In previous article, We got a brief introduction into drone intelligence. In this article, we are going to discuss about implementation of AI for controlling autonomous UAVs/drones.
Artificial intelligence is rapidly spreading across modern industries. The advent of advanced autonomous drones and their continuous technological
evolution has marked a significant milestone in various industrial sectors improving the performance and capacity of industrial processes. This progress has revolutionized sectors such as agriculture, infrastructure inspection and environmental monitoring. Likewise, innovative applications are emerging, such as the use of drones to create access points, thus
providing a constant connectivity service in areas where internet access is limited.
Drones have facilitated cargo transportation achieving economies of scale by trajectory optimization and extended autonomies both in range and in operation by the integration of AI in the control systems of drones. The evolution of this technology allows complex decisions to be made autonomously in real time by exploiting Deep Learning (DL) algorithms and massive data processing techniques.
The development of autonomous navigation systems provides greater adaptability and efficiency in trajectory optimization, especially in dynamic environments. In addition, AI has proven to be essential in predictive maintenance and failure detection, contributing to the reliability and safety of drones. In the area of cargo transportation, AI has revolutionized logistics operations by facilitating the planning of optimal and adaptable routes. This dynamic approach, supported by real time tracking capability, not only improves operational efficiency but also contributes significantly to building a more sustainable
logistics future.
Now let’s deep dive into the combination of Drone and AI technologies.
Background of AI drone building:
The recent developments of AI has give us great advantages for building most advanced drone systems. Specially with recent approaches for machine learning algorithms, The ability to build fully-autonomous drones that have self-decision making ability during the mission.
Machine learning is a major field of Artificial intelligence that is used for data analyzing for data pattern recognition and self-decision making process. During last decade, we were able to develop more advanced ML algorithms called deep learning algorithms with their neural network architecture.
Through the use of statistical methods, models are trained to make classifications or predictions by discovering key information in various data mining-based projects. In this context of evolution, Deep learning emerges as an advanced technique within ML, consisting of artificial neural networks
with multiple layers. These deep structures allow us to address complex tasks, such as recognizing patterns in images or understanding natural language. There are a wide variety of ML algorithms, and it is beneficial to pre-classify them into categories following some basic criteria. One of the main and most significant criteria is related to the way in which the algorithms are trained. In this classification, four main methodologies are distinguished: supervised learning, unsupervised learning, and Reinforcement Learning (RL).
Supervised Learning:
Supervised learning is defined by the use of labeled data sets to train models that accurately classify data or predict outcomes.
This technique allows organizations to solve a wide variety of real world problems at scale, such as sorting spam into a folder other than the inbox. Various algorithms and calculation techniques are used in this process (Support Vector Machine (SVM), K-nearest neighbors (KNN), Random Forest (RF) etc.). One specific branch of supervised learning is DL, which has applications that cover a wide range of fields, including voice and image recognition, automatic translation, medical diagnosis,
and autonomous driving.
Unsupervised learning:
Unsupervised learning carries out the clustering of unlabeled data sets. These algorithms discover hidden data groupings or patterns without the need for any human intervention. Their ability to discover similarities and differences in information makes them the ideal solution for exploratory data analysis, cross selling strategies, customer segmentation, and image recognition. One of the most important algorithms presented in this methodology is the K-Means algorithm, whose theoretical basis is based on dividing a set of data into âkâ groups or clusters. Its main objective is to minimize the sum of the squared distances between the data points and the centroids of their respective clusters.
For example, The dynamic obstacle avoidance is more harder than static obstacle avoidance. Lets look at the following figure.
The previous figure shows two separate examples for detecting birds from Drone cameras in real-time. Birds are probably the most common dynamic obstacles that will be encountered by Autonomous Drones. The machine learning algorithm which we used in previous test is a supervised learning algorithm with pre-trained labeled data and a neural network architecture. The algorithm is checking every frame with a median speed of 2.3 milli-seconds to analyze for data patterns that detects birds in the frame. Based on the analyzed data, the algorithm can make decision that change the cause for drone movements within milliseconds. The major problem we encountered is that hardware we used for test the algorithms are not enough to reach it’s true potential speed.
Most of the machine algorithms we use to enable autonomous capabilities are using supervised learning techniques with labeled data for more precious data analyzing.
Role of Hardware:
An UAV is a physical vehicle system which contains both hardware and firmware/software. The interlinked connection between both of those components types decide the operational capability of aircraft.
In a UAV, there are several components that are essential for its flight capability.
Flight controller:
This is basically the brain unit or processing unit of a traditional drone. The flight controller’s main purpose is to analyze data from its internal sensors such as IMU units and external sensors such as GPS module to understand the drones current state and apply required adjustments to reach next state. There are several internal and external sensors in a Drone including IMU, accelerometer, Barometer, GPS, compass unit as well as other connected devices such as remote control receiver and connections to all speed controllers.
Motors and propellers:
Motors and propellers are essential for creating necessary external forces such as Thrust on the Drone. The high-speeding rotating is creating imbalance of air pressure between top and bottom layers of air near propellers. This pressure imbalance is creating a external force named as Thrust. This thrust work as the main energy factor for lifting-off the drone from ground level to a target altitude. The propellers are connected to motor units that transfer electricity into kinetic energy.
Speed controllers:
The motors are primarily controlled by speed controller units. They connected to flight controller and control the motor rotating speed by the signals from flight controller unit. Every propeller motor need a speed controller unit. So, if we are building a quadcopter model with 4 propeller units, we also need 4 speed controller units.
But the major factor about all of these components are that they are primarily controlled by remote control unit. So, we need to add more hardware components to make the aircraft a completely separate intelligent system. For that we are implementing following external hardware components.
- Onboard computer units
- Advanced sensors such as Lidar or ultrasonic
- Advanced Camera units
AI-applications for autonomous operations:
There are several subfields of Artificial intelligence that we are using to achieve Drone Intelligence including:
- Computer vision
- Deep learning
- supervised and sem-supervised learning
- Reinforcement learning
- natural language processing
A sample AI algorithmic structure:
following is a simple algorithmic program that is being used for taking off the Drone autonomously.
from dronekit import connect, VehicleMode, LocationGlobalRelative
import time
# connect vehicle:
autumn1 = connect('/dev/ttyACM0', baud=115200, wait_ready=True)
# function for take off:
def arm_takeoff(target_alt):
print("arming the motors..")
autumn1.mode = VehicleMode("GUIDED")
time.sleep(1)
while not autumn1.armed:
print("waiting for arming..")
time.sleep(1)
autumn1.armed = True
print("taking off the vehicle..")
autumn1.simple_takeoff(target_alt)
time.sleep(1)
while True:
print(f"Altitude: {autumn1.location.global_relative_frame.alt:.2f}m")
if autumn1.location.global_relative_frame.alt >= target_alt* 0.95:
print("Target altitude reached!")
break
time.sleep(1)
def landing():
print("vehicle is landing back..")
autumn1.mode = VehicleMode("LAND")
# intialize the autonomous process:
if __name__ == "__main__":
arm_takeoff(4) # take off to 3 meter altitude from ground
time.sleep(5) # hoover in target altitude
landing()
# terminating function:
autumn1.close()
The algorithm is written in Python programming language for autonomous take off without human intervention.
Advantages of AI-implementation and Drone-Intelligence:
There are several advantages of using intelligent drones in real world applications inluding:
- achieving more accuracy
- increasing cost and time efficiency
- increasing productivity
- expanding operational areas
- handling more complex tasks
- much easier usage
- high operational capacity and operational hours
- achieve more accuracy in operations
- intelligent operations
- climate and environmental impact
- using renewable energy
Importance of Drone intelligence and developing autonomous UVs:
The major conclusion from this article is that in the modern world, we have enough technological advancements and knowledge to build fully autonomous intelligent UVs. Countries are rapidly adapting to autonomous driving/self-driving vehicles. So, it is important to invest enough in autonomous unmanned vehicle developments as well. They can make a huge impact in the modern world by implementing autonomy in several fields.
The synergy between AI and drones is emerging as a dynamic and promising field of innovation. Drones that were originally designed for unmanned aerial operations have undergone a significant transformation thanks to the integration of AI algorithms. The growing availability of onboard computing power and the constant improvement of AI algorithms allow the design of tasks capable of adapting to changing environments, enabling drones to make complex decisions in real time. Security, both in visual navigation and environmental control, emerges as a significant application. The ability to avoid obstacles and to perform terrain recognition in real time has become apriority, together with the consideration of software attacks that may compromise safe drone operations. The identification of drones, especially those that are not catalogued, is acknowledged as a critical challenge. Although systems based on sound waves and radio frequency have proven effective with known drone models, uncertainty arises when
it comes to new models. Overcoming identification issues requires AI developments that allow systems to learn quickly and accurately as they encounter new signals and drone models.
Another fundamental aspect of security is cargo management, with concerns ranging from onboard resource limitations to personal and material security. This challenge, although largely negative, highlights the need for continued innovation in drone design and payload management because, in this application, drones are the main limiting factor. Due to drone resource limitations (i.e., battery, payload capacity, energy consumption, weather adversities, data storage limit, connectivity, and response time), AI algorithms promise the ability to analyze and process data in real time, allowing drones to make informed
decisions about how best to use their limited resources. For example, ML algorithms can predict energy consumption patterns and optimize flight paths to maximize range. Furthermore, the ability of AI to adapt to changes in environmental conditions or assigned tasks contributes to a more effective optimization of available resources, maximizing the usefulness of these aircraft in various applications.
Innovative and promising collaborative delivery and distribution paradigms face technical challenges like fleet coordination, obstacle avoidance in circumstances in which the cargo is transported by several drones, integration with urban infrastructure, integration with air and ground regulation standards, and connectives security due to an interconnection required between the aircraft that affect the operation. Also, last-mile delivery establishes itself as a key strategy in logistics operations, allowing products to be brought directly to the final destination. This approach is not without challenges that must be addressed
in order to guarantee the efficiency and effectiveness of this type of operations such as urban congestion, high operational costs, energy efficiency, package security, fleet location and integration systems, environmental impact, limitations in the autonomy of drones, restricting regulations, etc.
The future outlook for this technology is highly promising, although it faces some significant obstacles. The design of efficient and sustainable batteries stands as a fundamental challenge, since it constitutes a pillar for the advancement of operations related to AI algorithms and drones. On the other hand, but no less important, ethical, regulatory, and privacy aspects are crucial to help establish defined standards in this technical field. If these essential aspects can be successfully addressed, the future of this technology
looks extremely interesting, both in the logistics field and in the transformation of employment. Resolving the challenges raised will not only open new possibilities in terms of efficiency and applications but will also contribute to forging a solid framework. Consequently, an exciting and transformative future is foreseen for the integration of AI and drones in both society and industry.