The topic of ‘self-driving cars’ is huge and hot nowadays. Specially, with the rise of electric cars such as ‘Tesla’, there are already autonomous cars in the real world. Honestly, this is an awe-inspiring and interesting technology to look at. In this article, we are going to discuss how we can implement this technology to create an autonomous UAV/UAS.
The technology in self-driving cars is a little bit complex. The autopilot modes in these units are using AI and ML as the core concepts. But there are many software and hardware are being assisted in the concept of self-driving cars.
for example, self-driving cars use a method called ‘predictive modelling‘ in the autopilot software that can predict pedestrians and other vehicles on the route.
how it works?
Usually, manufacturers and developers of these cars use massive amounts of data to train from image recognition systems to machine learning algorithms, natural language processing and artificial neural networks. each of these models connect to make decisions based on the data they analyse.
hardware devices such as sensors(radar, ultrasonic, Lidar), cameras are continuously gathering data. Neural networks identify the basic patterns from receiving data. Then these data patterns will be added to machine learning algorithms. these algorithms can make decisions for the autopilot system, based on the data patterns. For Example; a camera can detect a traffic light in front of a self-driving car, and then the data will be sent to the neural network to identify it as a traffic light. After the identification, the data will be sent through a list of machine-learning algorithms to create a new decision to perform a new function.
Just like that, a self-driving car can understand and map a virtual environment about the surrounding area. so it can make a path planning to the destination. this method can help a self-driving car understand and select the safest and fastest route to its destination.
Geofencing
Geofencing is a specialized procedure that can be used to create a virtual geographic boundary around a specific object or an area using GPS, Wi-Fi, mobile networks, etc. When a mobile device or RFID tag enters or exits this defined area, a trigger is activated, which can initiate a pre-set action such as sending a notification, alerting a system, or enabling or disabling certain features on a device. It is often used in automation systems. In a self-driving car, this technology can be used for fleet management and vehicle tracking.
A.I algorithms
AI algorithms can be used as a main part of the process of autonomous cars. These algorithms can be either supervised or unsupervised learning algorithms.
1/Object recognition algorithms –
By using specialised supervised algorithms, autonomous systems can analyze and identify various elements or objects from sensor data.
2/ Modeling algorithms
3/Behaviour prediction algorithms
Comparison
Now, let’s discuss the potential comparison between self-driving cars and aerial vehicles.
First, both are autonomous vehicle systems that use AI-based technologies to perform tasks. But, self-driving cars need more complex software and hardware than an average UAV.
Both self-driving cars and UAVs use sensors such as Radar, Lidar, ultrasonic etc, to collect input data about the surroundings. Both systems use cameras to collect data for sending to image recognition algorithms.
But unlike an average UAV, self-driving cars use advanced software to create a virtual map of the surroundings and they also need more powerful processing computers and servers to collect and analyze data for autonomous functions.
both self-driving cars and UAVs can use Geofencing to create virtual geographic environments to detect boundaries around them using GPS or WiFi.
Technology
now, let’s talk about the technologies of a self-driving car that we can implement to create more advanced UAVs.
1. RADAR –
Radar sensors are an essential part of an autonomous vehicle. RADAR is short for Radio Detection and Ranging. radar sensors will deploy radio waves. These waves will hit objects and reflect to the receivers of the vehicle. By analyzing the time delay and frequency shift of the returned signals, radar can determine the distance, speed, and direction of objects relative to the vehicle.
this technology can be used to create the following applications.
Object detection/tracking – the main purpose of this technology is detecting other objects and tracking throughout the time. The main advantage is we can use radar sensors in any weather condition and at any given time.
RADAR can be used to create software that can adjust the speed of the vehicle to keep distance from other objects and avoid collisions.
Radar is also effective in reaching longer ranges than other sensors.
2. LiDAR –
Similar to radar, Lidar stands for Light Detection and ranging and is essential for an Autonomous vehicle. While radar is using radio waves, Lidar is using laser pulses. here is a simple definition. The lidar sensors send laser pulses in near-infrared frequency range and just like radio waves, these pulses bounce back after hitting objects in the direction. By using this process, we can calculate the distance to an object by measuring the time. As the speed of light is a constant(3 x 10^8 m/s), it is very easy to calculate the distance. Also, by sending a lot of laser pulses per second in every direction, Lidar can be used to create a virtual 3D environment/map. This 3D virtual environment is called the ‘point Cloud’.
Unlike radar, Lidar can detect and classify the object. This is far more advantageous than just detecting objects. Also, by generating 3D maps, LiDAR can be used to create detailed navigation systems for safer routes, and also path planning.
While Radar is more effective in any environmental condition, Lidar is more accurate in measurements. A Lidar system can cover every angle(360). LiDAR is working correctly in any light condition from daylight to night time.
3. Cameras
cameras are the most important equipment when it comes to the data collected from visual images. cameras can detect objects, classify them and identify them accurately by using supervised and unsupervised algorithms. cameras also can be used to create a virtual environment in real-time for many autonomous functions.
4. Autopilot software and systems
we all have heard of Tesla’s FSD(Fully-Self_Driving). This is an autopilot system to assist human drivers in switching to autonomous driving. But, we have to keep in mind that Tesla has not yet introduced a fully autonomous vehicle. Tesla FSD still needs significant human presence and interference while driving autonomously.
This same autopilot technology can be used to create far more advanced autonomous aerial vehicles with active human operating assistance. Unlike a car, a UAV is much more easier to handle with autonomous technology.
Mavrick Aerial
Maverick Aerial is a startup company focusing on creating advanced autonomous aerial vehicle systems for delivery and logistics purposes. We are currently developing our first demo-prototype ‘M-1’ multi-drone system.
We will provide more information about the process later in a new article.