A Review of Classification and Application of Machine Learning in Drone Technology

Introduction

Due to their ability to carry out tasks that were previously difficult or impossible for humans, drones, also known as unmanned aerial vehicles (UAVs), have witnessed a surge in popularity in recent years. Drones are being used in a wide range of applications, including delivery, agriculture, and infrastructure inspection, as well as military and surveillance operations. However, the classification and management of drones have also been complicated by the rapid advancement of drone technology. We present the ways in which machine learning (ML) algorithms can be used to improve drone technology in various applications and the classification of drones according to their size, shape, and functionality. In addition to shedding light on the advantages and drawbacks of employing machine learning in drone technology, recent cutting-edge methods and approaches as well as potential new developments are highlighted [1].

Size, shape, and functionality are few of the factors that are used to classify drones. Drones can be categorized according to their size, which can range from nano drones, which are smaller than a human hand, to large drones that are capable of carrying heavy payloads. Shape is another way to categorize drones, which can range from conventional quadcopters to novel designs like the X-wing and VTOL. Lastly, drones are also categorized according to their functions, such as delivery, agriculture, surveillance, and search and rescue [2].

New opportunities for enhancing drone technology have emerged as a result of the rapid development of ML algorithms. One application of ML is object recognition, in which drones with cameras can identify people or objects on the ground. Drones can now perform tasks like traffic monitoring and animal tracking in the wild thanks to ML algorithms that can be trained to recognize and classify specific objects in real time. Autonomous navigation, in which drones can fly without human intervention, is another application of ML. Autonomous navigation is especially useful for tasks like inspecting infrastructure or surveying large areas of land. Data from the drone’s sensors can be used to train ML algorithms that can decide where to fly, how to avoid obstacles, and when to return to the base. Additionally, drone delivery, which is becoming an increasingly popular application for drones, is being improved through the use of ML. Using ML algorithms, delivery drones can optimize their flight paths and delivery schedules, shortening delivery times and costs. During the delivery process, ML can also be used to identify and avoid obstacles, ensuring that packages are delivered safely and effectively [3].

There are several potential future developments that ML could make possible as drone technology continues to advance. The creation of swarming drones, in which multiple drones collaborate to complete a task, is one area of study. Swarming drones may be used for disaster responses, surveillance, and even temporary communication networks in faraway places. The creation of drones that are capable of operating in complex environments, such as indoors or in urban areas, is another area of research. Analyzing the environment and making decisions about navigation and avoid obstacles could be done with ML algorithms [4].

In this review, we examined how size, shape, and functionality can be used to classify drones and how ML algorithms can be used to improve drone technology in various applications as well as the most recent and cutting-edge methods, strategies and potential future developments. We anticipate that this field will continue to expand and innovate in the coming years because ML is an essential tool for improving drone technology. For those who are interested in the relationship between drones and machine learning, this survey is a comprehensive resource.

Scope of the review

The integration of ML in drone technology has rapidly evolved, necessitating a comprehensive analysis of existing methods, applications, and challenges. This review aims to explore the classification of drones based on size, shape, and functionality while providing an in-depth assessment of ML-driven advancements in navigation, energy management, mapping, control, and imaging applications.

Classification of UAVs

Unmanned Aerial Vehicles (UAVs) are broadly classified based on their size, shape, propulsion system, and functionality. The standard classification used by military, regulatory, and research institutions groups UAVs into five categories based on their weight, altitude, and operational range, as shown in Table 1.

3.2.

Fixed-wing UAVs

Drone aircraft with a fixed, non-rotating wing structure are referred to as fixed-wing drones, aerial drones, and UAVs. Fixed-wing drones generate lift through the aerodynamics of their wings, whereas, rotary-wing drones rely on rotating blades to generate lift. They are great for surveillance, mapping, and delivery because of their ability to fly over great distances quickly and remain airborne for a long time.

Single or multiple engines are used to propel fixed-wing drones, which typically have a longer wingspan than rotary-wing drones. Due to their reliance on aerodynamic forces and lack of vertical takeoff and landing capabilities, they are typically more challenging to operate and control than rotary-wing drones. However, the advantages of longer flight times, faster speeds, and more effective power use outweigh this drawback [6].

Fixed-wing drones are further classified into:

• Manned Fixed-Wing UAVs: Piloted onboard.

• Unmanned Fixed-Wing UAVs: Remotely controlled or autonomous.

A prototype image of a fixed-wing drone is shown in Figure 1.

3.3.

Multirotor UAVs

Multirotor drones, commonly known as quadcopters, hexacopters, or octocopters, use multiple rotors for both lift and propulsion. Their ability to hover and maneuver precisely makes them highly suitable for aerial photography, search-and-rescue, and inspection tasks [7]. An example of a multirotor drone is shown in Figure 2.

Multirotor drones are known for:

• Hovering Stability: Ideal for aerial photography and videography.

• Maneuverability: Can fly in any direction and are easy to control.

• Variety of Configurations: Common configurations include quadcopters, hexacopters, and octocopters.

3.4.

Flapping-wing UAVs

A flapping-wing drone, also known as an ornithopter, mimics the flight of birds by flapping its wings, as shown in Figure 3. Unlike traditional drones, which rely on rotors, these UAVs generate lift and propulsion through wing motion.

Advantages of Flapping-Wing UAVs:

• Energy Efficiency – Uses less energy compared to conventional drones.

• Low Noise – Quieter operation, ideal for wildlife monitoring.

• Enhanced Stability – Allows for intricate flight patterns.

However, they also have limitations, such as small payload capacity and the need for complex control systems [10].

3.5.

Tilt-rotor UAVs

Tilt-rotor drones combine the capabilities of helicopters and fixed-wing aircraft by tilting their rotors to transition between hover and forward flight, as shown in Figure 4.

Key Advantages:

• Vertical take-off and landing (VTOL) Capability – Can take off and land like a helicopter.

• Longer Range – When in fixed-wing mode, operates efficiently over long distances.

• Versatility – Suitable for both urban and remote environments.

Tilt-rotor UAVs require advanced control systems to stabilize their dual flight modes, making them more complex than traditional drones [12].

The classification of UAVs is essential to understanding their diverse applications and technological advancements. While industry-standard classification (Groups 1–5) helps categorize UAVs based on their operational capabilities, classification by design (fixed-wing, multirotor, flapping-wing, tilt-rotor) further refines their functional purposes. With ongoing advancements in AI and autonomous flight systems, UAVs are expected to evolve, improving efficiency and versatility in both civilian and military applications.

Machine learning techniques in drone energy management

A deep reinforcement learning (DRL) strategy for coordinated multi-agent UAV systems has been presented in [13]. To achieve energy awareness in UAV-based big-data platforms, which are used for data collection and analysis in large-scale environments, the authors proposed a system that employs DRL. The system was intended to reduce the amount of energy used by multiple UAVs for data collection and analysis. To achieve energy efficiency, the actions of multiple UAVs are coordinated using the DRL algorithm. The findings demonstrate that the proposed system is capable of maintaining the performance of data collection and analysis tasks while simultaneously reducing energy consumption. The limitations of using DRL in UAV-based big-data platforms, as well as the need for additional research and development in this area have also been discussed by the authors. Overall, the study demonstrates the importance of continuing progress in this field and the potential of DRL to enhance energy efficiency in big-data platforms based on UAVs.

A multi-UAV charging system that makes use of orchestrated scheduling and multi-agent deep reinforcement learning (MARL) was presented in [14]. To provide UAVs with charging that is both efficient and effective, the authors suggest a system that combines charging stations, UAVs, and a cloud platform. The charging of the UAVs is orchestrated by the system using a scheduling algorithm that takes into account the battery levels, flight plans, and charging availability of the UAVs. After that, the scheduling results were used by the MARL algorithm to improve efficiency and shorten the charging time by optimizing the charging procedure. The study demonstrated the potential of orchestrated scheduling and MARL for improving the efficiency and effectiveness of UAV charging systems by demonstrating that the proposed system outperformed traditional charging methods in terms of charging time and energy consumption. The authors also discussed the problems with these kinds of systems and the future steps, like how much research and testing in real-world situations would be required. Overall, the study demonstrated the potential of cloud-assisted systems and the significance of integrated approaches for enhancing the performance of UAV charging systems.

A study [15] described a DRL strategy for allocating frequency bands and designing 3D UAV trajectories to ensure fair and energy-efficient communication. By optimizing the UAV’s flight path and frequency band allocation, the authors hoped to boost the efficiency of UAV communication systems. Based on real-time communication requirements and energy consumption, the proposed strategy made use of a DRL algorithm to learn and optimize the UAV’s trajectory and frequency band allocation. The study demonstrated that DRL has the potential to improve the performance of UAV communication systems by outperforming conventional methods in terms of energy efficiency and communication fairness. The authors also discussed problems with these kinds of systems and the future needs, like how much research and testing in real-world situations would be needed. Overall, the study highlighted the potential of DRL in this context and the significance of optimizing the trajectory and frequency band allocation of the UAV for energy-efficient and fair communication.

A deep learning (DL) approach to predict drone energy consumption was discussed in [16]. The authors modelled the drones’ energy consumption based on flight time, altitude, speed, payload, and weather conditions using a deep neural network. The findings demonstrated that the proposed method applied to real-world scenarios and could accurately predict drones’ energy consumption.

Machine learning techniques in drone navigation

An investigation into the use of reinforcement learning (RL) for the autonomous navigation UAVs has been presented in [17–20]. For UAV navigation in unknown environments, where a UAV must learn to fly from one location to another while avoiding obstacles, the authors proposed an RL-based method. The authors compared their proposed method to more conventional approaches to navigation and evaluated its efficacy in a simulation environment. According to the findings, the RL-based approach performed better than conventional methods in terms of speed, accuracy, and efficiency. The authors concluded that RL can be a promising method for autonomous UAV navigation and recommended that future research concentrate on adapting the strategy to more realistic environments.

A study on using DRL for autonomous drone navigation has been presented in [21–24]. For drone navigation in complex environments where the drone must use sensor data to avoid obstacles and reach its destination, the authors proposed a DRL-based strategy. The authors compared their proposed method to more conventional approaches for navigation and evaluated its efficacy in a simulation environment. The findings demonstrated that DRL-based approach can deal with complex environments with multiple obstacles effectively and outperforms conventional methods in terms of efficiency, speed, and accuracy. The authors concluded that DRL is a promising method for autonomous drone navigation and recommended that additional sensor data and real-world testing be the primary focus of subsequent research.

Presently, several studies [25–30] provide an overview of the state of DL for drone navigation research. The authors, Thomas Lee, Susan Mckeever, and Jane Courtney, examined various methods needed for drones to navigate on their own and determine the problems that still need to be solved. They looked at how DL techniques like RL, CNN, and recurrent neural networks (RNNs) can be used in drone navigation. The authors also discussed how obstacle avoidance, flight planning, and landing are affected by DL in drone navigation. They concluded by highlighting the potential for profound comprehension to revolutionize drone navigation and provided research avenues for the future.

A novel method for using DRL for drone navigation and obstacle avoidance was proposed in several studies [31–37]. Using a deep neural network to make decisions about what to do based on observations of the environment, the authors created a system that lets a drone navigate through complex environments. The neural network was then trained by the system using RL to avoid obstacles and reach a desired location. In a simulation, the authors evaluated their strategy and demonstrated that the drone can successfully navigate and avoid obstacles in difficult environments. The findings indicate that real-world drone navigation and obstacle avoidance systems could benefit from the use of DRL.

In [38], a novel strategy for navigating UAVs was proposed using Deep Reinforcement Learning (DRL) and massive MIMO technology. These two methods have been combined by the authors to create a system that can avoid obstacles while navigating UAVs through complex environments. A deep neural network was used in the system, which was trained through RL to decide what needed to be done based on what it sees around it. The vast MIMO technology was utilized to enhance the quality of communication between the control system and the UAV. In simulations, the authors evaluated how the proposed method performed and demonstrated that, in terms of stability and accuracy for UAV navigation, it performed better than conventional approaches. The findings point to the possibility of putting massive MIMO technology and DRL together in UAV navigation systems.

One study [39] suggested a DRL strategy for navigating high-dynamic environments with UAVs. A system that uses a deep neural network that has been trained through RL to make decisions about what to do based on what it sees around it was created by the authors. The system was built to deal with the problems of highly dynamic environments, such as sudden changes in the environment or obstacles in the way of the UAV. In simulations, the authors evaluated how well their method performed and demonstrated that, in terms of UAV navigation accuracy and stability in highly dynamic environments, it performed better than conventional approaches. The outcomes demonstrated the usefulness of DRL for navigating UAVs in difficult environments.

The application of DL to autonomous indoor drone racing was investigated in a study [23]. Using perception, guidance, and navigation systems based on DL algorithms, the authors proposed a system that can effectively guide a drone through an indoor racecourse. A DL-based approach to overcome various obstacles encountered in indoor drone racing was presented and the results showed that the system could successfully navigate the racecourse and maintain a stable flight trajectory. The system was trained using a combination of visual and sensory data from the drone. The study’s results show that DL has the potential to improve the capabilities of autonomous indoor drone racing.

A method for simultaneous navigation and radio mapping for UAVs connected to cellular networks of UAVs was presented in [40]. To address the issue of radio mapping and navigation in a connected to cellular networks, the authors suggested using DRL. The UAVs can fly to areas with strong signals while avoiding areas with weak signals thanks to the DRL algorithm’s training to make decisions based on the quality of the cellular signal. In terms of radio mapping efficiency and navigation accuracy, the study’s findings demonstrated that the proposed DRL strategy performed better than conventional methods. The results show that cellular-connected UAVs can use DRL for simultaneous radio mapping and navigation.

An RL strategy for autonomous navigation of UAVs was presented in [41]. To boost both the speed and accuracy of the navigation process, the authors suggested incorporating function approximation into the RL algorithm. Based on the UAV’s current state, the function approximation was used to estimate the expected reward for each action. The study showed that, in terms of speed and accuracy for navigation, the proposed RL approach with function approximation performed better than conventional RL methods. The results show that autonomous UAV navigation with RL and function approximation is feasible.

A DRL strategy for distributed energy-efficient multi-UAV navigation for long-term communication coverage was presented in [42]. The authors attempted to reduce the amount of energy each UAV used while simultaneously increasing the network’s communication coverage. The proposed DRL algorithm was taught to decide how to navigate each UAV, taking into account the network’s energy efficiency and communication coverage. In terms of both communication coverage and energy efficiency, the study demonstrated that the proposed DRL approach performed better than conventional methods. The results show that DRL can be used to provide long-term communication coverage for distributed energy-efficient multi-UAV navigation.

Machine learning techniques in drone mappings

In [43], a method for mapping soil pollution was presented using drone image recognition and machine learning. The authors suggested taking pictures of an arsenic-contaminated agricultural field with a camera on a drone. ML algorithms were used to analyze the images and figure out which parts of the field were contaminated with arsenic. A dataset of images that have been labeled as either contaminated or uncontaminated was used to train the ML algorithm. The findings demonstrated that the proposed method was successful in accurately locating arsenic-contaminated fields. The proposed method has the potential to boost soil pollution mapping’s efficiency and accuracy, which are crucial for ensuring the safety of agricultural products and human health.

A study [44] compared three approaches for using UAV images to map vegetation on small island ecosystems. To evaluate the precision and efficacy of each method, the authors compared ML, visual interpretation, and pixel classification approaches. The outcomes demonstrated that the ML approach performed better than the other two as it achieved high accuracy with lower effort. The dataset of labeled images used to train the ML algorithm could classify vegetation types based on patterns in the images. The proposed method has the potential to make vegetation mapping on small island ecosystems more effective and accurate, which is important for ecological and conservation research.

Machine learning techniques in drone design

A new approach to design low-Reynolds-number airfoils using DL-based tailored airfoil modes was proposed [45]. The authors suggested using a DL algorithm to learn a low dimensional representation of the shape of an airfoil. This representation could then be used to create new airfoil shapes that are optimized for particular characteristics of aerodynamic performance.The low-Reynolds-number airfoils that are frequently found in small UAVs and micro air vehicles can be designed using the proposed approach. The findings demonstrated that the proposed approach can design airfoils with improved aerodynamic performance when compared to other approaches, achieving an improvement of up to 40% in the lift-to-drag ratio for certain design specifications. Small UAVs and MAVs, which are increasingly used in environmental monitoring, search and rescue, and precision agriculture, could benefit from the proposed method’s increased efficiency and performance.

A method for using DL to create a multi-fidelity surrogate model for aerodynamic design optimization was presented in [46]. A more accurate and effective surrogate model for the design optimization process was created by combining low-fidelity models with high-fidelity computational fluid dynamics (CFD) simulations in the proposed approach. From the combined data set, DL algorithm was used to learn the mapping between the input parameters and the output response. A UAV wing’s aerodynamic design was optimized using the proposed approach. The findings demonstrated that the proposed approach was effective in reducing the number of high-fidelity CFD simulations required by 25% and increasing overall design performance by 10%, both of which contributed to an increase in the process’s efficiency and accuracy in aerodynamic design optimization. Beyond UAVs, the proposed method has the potential to improve the design process for automotive and aerospace engineering, among other engineering fields.

A strategy for designing the trajectories and access control of aerial drones to optimize communication performance was proposed in [47]. The authors suggested coordinating the drones’ trajectories and access control by employing a MARL algorithm. By optimizing the drones’ position and movement at the ground base station, the proposed method aimed to improve communication performance. A scenario in which multiple drones are utilized to provide communication coverage in a disaster area was the subject of the proposed approach. The findings demonstrated that the proposed approach was successful in increasing communication coverage and decreasing communication delay, with an improvement in communication performance of up to 60% when compared to previous approaches. Communication systems for disaster response and other crucial applications could benefit from the proposed approach’s increased effectiveness and dependability.

The application of ML methods to aerodynamic shape optimization was discussed in [48]. Aerodynamic design optimization, which can be challenging due to the complexity of the underlying physics and the large design spaces involved, was highlighted by the authors as requiring precise and effective methods. Neural networks, genetic algorithms, and other optimization techniques were reviewed for the application of ML to aerodynamic shape optimization. The authors highlighted the potential of ML to enhance the accuracy and efficacy of aerodynamic design optimization and discussed the advantages and disadvantages of these methods. Several research directions for the future were identified, such as the creation of more sophisticated ML algorithms and its integration with other methods of optimization. The authors argued that the advancement of ML methods for aerodynamic shape optimization has the potential to revolutionize the field and make it possible to design aircraft that are effective and better for the environment.

The use of neural networks in the design of UAVs for the fitting and classification of CFD data was proposed in a study. The authors pointed out that the traditional method of designing UAVs required a lengthy process of trial and error, which can be significantly sped up with the help of ML methods. Neural networks were used in the proposed method to predict the CFD results for a specific UAV design and classify it according to various performance metrics. The collection of CFD data, the creation of a neural network model, and the utilization of the model to optimize the design of a UAV were the components of the method presented by the authors for putting the proposed strategy into practice. The authors presented the outcomes of several experiments, demonstrating that the proposed strategy was effective in reducing the amount of time required for design optimization and improving the performance of unmanned aerial vehicles. The use of RL to improve the design of UAVs in dynamic environments and the incorporation of additional performance metrics into the classification process are two potential areas for future research.

Machine learning techniques in drone control

In [49], the authors presented a novel strategy for maximizing communication coverage while minimizing energy consumption by optimizing the flight path of UAVs. To accomplish this, the authors suggested using DRL, which involved training a neural network in a simulation environment to learn the best policy for UAV control. The authors proposed a novel reward function that takes into account the fairness and quality of the UAV’s communication coverage. To prevent an excessive concentration of resources in particular locations, the reward function was intended to encourage the UAV to prioritize areas with poor coverage while avoiding areas with good coverage. The authors presented the outcomes of experiments that were carried out in a simulated setting. These results showed that the proposed method was capable of increasing communication coverage while also ensuring that coverage was distributed fairly. The authors also determined how well the proposed method worked in different situations, such as when there are different demands on communication and the environment is different. The proposed method has applications in different areas, like disaster response, public safety, and military operations.

A system for social distancing monitoring in public places that makes use of a DL algorithm was proposed in [50]. In a public setting, the system detected individuals and their distances using a camera mounted on a drone. Two parts made up the proposed DL algorithm: estimation of distance and object detection. The You Only Look Once (YOLO) algorithm was used for object detection portion, and SD (Single Shot MultiBox Detector) algorithm was used for distance estimation. Multiple people could be tracked by the proposed system, which can be used in real time to make sure social distance rules are followed. The system can be used to effectively maintain social distance in public places and in identifying individuals and their distances.

A study [51] involved analyzing drone data using ML methods. The three stages of the proposed framework were: data processing, data analysis, and data acquisition. The collection of evidence from drones using a variety of tools and methods, including digital forensics tools and a physical examination of the drone, was a part of the data acquisition phase. Preparing and extracting data from the acquired evidence was a part of the data processing stage. ML methods were applied to the extracted data during the data analysis phase to find patterns, anomalies, and potential evidence related to the incident.

A case study is also included in the study to elucidate how well the proposed framework works. The crash of a drone that caused property damage is the subject of the case study. The collected evidence was analyzed using ML methods and the proposed framework was applied to it. The findings of the analysis provided valuable evidence for the investigation and revealed the incident’s likely cause.

In general, the proposed forensic framework and ML methods provide an efficient and effective method for investigating drone-related incidents.

For controlling UAVs [52] in a variety of tasks, including navigation, tracking, and obstacle avoidance, the various RL and deep learning algorithms were evaluated by comparing the algorithms based on accuracy, speed, and generalizability using a simulated environment and various metrics. Q-learning, SARSA, and ActorCritic are examples of RL algorithms tested, while CNNs and DBNs are examples of deep learning models.

While the speed of the RL algorithms was superior, the accuracy and generalization of the DL models were generally superior to those of the RL algorithms. However, each algorithm performed differently depending on the task and environment at hand. The authors also pointed out the possibility of enhancing the overall performance of UAV control by combining RL and DL.

Overall, the study demonstrated the control potential of DL and RL algorithms for UAVs and emphasized the need for additional research to tailor these methods to specific tasks and environments.

A method for controlling a drone in virtual reality using ML and MEMS sensor technology was proposed in [53]. To predict the drone’s motion, the authors used an IMU (Inertial Measurement Unit) to capture the drone’s motion in three-dimensional space. The authors discovered that the ML algorithm could accurately predict the motion of the drone, which could be utilized for virtual reality drone control. They posited how this technology could be used for training and simulation, as well as for making autonomous drones. Overall, the study showed that it is possible to control drones in virtual reality with MEMS sensor technology and ML, which could have an impact on the development of more advanced drone technologies.

Another study [54] utilized a deep DL-based approach to use RF signals in order to find and identify drones. To train and evaluate their DL model, the authors presented a dataset of RF signals gathered from various drone models. Two main steps in their proposed method were: classification and detection. A deep CNN was used in the detection step to find a drone signal in the RF data. An RNN was used in the classification step to divide the detected signals into various drone models. The collected dataset served as the basis for the authors’ evaluation of the proposed method, which yielded encouraging results with high detection and classification accuracies. Their method could be used to identify and detect drones in a variety of settings, like safety and security.

Using ML models, the authors [55] proposed a framework for the autonomous detection of malicious events in drone networks. Analyzing the network traffic data and identifying abnormal traffic patterns that could indicate an attack or malicious event, the authors made use of the power of AI, particularly DL techniques. The experimental results showed that the framework was effective at accurately detecting and classifying a variety of malicious events, including reconnaissance attacks, denial-of-service attacks, and malware infections, on a custom drone network dataset. By making it possible to detect malicious activities in a timely and accurate manner, the proposed framework can help to improve the security of drone networks, preventing potential damage and maintaining the network’s integrity.

A cooperative multi-agent DRL framework was proposed in [56] to achieve effective and reliable surveillance through autonomous multi-UAV control. By allowing each agent to learn from and work with other agents to improve the global objective function, the proposed framework addressed the difficulties of autonomous multi-UAV systems. The four main components of the proposed framework were: (1) observation extraction module; (2) communication module; (3) action selection module; and (4) learning and cooperation module. The experimental results of the proposed method tested in a simulated environment demonstrated that, while maintaining a robust and dependable surveillance system, it was capable of successfully detecting and tracking moving targets with a high success rate. The proposed method looks promising and could be used in real-world situations like disaster management, search and rescue, and surveillance.

The authors of the study [57] proposed a circulated calculation for improving a PID regulator for a heterogeneous group of UAVs utilizing AI. The calculation intended to limit the deviation of the genuine direction of the UAVs from the ideal direction. The proposed approach depended on the utilization of a versatile pundit plan strategy, which joins a pundit brain network that gauges the expense capability with an activity network that produces the control activities. The pundit network is based on internet utilizing the information produced by the UAVs during the flight, while the activity network is refreshed utilizing the approach inclination strategy. The creators showed that the proposed calculation outflanks conventional strategies and accomplished better intermingling.

For obstacle avoidance in UAVs with limited environmental knowledge, a study proposed a memory-based DRL strategy. The proposed approach [58] stored the current state of the UAV and its environment in memory by encoding it using an RNN. The decisions regarding the UAV’s trajectory to avoid obstacles were then made using the stored information.

A DRL algorithm known as proximal policy optimization (PPO), which optimizes the UAV’s policy for obstacle avoidance, was used to train the RNN. In a simulation, the proposed strategy was tested against other obstacle avoidance strategies that are based on rules and DRLs.

According to the findings, the success rate and path efficiency of the proposed memory-based DRL method were superior to those of the other approaches. The authors concluded that the memory-based strategy is a promising approach for UAVs with limited environmental knowledge to avoid obstacles.

Applications of machine learning techniques in drone Technology

Discussion

Conclusion

In recent years, the combination of drone technology with machine learning has become an important focus in research and development. This review provides an in-depth understanding of the current use of machine learning in drones and its potential applications. This study covers several machine learning methods, including reinforcement, supervised, and unsupervised learning, highlighting their strengths and weaknesses. For example, supervised learning methods like deep neural networks have significantly improved tasks such as object detection and tracking. In contrast, unsupervised learning has proven useful for identifying unusual patterns and clustering data. Reinforcement learning has enabled drones to perform tasks like autonomous navigation and mission planning more effectively. Machine learning plays a critical role in tasks like object detection, tracking, mapping, and classification. These capabilities have been applied across agriculture, wildlife management, and in search and rescue operations. Machine learning improves terrain and vegetation mapping, helping drones collect and analyze high quality data more efficiently. One of the biggest advantages of using machine learning in drones is its ability to handle complex tasks quickly and accurately. By processing large amount of data in real time, drones can make smarter decisions and adapt to environmental changes. This is particularly valuable for autonomous navigation, where drones need to respond instantly to new challenges. However, the use of machine learning in drones comes with challenges. High-quality training data is essential for accurate algorithms, but collecting such data is an arduous task. Drones also have limited processing power and memory, making it hard to run computationally demanding algorithms. Additionally, many machine learning models act like “black boxes,” making it unclear how they reach certain decisions, which can be a limitation for critical applications. Despite these obstacles, this study concludes that machine learning has great potential to improve drone capabilities, enabling them to perform a wide range of tasks with higher precision and efficiency. Industries like agriculture, forestry, and environmental monitoring benefit significantly from these advancements. In summary, this study is a valuable resource for researchers and practitioners. It highlights the challenges that need to be addressed and emphasizes the importance of continued research and development to unlock the full potential of machine learning in drones. With further innovations, machine learning will continue to enhance the abilities of drones and their applications across various industries.

Author’s contribution

Haque, Ahshanul: Conceptualization, Investigation, Formal analysis, Writing – original draft, Visualization; Chowdhury, Md Naseef Ur Rahman: Investigation, Formal analysis, Writing – review & editing; Hassanalian, Mostafa: Writing – review & editing, Supervision.

Funding

This research did not receive external funding from any agencies.

Ethical statement

Not Applicable.

Data availability statement

Source data is not available for this article.

Conflict of Interest

The authors declare no conflict of interest.