Have you ever wondered how you can walk or jog, with your head bouncing up and down, while still focusing on a near or far object? Have you noticed how you can do the same thing and judge the distance, the speed of the object and the minute details of that object quickly and accurately? Well, the reason you can do this so well is how your mind uses the burst of image frames from your memory and the instability of your retina to help you quickly fill in the details, while your visual cortex fills in the blanks. , all of this happens in micro-seconds using a brain that consumes barely 20 watts of power. Wow, speaking of state-of-the-art organic design and technology: awesome, my fellow human.
Of course, some animals and birds do this even better than we do, with much smaller brains. Consider whether you want an owl, a hawk or a bald eagle. The phrase “eagle eyes” is appropriate here, think about it. Using biomimicry strategies, perhaps we can make our UAV (unmanned aerial vehicle) or drone video footage more powerful and sharp, and in doing so, consider for a moment how many applications this will affect. How are we doing so far with these concepts? Well, 3-axis gimbals are the most sought after by small drone owners, but why have a 3-axis when you can make a 4.5 or 6-axis gyro stabilization gimbal for better video resolution and accuracy? ? That would certainly help stabilize the video camera, as would quad helicopter designs which are quite stable even in moderate turbulence.
Let’s talk strategy for a moment: to get to that eagle-eyed ability we see in the wild. A patent, “Apparatus and Methods for Vibration Stabilization and Reduction”, US 9277130 B2, duly states: “Currently, there are mainly four methods of vibration damping commonly employed in photography and videography to reduce the effects of vibration on the image : stabilization software, lens stabilization, sensor stabilization, and general shooting rig stabilization.”
What if we also work with visual recognition systems for frame explosion, focusing only on things that meet our mission criteria, “OR” are complete anomalies (misplaced). In a human mind, things out of place often activate brainwave N400, evoking curiosity, nuance, or interest. We can program the same thing using algorithms that require the video camera; investigate, identify and act. However, as Colonel Boyd’s “OODA Loop Strategy” suggests: observe, orient, decide and act. And the fighter pilot who can do it the fastest should win the dogfight as long as he makes good use of his energy and speed. Good advice, even if we borrow it to discuss the best way to program a UAS (unmanned aerial system) to complete a task or mission.
In an article, “Model-Based Video Stabilization for Micro Aerial Vehicles in Real Time,” the abstract states; “The emerging branch of micro aerial vehicles (MAVs) has attracted a great deal of interest for their indoor navigation capabilities, but they require high-quality video for autonomous or teleoperated tasks. A common problem with on-board video quality is the effect of unwanted movement, and there are different approaches to solve it with mechanical stabilizers or video stabilizer software. Very few video stabilizer software can be applied in real time and their algorithms do not consider the intentional movements of the telemarketer.”
Indeed, this is the problem, and it is a real one if we ever hope to send drones out to perform autonomous missions, whether delivering a package or working as a flying security guard for, say, a commercial construction site.
This paper goes on to suggest a way to solve some of these challenges, namely: “A novel technique for real-time video stabilization is introduced with low computational cost, without generating false movements or diminishing performance. Our proposal uses a combination of Geometry Transforms and Outlier Rejection for Robust Interframe Motion Estimation and a Dynamic Model-Based Kalman Filter”.
Now, although there are people working on these things, it is obvious that until sensors, images and equipment improve on such tasks, we will not fulfill the desire to allow drones to work autonomously safely and efficiently. benefits we expect from these technologies in the future. I hope you will consider my thoughts here and some of my recommendations for borrowing strategies from nature to accomplish such goals.
Quotes:
A.) “Vision-Based Detection and Distance Estimation of Micro Unmanned Aerial Vehicles”, by Fatih Gokce, Gokturk Ucoluk, Erol Sahin, and Sinan Kalkan. Sensors 2015, 15(9), 23805-23846; doi: 10.3390/s150923805
B.) Thesis: “Accelerated Tracking of Objects with Local Binary Features”, by Breton Lawrence Minnehan of the Rochester School of Technology; July 2014.
C.) “Model-Based Video Stabilization for Micro Aerial Vehicles in Real Time”, by Wilbert G Aguilar and Cecilio Angulo.
D.) “Real-Time Megapixel Multispectral Bioimaging,” by Jason M. Eichenholz, Nick Barnetta, Yishung Juanga, Dave Fishb, Steve Spanoc, Erik Lindsleyd, and Daniel L. Farkasd.
E.) “Improved Tracking System Based on Microinertial Measurement Unit for Measuring Sensorimotor Responses in Pigeons”, by Noor Aldoumani, Turgut Meydan, Christopher M Dillingham, and Jonathan T Erichsen.
