Since artificial intelligence (AI) has taken the world by storm, many businesses have begun to explore its usefulness in various applications. One such application is in the realm of drone deliveries, which involves using an autonomous drone fleet to transport goods from one location to another.
This form of delivery has been growing increasingly popular due to its cost-effectiveness and efficiency. To make this type of delivery successful, aerial vehicles must use artificial intelligence to facilitate the process. This blog post will explore the different components of AI that make drone delivery possible.
Five Applications of AI in the Drone Delivery Ecosystem
Autonomous deliveries are already a reality, with logistics companies likeDrone Express andAmazon Prime Air leading the way. How do they do it? Let’s explore the five components of AI necessary for delivery drones to function without human intervention.
Obstacle Detection and Collision Avoidance
Just like human pilots avoid obstacles in their path, unmanned aircraft systems (UAS) must do the same. Drones rely on artificial intelligence for obstacle detection and collision avoidance to ensure a safe flight. These obstacles include electric wires, buildings, trees, birds, or other aircraft in the drone’s path.
AI-powered image recognition and pattern detection algorithms help drones adjust their flight paths to avoid collisions with objects. Image sensors, radar, and lidar also help create a 3D map of the area, giving the drone a “bird’s eye view” of its surroundings. This feature helps ensure that the drone avoids obstacles and follows its desired flight path without trouble. Computer vision software then enables the drone to recognize and identify environmental obstacles.
Note that some units also come equipped with microphones and audio sensors, which can detect sound waves to help the drone make flight decisions. As such, drones with this technology can distinguish between a flock of birds and an airplane.
Some autonomous vehicles, such as self-driving cars, use GPS for navigation. But this isn’t always a feasible solution when it comes to drone technology. What if they must bypass a canyon, skyscraper, or other structure that is not GPS-enabled? That’s why some drones come equipped with AI-powered navigation systems, which enable them to navigate without pre-programmed flight paths.
Using machine learning algorithms, drones can create a 3D map of their environment and use it to identify the best route to get from Point A to Point B. These algorithms are also able to detect changes in terrain, wind speed, and other environmental conditions that could affect the drone’s flight path. The drone can then use its in-flight navigation systems to adjust its route to reach the intended destination.
This level of autonomy falls into the level 4 category of autonomous vehicles, which require minimal human intervention. It’s a step up from level 3 autonomy, where a human must be present at all times to monitor the vehicle.
Contingency Management and Emergency Landing
Since machine learning models enable the drone to “think” for itself, it can make decisions in the event of a contingency. That can include an emergency landing for low battery or other issues that could threaten the safety of the mission. Otherwise, we risk crashing the drone or endangering lives.
AI-powered autonomous drones can make decisions based on their environment and the data they collect in flight. For instance, if a battery is low, the drone can use its image recognition algorithms to identify a safe landing spot. It helps ensure the drone completes its mission while maintaining safety standards.
Without drone operators, flight decisions must be made by the drone itself. Artificial intelligence is essential to enable drones to interpret their surroundings and make decisions in split-second scenarios. With lightning-fast processing speeds, AI-powered drones can make decisions in real-time, respecting contingency management and emergency landing protocols.
Delivery Drop
The delivery drop is one of the most critical aspects of drone logistics. Today, many drone delivery companies simply air-drop packages from their drones. The package has a parachute that opens up when it reaches a certain altitude, allowing it to safely reach its destination.
But this method is far from perfect: packages can get lost in the process or end up in an unsafe place. Why not land the drone? Because the spinning blades can cause quite a bit of damage when they come in contact with something. If they make contact with a person, it could be fatal.
The solution is an AI-powered landing system, which allows the drone to accurately identify a safe landing spot and then land there without putting anyone in danger. The AI can detect terrain anomalies, such as trees or other obstacles that could cause the drone to crash. It can also detect subtle environmental changes, such as wind speed and direction, to ensure a safe landing.
Safe Landing
In some cases, landing the drone will be even more complex. Urban areas, for instance, will require the drone to maneuver through tight spaces to make a safe delivery. Remember, there is no air traffic control system like there is for piloted aircraft, so the drone must be able to navigate tight spaces autonomously.
AI-enabled drones can do just that; they can recognize objects and obstacles in real-time, allowing them to make decisions on the fly. With image recognition algorithms and 3D mapping capabilities, AI-powered drones can maneuver through tight spaces and land safely in even the most difficult environments.
Eventually, these drones could take it a step further by delivering medical supplies to war-torn regions or rescuing people in need. One thing is sure: it all starts with developing the AI behind the drone delivery system.
Drones Need High-Quality Data To Deliver Goods
So, why are we not seeing more drones delivering goods? Besides the legislation hurdle, the technology is not yet sophisticated enough to guarantee safety and accuracy. The key to a successful drone delivery system is high-quality data. Machine learning needs lots of accurate data to make informed decisions on the fly.
Autonomous drone delivery depends on image recognition, object detection, and other AI-enabled algorithms. All of these require high-quality data to function properly. There are three main ways to acquire this data:
Pre-Processing Drone Data
The first way is to pre-process drone data before the mission. This includes performing detailed aerial surveys of the delivery area, mapping out potential obstacles, and having precise knowledge of the terrain. Using 2-D images, lidar sensors, and photogrammetry, we can create detailed 3-D models in order to navigate the environment. Commercial drones can make smarter decisions while in flight and plan a safe course of action by pre-processing the data before takeoff.
Imagine feeding a delivery drone detailed information about New York City before it takes off. This data would include the exact locations of buildings, sidewalks, parks, and other obstacles. The drone could plan its route with this information, allowing it to avoid any potential risks.
These digital twins are especially important in densely populated cities or areas with ever-changing landscapes. By pre-processing the data, drones can identify potential hazards and devise a safe path of flight.
Live-Processing Drone Data
Another technique is to use live-processing data. We can enhance the drone’s AI capabilities by providing real-time data from its environment. This technique should be used when the drone’s sensors aren’t enough to make an informed decision. For instance, a simple image recognition camera might see a volcano as a normal mountain. To ensure safety, the drone should be able to identify a volcano based on more detailed data.
In that case, the drone could collect additional information from its environment, such as temperature readings or seismic activity. This live-processing data can then inform the drone of any potential hazards in real-time.
Post-Processing Drone Data
Finally, we can look back at the drone’s data after it has completed its mission. This post-processing technique is often used to detect errors in the drone’s path and plan future missions. We can also use this data to analyze the efficiency of our algorithms and make further improvements.
Improving delivery speeds, safety, and accuracy is essential for commercializing drone delivery. Customer service is key, as customers expect their goods to be delivered on time and in one piece. By combining pre-, live- and post-processing data, we can ensure that our drones are making the right decisions while in flight.
Annotation Techniques for Drone Data
As the intelligent aircraft makes its way through space, it needs to distinguish between objects, such as buildings and trees. In order to do this, the logistics industry must annotate the data accurately. Annotation techniques for drone data involve labeling each object in the image or video feed to train an AI’s neural networks.
Several different annotation techniques can be used for drone data. Here are a few of the most common:
Bounding Box
Airborne logistics companies annotate objects in aerial photographs using the bounding box technique. This method involves using shapes such as squares and rectangles around each object to identify them. It can label items such as buildings, trees, vehicles, people, and more.
Human operators can draw a box around each object using manual and automated methods to create a label. This method allows the AI to learn and identify objects in a scene quickly.
Tracking
AI-powered drones can also use tracking to follow an object’s trajectory throughout the course of an operation. This technique involves using a combination of sensors and cameras to locate and follow an object as it moves through space. By following each frame of a video feed, the drone can identify objects in a scene and track their distances from each other.
For example, a drone can track a bird in flight and determine its speed and direction. This technique can be used to analyze an object’s behavior as it moves through the air, which is essential for ensuring safety while operating drone fleets.
Polygon
Aerial drone delivery companies also use the polygon technique for annotating data. This method involves capturing the highest point of an object and connecting it with multiple points around its perimeter. This technique is more accurate than the bounding box method, allowing for a better definition of objects.
It can accurately identify objects such as buildings and trees, which are often difficult to pinpoint with traditional bounding box techniques.
Polyline
Similarly, the polyline annotation technique is used to identify objects in drone footage. It entails drawing a line between two points of an object, connecting them, and creating a path or trajectory.
A road or a power line may seem to go on for miles from the drone’s perspective. Polyline annotation allows for more accurate identification of pathways and routes that the drone’s AI can use to navigate.
3-D Cuboid
Next, the 3-D cuboid uses 3-D laser scanners, RADAR, and LiDAR sensors to annotate objects in an environment. It uses a point cloud model to identify objects in depth, allowing for accurate object detection. 3-D bounding boxes are created to label objects, and the AI can orient itself with the environment accurately.
2-D and 3-D Semantic Segmentation
Finally, the 2-D and 3-D cuboid techniques use the same laser scanning, RADAR, and LiDAR sensors as the 3-D semantic segmentation. But this technique involves labeling each pixel of an image with a specific class label, such as trees, roads, buildings, or people. The AI can then classify the pixels accurately and create a more accurate understanding of its environment.
The Challenge: AI and Computer Vision Data Quality in Drones
Drone delivery operations currently all face the same problem: access to high-quality training data. For a vision model to successfully identify objects, it must be trained on accurate and annotated data. Additionally, it takes more than a handful of images to train a model — hundreds or thousands of labeled frames are typically required.
A logistics network needs accurate data to ensure the drones can properly identify objects, navigate the environment, and make decisions. Obtaining such data can be difficult for various reasons — from cost to time constraints — but it’s essential for implementing effective drone delivery operations.
Therefore, organizations need reliable sources of annotated AI and computer vision data to improve their drones’ accuracy and performance.
Although the cost savings and environmental benefits of drone delivery systems are clear, the reality is that achieving full autonomy is a complex task. Besides the operating costs associated with running a drone delivery network, there are regulatory, safety, and privacy issues that need to be addressed.
Since delivery drones will travel hundreds, if not thousands, of miles daily, they must be equipped with sophisticated sensors and AI algorithms to ensure safe navigation. According to aerospace standards, drone air traffic control beyond the visual line of sight (BVLOS) requires clearance from the Federal Aviation Administration (FAA). Additionally, drones must abide by local laws and safety regulations.
This computer vision will act as the drones’ “eyes,” allowing them to accurately identify objects, detect obstacles, and understand the environment. The AI must be trained on high-quality and accurate annotated data to make this possible. With zero room for error, the data must be valid and of the highest quality.
Before we can see the full potential of drone delivery, organizations need to address these realities and leverage the right technologies to enable safe and efficient operations.
Drone Express partners like Microsoft have shown us a glimpse of the future of drone delivery. As more drone delivery facilities gain approval from the FAA, drone autonomy technology must continue to evolve. This application of AI and computer vision can potentially revolutionize logistics, but only if organizations have access to the right data.
What began as a few hobbyists playing around with drones is now becoming a reality. Soon, human interaction won’t be required for delivery services thanks to the advances in AI. The possibilities are endless as we continue working together to make this technology available.
References
Koubaa, Anis, and Ahmad Taher Azar. Deep Learning for Unmanned Systems. Springer Nature, 2021.
Lukomnik, Jon, and James P. Hawley. Moving Beyond Modern Portfolio Theory: Investing That Matters. Routledge, 2021.
Mohanty, Sachi Nandan, et al. Drone Technology: Future Trends and Practical Applications. John Wiley & Sons, 2023.
Solanki, Arun, et al. The Internet of Drones: AI Applications for Smart Solutions. Apple Academic Press Incorporated, 2022.
Introduction
Since artificial intelligence (AI) has taken the world by storm, many businesses have begun to explore its usefulness in various applications. One such application is in the realm of drone deliveries, which involves using an autonomous drone fleet to transport goods from one location to another.
This form of delivery has been growing increasingly popular due to its cost-effectiveness and efficiency. To make this type of delivery successful, aerial vehicles must use artificial intelligence to facilitate the process. This blog post will explore the different components of AI that make drone delivery possible.
Table of contents
Five Applications of AI in the Drone Delivery Ecosystem
Autonomous deliveries are already a reality, with logistics companies like Drone Express and Amazon Prime Air leading the way. How do they do it? Let’s explore the five components of AI necessary for delivery drones to function without human intervention.
Obstacle Detection and Collision Avoidance
Just like human pilots avoid obstacles in their path, unmanned aircraft systems (UAS) must do the same. Drones rely on artificial intelligence for obstacle detection and collision avoidance to ensure a safe flight. These obstacles include electric wires, buildings, trees, birds, or other aircraft in the drone’s path.
AI-powered image recognition and pattern detection algorithms help drones adjust their flight paths to avoid collisions with objects. Image sensors, radar, and lidar also help create a 3D map of the area, giving the drone a “bird’s eye view” of its surroundings. This feature helps ensure that the drone avoids obstacles and follows its desired flight path without trouble. Computer vision software then enables the drone to recognize and identify environmental obstacles.
Note that some units also come equipped with microphones and audio sensors, which can detect sound waves to help the drone make flight decisions. As such, drones with this technology can distinguish between a flock of birds and an airplane.
Also Read: Which Companies Use Drone Delivery?
GPS-Free Navigation
Some autonomous vehicles, such as self-driving cars, use GPS for navigation. But this isn’t always a feasible solution when it comes to drone technology. What if they must bypass a canyon, skyscraper, or other structure that is not GPS-enabled? That’s why some drones come equipped with AI-powered navigation systems, which enable them to navigate without pre-programmed flight paths.
Using machine learning algorithms, drones can create a 3D map of their environment and use it to identify the best route to get from Point A to Point B. These algorithms are also able to detect changes in terrain, wind speed, and other environmental conditions that could affect the drone’s flight path. The drone can then use its in-flight navigation systems to adjust its route to reach the intended destination.
This level of autonomy falls into the level 4 category of autonomous vehicles, which require minimal human intervention. It’s a step up from level 3 autonomy, where a human must be present at all times to monitor the vehicle.
Contingency Management and Emergency Landing
Since machine learning models enable the drone to “think” for itself, it can make decisions in the event of a contingency. That can include an emergency landing for low battery or other issues that could threaten the safety of the mission. Otherwise, we risk crashing the drone or endangering lives.
AI-powered autonomous drones can make decisions based on their environment and the data they collect in flight. For instance, if a battery is low, the drone can use its image recognition algorithms to identify a safe landing spot. It helps ensure the drone completes its mission while maintaining safety standards.
Without drone operators, flight decisions must be made by the drone itself. Artificial intelligence is essential to enable drones to interpret their surroundings and make decisions in split-second scenarios. With lightning-fast processing speeds, AI-powered drones can make decisions in real-time, respecting contingency management and emergency landing protocols.
Delivery Drop
The delivery drop is one of the most critical aspects of drone logistics. Today, many drone delivery companies simply air-drop packages from their drones. The package has a parachute that opens up when it reaches a certain altitude, allowing it to safely reach its destination.
But this method is far from perfect: packages can get lost in the process or end up in an unsafe place. Why not land the drone? Because the spinning blades can cause quite a bit of damage when they come in contact with something. If they make contact with a person, it could be fatal.
The solution is an AI-powered landing system, which allows the drone to accurately identify a safe landing spot and then land there without putting anyone in danger. The AI can detect terrain anomalies, such as trees or other obstacles that could cause the drone to crash. It can also detect subtle environmental changes, such as wind speed and direction, to ensure a safe landing.
Safe Landing
In some cases, landing the drone will be even more complex. Urban areas, for instance, will require the drone to maneuver through tight spaces to make a safe delivery. Remember, there is no air traffic control system like there is for piloted aircraft, so the drone must be able to navigate tight spaces autonomously.
AI-enabled drones can do just that; they can recognize objects and obstacles in real-time, allowing them to make decisions on the fly. With image recognition algorithms and 3D mapping capabilities, AI-powered drones can maneuver through tight spaces and land safely in even the most difficult environments.
Eventually, these drones could take it a step further by delivering medical supplies to war-torn regions or rescuing people in need. One thing is sure: it all starts with developing the AI behind the drone delivery system.
Drones Need High-Quality Data To Deliver Goods
So, why are we not seeing more drones delivering goods? Besides the legislation hurdle, the technology is not yet sophisticated enough to guarantee safety and accuracy. The key to a successful drone delivery system is high-quality data. Machine learning needs lots of accurate data to make informed decisions on the fly.
Autonomous drone delivery depends on image recognition, object detection, and other AI-enabled algorithms. All of these require high-quality data to function properly. There are three main ways to acquire this data:
Pre-Processing Drone Data
The first way is to pre-process drone data before the mission. This includes performing detailed aerial surveys of the delivery area, mapping out potential obstacles, and having precise knowledge of the terrain. Using 2-D images, lidar sensors, and photogrammetry, we can create detailed 3-D models in order to navigate the environment. Commercial drones can make smarter decisions while in flight and plan a safe course of action by pre-processing the data before takeoff.
Imagine feeding a delivery drone detailed information about New York City before it takes off. This data would include the exact locations of buildings, sidewalks, parks, and other obstacles. The drone could plan its route with this information, allowing it to avoid any potential risks.
These digital twins are especially important in densely populated cities or areas with ever-changing landscapes. By pre-processing the data, drones can identify potential hazards and devise a safe path of flight.
Live-Processing Drone Data
Another technique is to use live-processing data. We can enhance the drone’s AI capabilities by providing real-time data from its environment. This technique should be used when the drone’s sensors aren’t enough to make an informed decision. For instance, a simple image recognition camera might see a volcano as a normal mountain. To ensure safety, the drone should be able to identify a volcano based on more detailed data.
In that case, the drone could collect additional information from its environment, such as temperature readings or seismic activity. This live-processing data can then inform the drone of any potential hazards in real-time.
Post-Processing Drone Data
Finally, we can look back at the drone’s data after it has completed its mission. This post-processing technique is often used to detect errors in the drone’s path and plan future missions. We can also use this data to analyze the efficiency of our algorithms and make further improvements.
Improving delivery speeds, safety, and accuracy is essential for commercializing drone delivery. Customer service is key, as customers expect their goods to be delivered on time and in one piece. By combining pre-, live- and post-processing data, we can ensure that our drones are making the right decisions while in flight.
Annotation Techniques for Drone Data
As the intelligent aircraft makes its way through space, it needs to distinguish between objects, such as buildings and trees. In order to do this, the logistics industry must annotate the data accurately. Annotation techniques for drone data involve labeling each object in the image or video feed to train an AI’s neural networks.
Several different annotation techniques can be used for drone data. Here are a few of the most common:
Bounding Box
Airborne logistics companies annotate objects in aerial photographs using the bounding box technique. This method involves using shapes such as squares and rectangles around each object to identify them. It can label items such as buildings, trees, vehicles, people, and more.
Human operators can draw a box around each object using manual and automated methods to create a label. This method allows the AI to learn and identify objects in a scene quickly.
Tracking
AI-powered drones can also use tracking to follow an object’s trajectory throughout the course of an operation. This technique involves using a combination of sensors and cameras to locate and follow an object as it moves through space. By following each frame of a video feed, the drone can identify objects in a scene and track their distances from each other.
For example, a drone can track a bird in flight and determine its speed and direction. This technique can be used to analyze an object’s behavior as it moves through the air, which is essential for ensuring safety while operating drone fleets.
Polygon
Aerial drone delivery companies also use the polygon technique for annotating data. This method involves capturing the highest point of an object and connecting it with multiple points around its perimeter. This technique is more accurate than the bounding box method, allowing for a better definition of objects.
It can accurately identify objects such as buildings and trees, which are often difficult to pinpoint with traditional bounding box techniques.
Polyline
Similarly, the polyline annotation technique is used to identify objects in drone footage. It entails drawing a line between two points of an object, connecting them, and creating a path or trajectory.
A road or a power line may seem to go on for miles from the drone’s perspective. Polyline annotation allows for more accurate identification of pathways and routes that the drone’s AI can use to navigate.
3-D Cuboid
Next, the 3-D cuboid uses 3-D laser scanners, RADAR, and LiDAR sensors to annotate objects in an environment. It uses a point cloud model to identify objects in depth, allowing for accurate object detection. 3-D bounding boxes are created to label objects, and the AI can orient itself with the environment accurately.
2-D and 3-D Semantic Segmentation
Finally, the 2-D and 3-D cuboid techniques use the same laser scanning, RADAR, and LiDAR sensors as the 3-D semantic segmentation. But this technique involves labeling each pixel of an image with a specific class label, such as trees, roads, buildings, or people. The AI can then classify the pixels accurately and create a more accurate understanding of its environment.
Also Read: How Can AI Help Us Optimize Physical Security.
The Challenge: AI and Computer Vision Data Quality in Drones
Drone delivery operations currently all face the same problem: access to high-quality training data. For a vision model to successfully identify objects, it must be trained on accurate and annotated data. Additionally, it takes more than a handful of images to train a model — hundreds or thousands of labeled frames are typically required.
A logistics network needs accurate data to ensure the drones can properly identify objects, navigate the environment, and make decisions. Obtaining such data can be difficult for various reasons — from cost to time constraints — but it’s essential for implementing effective drone delivery operations.
Therefore, organizations need reliable sources of annotated AI and computer vision data to improve their drones’ accuracy and performance.
The Reality of Drone Autonomy
Although the cost savings and environmental benefits of drone delivery systems are clear, the reality is that achieving full autonomy is a complex task. Besides the operating costs associated with running a drone delivery network, there are regulatory, safety, and privacy issues that need to be addressed.
Since delivery drones will travel hundreds, if not thousands, of miles daily, they must be equipped with sophisticated sensors and AI algorithms to ensure safe navigation. According to aerospace standards, drone air traffic control beyond the visual line of sight (BVLOS) requires clearance from the Federal Aviation Administration (FAA). Additionally, drones must abide by local laws and safety regulations.
This computer vision will act as the drones’ “eyes,” allowing them to accurately identify objects, detect obstacles, and understand the environment. The AI must be trained on high-quality and accurate annotated data to make this possible. With zero room for error, the data must be valid and of the highest quality.
Before we can see the full potential of drone delivery, organizations need to address these realities and leverage the right technologies to enable safe and efficient operations.
Also Read: A drone that can dodge anything thrown at it.
Conclusion
Drone Express partners like Microsoft have shown us a glimpse of the future of drone delivery. As more drone delivery facilities gain approval from the FAA, drone autonomy technology must continue to evolve. This application of AI and computer vision can potentially revolutionize logistics, but only if organizations have access to the right data.
What began as a few hobbyists playing around with drones is now becoming a reality. Soon, human interaction won’t be required for delivery services thanks to the advances in AI. The possibilities are endless as we continue working together to make this technology available.
References
Koubaa, Anis, and Ahmad Taher Azar. Deep Learning for Unmanned Systems. Springer Nature, 2021.
Lukomnik, Jon, and James P. Hawley. Moving Beyond Modern Portfolio Theory: Investing That Matters. Routledge, 2021.
Mohanty, Sachi Nandan, et al. Drone Technology: Future Trends and Practical Applications. John Wiley & Sons, 2023.
Solanki, Arun, et al. The Internet of Drones: AI Applications for Smart Solutions. Apple Academic Press Incorporated, 2022.
Share this: