1. Introduction

2. Classification of Drones

2.4. Tilt-Rotor

A type of drone known as a tilt-rotor drone can tilt its rotors to switch between helicopter and fixed-wing flight modes. In fixed-wing mode, the rotors are horizontal and provide forward propulsion like a conventional aircraft, whereas, in helicopter mode, the rotors are vertical and provide lift for hovering. Tilt-rotor drones can now combine the speed and efficiency of a fixed-wing aircraft with the vertical takeoff and landing (VTOL) capability of a helicopter.

Most tilt-rotor drones have two or more rotors that are attached to articulated arms. Using a mechanism that is controlled by the onboard computer of the drone, the rotors can be tilted between the horizontal and vertical positions. As a result, the drone is able to switch between helicopter and fixed-wing modes, allowing it to perform a wide range of tasks.

The ability of tilt-rotor drones to operate in a variety of environments and conditions is one of their primary advantages. They are ideal for tasks that require precise maneuvering and stability, such as aerial photography and videography, inspection and surveying and search and rescue operations because they can hover in place and perform vertical takeoff and landing in helicopter mode. They are able to fly effectively and at high speeds when in fixed-wing mode, making them ideal for long-distance transportation and scientific research.

The plan and activity of slant rotor drones require progressed control frameworks and sensors to guarantee a steady and controlled flight. To control the tilt of the rotors and detect changes in the orientation and movements of the drone, the control systems make use of a combination of accelerometers, gyroscopes, and barometers.

In conclusion, tilt-rotor drones are a novel option that provides a one-of-a-kind combination of advantages and capabilities. They are ideal for a wide range of applications and environments because they combine the best aspects of helicopter and fixed-wing flight. Tilt rotor drones are likely to gain in popularity and versatility as a result of technological advancements, providing novel opportunities and solutions to numerous social problems.

Figure 1. Examples of Drones, (a) Fixed Wing, (b) Multi-Rotors, (c) Flapping Wings, (d) Tilt-Rotor drones [8,9,10,11].

Figure 1. Examples of Drones, (a) Fixed Wing, (b) Multi-Rotors, (c) Flapping Wings, (d) Tilt-Rotor drones [8,9,10,11].

3. Machine Learning Techniques in drone design

Table 1. Comparison table with different ML approaches in drone design.

4. Machine Learning Techniques in Drone Control

Figure 2. Reinforcement learning architecture in Drone control.

Figure 2. Reinforcement learning architecture in Drone control.

Table 2. Comparison table with different ML approaches in drone control.

5. Machine Learning Techniques in Drone Energy Management

Table 3. Comparison table with different ML approaches in drone energy management.

6. Machine Learning Techniques in Drone Navigation

Table 4. Comparison table with different ML approaches in drone navigation.

7. Machine Learning Techniques in Drone Mappings

Table 5. Comparison table with different ML approaches in drone mappings.

8. Application of Machine Learning Techniques

8.1. Search for an Object

The research paper [51] describes a reinforcement learning strategy for using unmanned aerial vehicles (UAVs) to locate ground objects. An autonomous navigation and object detection system for a UAV has been developed by the authors. A deep neural network that has been trained through reinforcement learning is used by the system to decide what to do based on what it sees around it. In a simulation, the authors evaluate their strategy and demonstrate that the unmanned aerial vehicle can precisely locate ground objects while avoiding obstacles in its path. In real-world applications, the findings demonstrate the potential of reinforcement learning for autonomous UAV navigation and object localization.

A deep learning strategy for human target search and detection using unmanned aerial vehicles (UAVs) is proposed in [52] A system designed by the authors makes it possible for an unmanned aerial vehicle (UAV) to search for and identify human targets in challenging environments. A large dataset of human targets is used to train the system’s deep neural network, which is used to make decisions about what to do based on environmental observations. In simulations, the authors evaluate how well their method works and demonstrate that the unmanned aerial vehicle can accurately identify human targets while avoiding obstacles in its path. In real-world UAV applications, the outcomes demonstrate the potential of deep learning for human target search and detection.

A deep reinforcement learning strategy for object tracking in drone images is proposed in [53] A method devised by the authors enables a drone to simultaneously take pictures and track objects in real time. Based on observations of the environment, including the captured images, the system makes decisions about what to do with a deep neural network that has been trained through reinforcement learning. In simulations, the authors evaluate their strategy and demonstrate that the system can accurately track objects in real-time while avoiding obstacles in the path of the drone. The outcomes demonstrate the practical application potential of deep reinforcement learning for object tracking in drone images.

A study on the potential of deep reinforcement learning for vision-based autonomous drone navigation with a mission in real-world scenarios is presented in [54] For drone navigation, the authors propose a method based on deep reinforcement learning that decides what to do based on visual observations of the environment. They talk about the difficulties and things to think about when putting their method into practice in the real world and evaluate how well it works in simulations. The findings emphasize the need for additional research and development to address the difficulties of putting deep reinforcement learning to use in the real world for mission-related vision-based autonomous drone navigation.

Table 6. Comparison table with different ML applications with the search for an object.

Table 6. Comparison table with different ML applications with the search for an object.

Study	Objective	Methodology
[51]	Autonomous UAV navigation and object localization	RL-based decision making using a Deep Neural Network
[52]	Human target search and detection using UAVs	DL-based decision-making using a Deep NN trained on a large dataset of human targets
[53]	Object tracking in drone images	Rl-based decision-making using a Deep NN
[54]	Vision-based autonomous drone navigation in real-world scenarios	RL-based decision making using visual observations of the environment
[55]	Human detection in real-time UAV applications	Deep NN-based YOLO-v2 object detection algorithm
[56]	Use of drones for search and rescue in natural disasters	Suggests using drones and cutting-edge technologies like computer vision DL to enhance drone capabilities in search and rescue operations

A deep learning strategy for human detection in the real-time unmanned aerial vehicle (UAV) applications is proposed in [55] The authors detect humans in UAV images by employing the deep neural network-based YOLO-v2 (You Only Look Once version 2) object detection algorithm. The system’s speed and effectiveness make it ideal for use in real-time UAV applications. On a dataset of UAV images, the authors evaluate their method and demonstrate that the system can accurately identify human targets while maintaining a high processing speed. The outcomes demonstrate that real-time human detection in UAV applications using YOLO-v2 and deep learning is feasible.

The use of drones for search and rescue in natural disasters is suggested in [56] The authors talk about the challenges and benefits of using drones in these kinds of situations, like being able to quickly survey large areas and get into hard-to-reach places. In addition, they suggest enhancing the capabilities of drones in search and rescue operations by utilizing cutting-edge technologies like computer vision and deep learning. The authors highlight the significance of collaboration between stakeholders, such as government agencies, non-profit organizations, and technology companies, in order to effectively utilize drones for search and rescue operations and provide examples of successful drone surveillance during natural disasters. The findings of this study emphasize the significance of further research and development in this field as well as the potential of drones as a useful tool for responding to natural disasters.

8.2. Autonomous Landing

A study on how to use deep reinforcement learning for the autonomous landing of unmanned aerial vehicles (UAVs) is presented in [57] An end-to-end deep reinforcement learning strategy for landing UAVs that can handle the dynamic and complex nature of landing tasks is proposed by the authors. In a simulation, they evaluate their strategy and demonstrate that the UAV is capable of landing accurately and successfully while avoiding obstacles in its path. The findings emphasize the significance of continuing research in this field and demonstrate the potential of deep reinforcement learning for autonomous UAV landing. The authors also talk about the difficulties that come with putting the ideas into practice in the real world, the things to keep in mind, and the need for more work to fix these problems. In general, the study presents a promising deep reinforcement learning-based strategy for autonomous UAV landing.

Table 7. Comparison table with different ML applications with drone Autonomous landing.

Table 7. Comparison table with different ML applications with drone Autonomous landing.

Study	Main Focus	Proposed Method	Findings
[57]	Autonomous landing of UAVs	End-to-end DRL	Successful and accurate landing with obstacle avoidance
[58]	Autonomous landing of UAVs on a moving platform	DRL algorithm	More stable and accurate landing than conventional strategies
[59]	Autonomous landing of multi-copter UAVs	Vision-based RL strategy	High-precision autonomous landing even in difficult conditions
[60]	Autonomous tracking and landing of UAVs	DRL algorithm combining image recognition, control, and decision-making	High stability and accuracy in autonomous tracking and landing
[61]	Drone landing	System combining maker detection with super-resolution reconstruction	High precision and stability in drone landing

The use of deep reinforcement learning for autonomous unmanned aerial vehicles (UAV) landing on a moving platform is suggested in [58] A deep reinforcement learning algorithm that can successfully land the UAV and adapt to the shifting conditions of the moving platform is presented by the authors. To guarantee a secure and precise landing, the algorithm takes into account wind, platform velocity, and platform orientation. The findings demonstrate that, in terms of stability and accuracy, the proposed algorithm performs better than conventional landing strategies. The authors also emphasize the need for additional research in this area and discuss the difficulties and limitations of using deep reinforcement learning for autonomous UAV landing on moving platforms. Overall, the study highlights the significance of ongoing research and advancement in this field and demonstrates the potential of deep reinforcement learning for autonomous UAV landing on moving platforms.

The use of reinforcement learning for the autonomous landing of a multi-copter unmanned aerial vehicle (UAV) is the primary focus of the paper [59] To deal with the difficulties of landing a multi-copter unmanned aerial vehicle (UAV), the authors propose a vision-based reinforcement learning strategy that takes into account things like landing site detection, stability, and orientation. The algorithm improves its accuracy over time through trial-and-error learning. The findings demonstrate that the proposed strategy is able to carry out autonomous landings with high precision even in difficult circumstances like strong wind or uneven landing surfaces. The limitations of using reinforcement learning for autonomous UAV landing are also discussed, as is the need for additional research and development in this area. Overall, the study demonstrates the importance of continuing progress in this field and the potential of reinforcement learning for the vision-based autonomous landing of multi-copter UAVs.

A deep reinforcement learning strategy for the autonomous tracking and landing of unmanned aerial vehicles (UAVs) is presented in [60] To enable autonomous UAV tracking and landing, the authors propose a reinforcement learning algorithm that combines image recognition, control, and decision-making. The UAV’s camera’s image data are analyzed by the algorithm, which uses deep neural networks to make decisions based on that information. The findings demonstrate that the proposed algorithm is capable of autonomous tracking and landing with high stability and accuracy. The authors also offer suggestions for future research and talk about the difficulties and limitations of using deep reinforcement learning for autonomous UAV tracking and landing. Overall, the study demonstrates the importance of continuing progress in this field and the potential of deep reinforcement learning for autonomous UAV tracking and landing.

A deep learning-based strategy for landing drones is presented in [61] To enhance the precision and stability of drone landings, the authors propose a system that combines marker detection with super-resolution reconstruction. In order to enable more precise marker detection, the system uses deep neural networks to perform super-resolution reconstruction on the images captured by the drone’s camera. The drone’s landing is then guided by the marker detection data. The findings demonstrate that the proposed system is capable of landing drones with high precision and stability even in challenging conditions. The limitations of using deep learning for drone landing are also discussed, as is the need for additional research and development in this area. Overall, the study emphasizes the significance of continuing progress in this field and demonstrates the potential of deep learning to enhance the stability and accuracy of drone landings.

8.3. Drone Imaging

A method for recognizing diseased Pinus trees in images taken by unmanned aerial vehicles (UAVs) is presented in [62] To locate diseased trees in UAV images, the authors employ a combination of deep learning and the AdaBoost classifier. The findings demonstrate that, in comparison to more conventional machine learning approaches, the proposed method is capable of accurately identifying diseased Pinus trees. Monitoring and managing forests and preventing the spread of diseases in trees can both benefit from this approach.

Table 8. Comparison table with different ML applications with drone Imaging.

Table 8. Comparison table with different ML applications with drone Imaging.

Study	Objective	Method	Findings
[62]	Identify diseased trees	DL and Adaboost classifier	More accurate than conventional ML approches
[63]	Differentiate mature and immature strawberries	DL	Accuracy classifies maturity
[64]	Assess disaster damage	DL	Effective and efficient approach
[65]	Find disease in plants	ML and hyperspectral imaging	Effective at detecting diseases
[66]	Identity vine diseases	DL segmentation and optimized image registration	Efficient and accurate identification of vine diseases

The purpose of [63] is to use deep learning methods to classify the maturity of strawberries in images taken by near-ground imaging and unmanned aerial vehicles (UAVs). The goal of the authors is to create a classification model that can tell the difference between mature and immature strawberries in the images. This could be useful in agriculture for predicting yields and harvesting fruit in an effective way. The results demonstrate that the deep learning model can accurately classify strawberries’ maturity when trained on a dataset of UAV and near-ground images.

A method for estimating the damage caused by a disaster using aerial images captured by unmanned aerial vehicles (UAVs) and deep learning algorithms is discussed in [64] For the purposes of disaster management, the purpose of this study is to provide an effective and precise method for assessing disaster damage. The authors suggest using deep learning algorithms to look at UAV images and figure out how much damage the disaster did. The findings demonstrate that the proposed approach can provide useful information for disaster management systems and is both effective and efficient in estimating disaster damage.

The use of unmanned aerial vehicles (UAVs) for remote sensing to detect citrus canker disease is investigated in [65] The authors analyze the data they have collected and use machine learning algorithms and hyperspectral imaging to find the disease in the plants. The findings demonstrate that the proposed method is effective at detecting the disease, which can aid in early treatment and prevent further spread.

A method for detecting vine diseases in UAV multispectral images using optimized image registration and deep learning segmentation is proposed in [66] A deep learning segmentation technique is used to identify the diseases, and an optimized image registration method is used to precisely align the multispectral images taken by the UAV. When compared to more conventional approaches, the outcomes of the proposed strategy are evaluated and found to be promising for efficiently and accurately identifying vine diseases.

8.4. Detection of Other Drones

8.4.1. Detection of Other Drones Using Sound

The creation of a real-time sound detection and analysis system for unmanned aerial vehicles (UAVs) is the primary focus of [67] This system aims to use unmanned aerial vehicles (UAVs) to detect and analyze sounds in real time, which can be useful for a wide range of applications, including search and rescue, environmental monitoring, and surveillance. The system’s design and implementation, as well as its hardware and software components, are described in detail, and the system’s performance is evaluated through experiments and analysis.

The development of a system for detecting and identifying drones based on their audio is the primary focus of [68] The system processes the drones’ audio signals and uses deep learning algorithms to identify them based on the distinctive sound patterns they produce. The research aims to develop an efficient and effective method for real-time drone detection and identification.

A novel method for detecting drones based on their sound is presented in [69] To improve drone detection accuracy, the paper proposes a system that combines result-level fusion of multiple audio features with deep learning. To identify drone sounds, the deep learning model is trained on audio data from various drone types and ambient noise. The outputs of multiple audio feature extraction models are then combined with the result-level fusion to further enhance the drone detection system’s performance. The proposed system was put through testing and evaluation in a real-world setting, and the findings demonstrated that it is capable of accurately identifying drones in real time with low false alarm rates. The authors conclude that security and surveillance applications requiring real-time drone detection may benefit from the proposed method.

A drone detection system that uses multiple acoustic nodes and machine learning models to detect unmanned aerial vehicles (UAVs) is presented in [70] Multiple microphones are used by the system to collect ambient acoustic signals, which are then analyzed by machine learning algorithms to see if they come from a UAV. Additionally, a decision-level fusion mechanism is incorporated into the system to combine the results of multiple acoustic nodes to enhance overall detection accuracy. Using real-world data, the authors evaluate the system’s performance and demonstrate that, in terms of detection accuracy and response time, it performs better than conventional drone detection methods. The findings show that the proposed system could be used in a variety of security and surveillance scenarios where UAV detection is crucial.

A framework for using radio frequency (RF) signals to identify unmanned aerial vehicles (UAVs) is presented in [71] To accurately detect and identify UAVs in real time, the authors suggest applying a machine learning method to the RF signals gathered by multiple sensors. To classify the RF signals into UAV and non-UAV categories, the system employs a combination of feature extraction, dimension reduction, and machine learning algorithms. Using real-world RF data, the authors evaluate the effectiveness of their framework and demonstrate that it detects and identifies UAVs with high accuracy. This study’s findings can be used in a variety of real-world situations, like security and surveillance, where accurate UAV detection is crucial.

Table 9. Comparison table with different ML applications with drone detection.

Table 9. Comparison table with different ML applications with drone detection.

Study	Technology	Advantages	Disadvantages
[67]	Sound detection and analysis	Real-time detection, useful for search and rescue, environmental monitoring, and surveillance	Limited range, may not work well in noisy environments
[68]	Sound analysis with DL	Efficient and effective method for real-time drone detection and identification	May have difficulty distinguishing drone sounds from other ambient noises
[69]	Combinations of audio features with DL	High detection accuracy, low false alarm rates	Complex system design and implementation
[70]	Acoustic node and ML	Better detection accuracy and response time	Multiple acoustic nodes may be expensive and impractical in some solutions
[71]	Radio frequency signal detection and ML	High accuracy in detecting and identifying UAVs	May not work well in noisy environments
[72]	MIMO-OFDM radar	Effective solution for drone detection	Limited range, requires line-of-sight
[73]	Phase-interferometric Doppler radar	Accurately detects and tracks drones in real time	Limited range, requires line-of-sight
[74]	Passive radar	Stealthy and cost-effective compared to active radar	Limited range and sensitivity, may not work well in noisy environments
[75]	CNN-based drone detection using RF signals	High accuracy and faster computation time than traditional ML approaches	may have difficulty distinguishing drone signals from other RF signals
[76]	Radar cross-section signatures and ML	Distingushes drones from other objects based on their shape and size	Limited range and sensitivity, requires line-of-sight

8.4.2. Detection of Other Drones Using Radar

The paper [72] focuses on using multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) radar system for detecting drones. The proposed system is based on the MIMO-OFDM radar technology and aims to provide an effective solution for drone detection. The paper presents the design and implementation of the MIMO-OFDM radar system and analyzes its performance for drone detection. The results demonstrate the feasibility of using MIMO-OFDM radar technology for drone detection and the effectiveness of the proposed system.

The paper [73] presents a drone detection and tracking system based on phase-interferometric Doppler radar. The system utilizes the Doppler shift to detect the drone’s motion and determine its location. The paper explains the design of the system and evaluates its performance through simulations and experiments. The results show that the system is capable of accurately detecting and tracking drones in real time, making it a promising solution for drone detection and tracking in various applications.

The paper [74] investigates the feasibility of using passive radars to detect drones. Passive radar systems rely on reflected signals from existing transmitters instead of emitting their own signals, making them stealthy and cost-effective compared to traditional active radars. The study explores the potential of passive radar systems to detect and track drones and analyzes the performance of different passive radar configurations. The results indicate that passive radar systems have the potential to detect and track drones in certain scenarios. However, the authors also highlight the limitations of passive radar systems and suggest ways to improve their performance for drone detection

The paper [75] explores the use of convolutional neural networks (CNNs) to detect drones based on radio-frequency (RF) signals. The authors collect a dataset of drone and non-drone signals and use it to train and test the CNN. They evaluate the performance of the CNN-based drone detection system and compare it with a traditional machine-learning approach. The results show that the CNN-based system outperforms the traditional approach in terms of accuracy and computation time. The authors conclude that CNNs are a promising approach for drone detection based on RF signals.

The paper [76] investigates the use of radar cross-section (RCS) signatures for the detection and classification of drones. The authors propose a system that uses an RCS measurement to capture the reflection of a drone’s electromagnetic signal, which can be used to distinguish the drone from other objects in the environment. The system utilizes machine learning algorithms, specifically decision trees and support vector machines, to classify the RCS signatures into different drone categories based on their shape and size. The authors evaluate the performance of their proposed system through simulations and experiments, showing that it can accurately detect and classify different types of drones. The results indicate that RCS signatures are a promising technique for drone detection and classification.

9. Discussion

9.2. Most Commonly Used Methods of Machine Learning in the Drone Industry

In order to enhance drones’ capabilities, safety, and efficiency, machine learning methods are being rapidly adopted by the drone industry. Deep reinforcement learning is one of the most widely used methods of machine learning in the drone industry.

Supervised learning: In the drone industry, supervised learning is a popular method of machine learning in which algorithms are trained on labeled data to find patterns and make predictions. Applications like object detection and recognition, image and video classification, and obstacle avoidance make use of supervised learning in the drone industry.

Unsupervised learning: Another popular method for machine learning in the drone industry is unsupervised learning, in which algorithms learn to recognize patterns and structures in unlabeled data. Unsupervised learning can be used for anomaly detection and clustering in the drone industry.

Reinforcement learning: A method of machine learning called reinforcement learning involves instructing an agent to take actions in a given setting in order to get the most out of a reward. In the drone industry, applications like autonomous flight, in which the drone learns to navigate and avoid obstacles in order to reach a destination, can benefit from reinforcement learning.

Deep learning: Neural networks are used in deep learning, a subset of machine learning, to model complex data relationships. Applications in the drone industry that make use of deep learning include autonomous flight, image, and video classification, and the detection and recognition of objects.

Transfer learning: A method of machine learning called transfer learning involves applying a previously trained model to a new task. Transfer learning can be used to adapt existing models for new environments or applications in the drone industry, thereby reducing the amount of data and training time required.

Hybrid methods: Methods that combine The drone industry also frequently employ hybrid strategies that combine multiple machine learning methods. For autonomous flight, for instance, deep reinforcement learning can be used to teach the drone to navigate and avoid obstacles by combining reinforcement learning with neural networks.

In general, a variety of machine learning methods, including deep reinforcement learning, are being rapidly adopted by the drone industry to make drone operations safer, more effective, and more efficient.

Figure 3. Commonly used ML techniques in Drone Industry.

Figure 3. Commonly used ML techniques in Drone Industry.

10. Conclusion

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