inference system (ANFIS), and PSO algorithms for trajectory tracking of multiple UAVs. The results evaluated that the PSO algorithm adjusted automatically the ANFIS parameters, minimized tracking error by improving the controller quality, and gave a high performance.

Liu et al. [67] suggested a kinetic controller, distributed β-angle test (BAT)-based topology control algorithm, and Flying ad-hoc network (FANET) for UAV flocking. This mechanism could perform neighbor selection and reduce the communication overhead significantly. Madridano et al. [68] applied the 3D probabilistic roadmaps (PRM) algorithm, Robot Operating System (ROS) architecture, Mav-Link protocol, Pixhawk autopilot, and Hungarian method for trajectory planning in 3D. This combination generated optimal solutions using minimum time and lessened the computational time and the total traveling distance. Zhou et al. [69] analyzed the Hierarchical control framework with different SI algorithms. This analysis categorized the major technologies with trends, future research, and limitations. Wubben et al. [70] employed MUSCOP protocol and an emulation tool, Ardu-Sim, to provide resilience to multiple UAVs. This protocol handled the loss of leaders and backup leaders efficiently and introduced an ignorable flight time delay.

Selma et al. [71] applied an adaptive-network-based fuzzy inference system (ANFIS) and improved ant colony optimization (IACO) for controlling trajectory tracking tasks. This strategy proved its superior performance, reduced the mean

squared error (MSE) along with root mean squared error (RMSE) significantly, and allowed the UAVs to reach the desired trajectory in a minimum period. Altan and Hacıoğlu [72] used Newton–Euler method-based 3-axis gimbal system, the Hammerstein model, and the model predictive control (MPC) algorithm for target tracking. This mechanism tracked the target with stability and showed robustness even under external disturbances. Sanalitro et al. [73] suggested a Fly-Crane system with an optimization-based tuning method and an inner or outer loop approach. This system dealt with parametric uncertainties performed by rotating and translating trajectories, guaranteed stability, and enhanced the performance of H∞. Chen and Rho [74] introduced the SI technique with self-organizing maps (SOMs) based on requests from end-users (EUs). This technique allowed self-organization for UAV arrays and reconfiguration of the UAVs into hubs or terminals. Moreover, it shared information efficiently.

Qing et al. [75] applied improved ant colony optimization (ACO), minimum-snap algorithm, and zeroing control barrier function (ZCBF) for multiple swarms. The results evaluated that the proposed algorithms gave optimal results for decision-making in real-time. Moreover, it efficiently provided collision and avoidance-free trajectories. Miao et al. [76] proposed a multi-hop mobile relay system, the minimum secrecy energy efficiency (MSEE) maximization transmission scheme, and generated an algorithm using the block coordinate descent method (BCD), successive convex approximation (SCA) techniques, and Dinkelbach method for multiple UAVs. The results guaranteed the convergence and provided major improvements in energy efficiency and secrecy rate. Shao et al. [77] linked multi-segment strategy with improved particle swarm optimization-Gauss pseudo-spectral method (IPSO-GPM) for UAV swarms. The outcomes evaluated that the applied mechanisms increased obtained solution optimality, generated high-quality trajectories, and took minimum running time.

Gu et al. [78] suggested Network Integrated trajectory clustering (NIT) for determining subgroups of a flock of UAVs. This clustering showed a quick response and accuracy and proved to be effective, fault-tolerant, and stable in complex environments. Ling et al. [79] presented a planning algorithm; out-of-the-box trajectory plotting with multi-round Monte Carlo simulation for UAV swarms. This developed algorithm worked in noise and unstable communication and proved to be useful for cooperative swarm applications. Yao et al. [80] employed swarm intelligence and optimization algorithms for UAV swarms. The results showed that the proposed algorithm controlled the UAVs effectively improved the autonomy and inspection efficiency and minimized the cost of the inspection. Xia et al. [81] suggested multiagent reinforcement learning (MARL) with multi-UAV soft actor-critic (MUSAC) for the UAV swarm. The suggested mechanism allowed to make intelligent flight decisions, reduced the power consumption, enhanced the tracking success rates, and gave high performances for detection coverage.

Nnamani et al. [82] applied a grid-structured approach to the UAV swarm. The outcomes showed improvement in the secrecy rate of communications and physical layer security and evaluated the optimal radius of the eavesdropper's unknown location. Xu et al. [83] designed communication-aware centralized and decentralized controllers for UAV swarm. Their proposed controllers achieved high waypoint tracking accuracy. Between both controllers, the decentralized controller outperformed and maintained stability. Sharma et al. [84] studied multiple SI algorithms for path planning of UAV swarm. This analysis showed that PSO had low computational complexity, ACO possessed good scalability, and Firefly utilized a single operator for solution searching. Han et al. [85] employed a backscatter communication system

with the massive multiple-input multiple-output (MIMO) and Central limit theorem (CLT)-based approach to analyze the performance and optimize the trajectory. This combination performed well to detect parasite devices and separate parasite signals. Moreover, it reduced energy consumption and optimized trajectory planning.

Zhou et al. [86] used Multi-Target Tracking (MTT) system, an intelligent UAV swarm-based cooperative tracking algorithm, and a multi-objective Lyapunov optimization model. The results showed a reduction in the execution in the complexity and energy consumption with an improvement in the prediction accuracy of trajectory. Brown and Raj [87] applied reactive tracking and reactive tracking with predictive pre-positioning to study the effects of initial swarm formation. The tracking gave a superior performance.

Sastre et al. [88] applied collision-less swarm take-off heuristic (CSTH) with two improvements and Euclidean distance-based CSTH (ED-CSTH) algorithms to analyze the trajectory and batch generations. This study also used the ArduSim simulator and vertical take-off and landing (VTOL) techniques with Kuhn-Munkres Algorithm (KMA) for UAV swarms. The proposed method showed the computation time optimization, ensured safe distancing, and improved the time required for take-off. Whereas KMA proved to be the most reasonable choice for realistic conditions. Bansal et al. [89] proposed a scalable authentication-attestation protocol, SHOTS, with Physical Unclonable Functions (PUFs), Mao Boyd logic approach, and Christofides algorithm for UAV swarms. The authors suggested a lightweight authentication and attestation mechanism for UAV swarms that makes use of Physical Unclonable Functions (PUFs) to ensure physical security as well as the necessary trust in a lightweight manner.

## **6. Discussion**

The significance of multiple UAVs is expanding their cooperative operations and applications in many fields. Swarms are deployed in many environments such as uncertain, indoor, outdoor, traffic, and many others. Findings show that many challenges such as decision-making, control, path planning, communication, monitoring, tracking, targeting, collision, and obstacle avoidance may hinder the motion planning of a UAV swarm. Survey shows that different approaches are adopted in all the research addressing different challenges. Like mission planning architectures provide a complete operative, robust, scalable, and flexible framework. Many controllers whether linear or nonlinear, proves to be easily applicable, intuitive, robust, and provide optimality and global stability. Improved model predictive controllers ensure real-time monitoring and tracking of swarms. Moreover, they enhance the tracking accuracy, effectiveness, and superiority. Machine learning, 5G networking, and other technologies alleviate limiting factors for previous studies and enhance the efficiency of the swarm and commercial usage. Among all these evolving technologies in this chapter, swarm intelligence is determined as an appropriate solution for the reliable and efficient deployment of swarms. Moreover, it enables self-organization, reconfiguration, control, efficient sharing of information, reduction in inspection costs, and improvement in autonomy.

Besides many mentioned advantages of the swarm and technological development, many important and interesting limitations exist that can hinder the swarm performance. Among these restrictions, the manufacturing cost of the large-scale swarm is still high. Existing loads are huge, expensive, and mostly not appropriate for

pursuing high performances. Hence, the lightweight and low-cost loads and platforms are essential for swarm formation. Battery capacity for aerial mission completion is of much significance. Long-lasting batteries are essential for continuous tasks. However, the capacity of the battery can be enhanced by increasing the UAV's weight. And this weight increment will also require an increment in energy consumption. To provide a proper battery solution such systems are essential that can easily and rapidly replace the depleted battery with the supplementary one and are capable to charge other batteries. Another limitation is the privacy protection protocol. This is essential for deploying swarm in sensitive locations safely. Otherwise, it can lead to national security issues.
