docs: edits to anticorona drones article (#318)

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Co-authored-by: Oleg Kalachev <okalachev@gmail.com>

docs/en/anticorona_drones.md

@@ -1,11 +1,15 @@
 # Drones to fight Corona
 
-[CopterHack-2021](copterhack2021.md), team: **Drones to fight Corona**. E-mail: [y.suhorukov@innopolis.ru](mailto:y.suhorukov@innopolis.ru).
+[CopterHack-2021](copterhack2021.md)
+
+team: **Drones to fight Corona**
 
 ## Team
 
 * Daria Miklashevskaya
+[d.miklashevskaya@innopolis.ru](mailto:d.miklashevskaya@innopolis.ru)
 * Yuriy Sukhorukov
+[y.suhorukov@innopolis.ru](mailto:y.suhorukov@innopolis.ru)
 
 Innopolis University, B17-DS-II, B17-RO-I
 
@@ -17,29 +21,27 @@ There are laws in place to enforce mask wearing, but there is just not enough co
 
 ## Custom airframe
 
-Since we do not have the Clover drone, we will use an airframe built and designed by us.
+Since we do not have the Clover drone, we will use an airframe built and designed by us. Our system is designed to be platform-independent, so it can be installed on almost everything, even VTOL aircrafts.
 
-The main idea is to use truss structure, because it works well against twisting and warping.
+The main idea is to use truss structure, because it works well against twisting and warping, besides, it can be assembled out of carbon tubes relatively easily.
 
 <img src="../assets/anticorona/drone_frame.jpg" title="Frame">
 
-The main advantage of such a system is that it distributes impact between beams and effectively dissipates it. However, engine mounts are not impact-proof, this is because we sacrifice them, but save much more expensive and not-readily-available engine. This is why mounts are quickly-replaceable (only 3 screws) and mate of cheap PLA plastic.
+The main advantage of such a system is that it distributes the impact between beams and effectively dissipates it. Engine mounts, however, are not impact-proof because they are specifically designed to break but save much more expensive and not-readily-available engines. This is why mounts are quickly-replaceable (only 3 screws) and made of cheap PLA plastic.
 
-The space inside the central rhombus is occupied by the on-board equipment: batteries, PX4 flight controller, Jetson Xavier NX / AGX.
+The space inside the central rhombus is occupied by the on-board equipment: batteries, PX4 flight controller, Jetson Xavier NX / AGX, power electronics, sensory equipment.
 
-As it is shown on this picture, AGX is mounted on top of the platform, while NX can be mounted on the bottom, and completely protected from any damage by beams and landing legs.
+As it is shown on this picture, computers can be mounted on the bottom and completely protected by legs and the truss structure from any collision damage. Jetson AGX is marked with arrow. Almost invisible, isn't it?
 
 <img src="../assets/anticorona/drone_with_jetson.jpg" title="AGX is safe">
 
-All sensory equipment, like cameras, rangefinder, etc can be easily mounted on the beams with special connectors, which provide flexibility because you can fine-tune camera angle or position before tightening screws and fixing it firmly in place, which is especially relevant for tracker-cameras.
+All sensory equipment, like cameras, rangefinder, etc can be easily mounted on the beams with special bracket connectors. This type of connection provides flexibility because you can fine-tune camera angle or position before tightening screws and fixing it firmly in place, which is especially relevant for tracker-cameras.
 
-We used two T-265 cameras for visual odometry (fuse them together with Kalman filter). And one D-435 depth camera for both video input for neural net and for map-building (collision avoidance). Also, we use dampers to prevent odometry drift.
-
-And one D-435 depth camera for both video input for neural net and for map-building (collision avoidance).
+We used one T-265 camera for visual odometry and one D-435 depth camera for both video input for neural net and for map-building (collision avoidance). T-265 suffers from "odometry drift" especially when engines are beat-up, which eventually happens after a number of crashes, so we have incorporated dampers to solve this problem.
 
 <img src="../assets/anticorona/camera.jpg" title="Begone odometry drift" width=400 class="zoom center">
 
-Here are the photo of assembled drone:
+Finally, the drone with all equipment installed looks like this
 
 <img src="../assets/anticorona/drone_assembled.jpg" title="Assembled drone" width=400 class="zoom center">
 
@@ -52,15 +54,38 @@ We have split our drone software into two modules:
 * Pipeline, which manages communication with ROS.
 * Neural net module, which talks with the pipeline via sockets.
 
-The reason for this is simple - ROS supports ony Python 2, and I do not feel like I'm building it with Python 3 because it is a bit troublesome. Our neural net, on the other hand, uses Pytorch which is Python 3 only.
+The reason for this is simple - ROS supports only Python 2, and I do not feel like I'm building ROS with Python 3 because it is a bit troublesome. Our neural net, on the other hand, uses Pytorch which is Python 3 only, so the only way to run them both is to use inter-process communication of some sort.
 
-But apart from compatibility issue, such an arrangement allows us to run inference module anywhere we want, like in more powerful desktop PC or even somewhere in the cloud.
+Apart from compatibility issue, such an arrangement allows us to run inference module anywhere we want, e.g. on more powerful desktop PC or even somewhere in the cloud (google collab? Why not!).
 
-This means that we can make our drone lighter by excluding heavy on-board computer like Jetson AGX / NX and replacing it with something light like Raspberry Pi. Pipeline image is made as lightweight as possible, so it should be runnable even on really weak computers.
+This means that we can make our drone lighter by excluding heavy on-board computer and replacing it with something light like Raspberry Pi. Pipeline image is made as lightweight as possible, so it should be runnable even on really weak computers.
 
-## Neural net
+More detailed instructions on how to build and run our software are available in our [Gitlab repo](https://gitlab.com/k0t1k/thegreateye/-/tree/master).
 
-We use 3rd version of YoLo neural network, pretrained on customly-built dataset for 50 epochs.
+## Neural net [^1]
+
+We use 3rd version of YoLo neural network, pretrained on custom dataset for 50 epochs.
+
+It runs 10-15 FPS on Jetson NX, which is enough for our task
+
+## Exploration and collision avoidance
+
+To make our drone useful and to operate it safely, we should somehow make the drone aware of its current position and surrounding objects. There are two ways we can solve this problem
+
+* Use pre-built map as a ground truth and then calculate the position with e.g. Particle Filter
+* Build map on-the-fly, while avoiding collisions and moving towards the goal
+
+The second approach is more robust, because it does not rely on any external map, which can be erroneous or just missing and hence we opted for it.
+
+We use a path planner, described in [^2]. In this paper Receding
+Horizon Next-Best-View Planner is presented, which uses Rapidly-exploring Random trees to navigate and explore the environment. It yields the following results, here is the occupancy map and the corresponding tunnel as it is seen by human being:
+
+<div class="image-group">
+  <img src="../assets/anticorona/robot_view.jpg" title="Mask release manufactured" width=300 class="zoom center">
+  <img src="../assets/anticorona/human_view.jpg" title="Mask release in cad" width=300 class="zoom center">
+</div>
+
+The algorithm is lightweight, so even the small computer like Latte Panda can run it with high enough frequency, and since it is CPU-bound, it will not compete for resources with the neural net, which is almost entirely GPU-bound.
 
 ## Mask release
 
@@ -70,16 +95,34 @@ Detecting people without masks is cool, no doubt.
 
 But we want not only to detect them but to give him a mask as well, so, we have built this system that can give a mask to person.
 
-<img src="../assets/anticorona/release.jpg" title="Mask release in cad" width=300 class="zoom center">
-
-<img src="../assets/anticorona/release_Cad.jpg" title="Mask release manufactured" width=300 class="zoom center">
+<div class="image-group">
+  <img src="../assets/anticorona/release.jpg" title="Mask release in cad" width=300 class="zoom center">
+  <img src="../assets/anticorona/release_Cad.jpg" title="Mask release manufactured" width=300 class="zoom center">
+</div>
 
 This device looks like a regular firearm mag, and functions exactly in the same way. Masks can be loaded into containers made out of 20ml syringe barrels.
 
 This device needs further engineering because current iterations are too fragile and unreliable, probably the best solution will be to use linear actuator and push the "casing" out of the action, like in actual firearm.
 
+## 3D models
+
+All the 3D models used to build this cute drone can be found in our [gdrive](https://drive.google.com/drive/folders/1tR5ePX24_i9tpllOPMcnZIUfse89c1YI?usp=sharing).
+
 ## Final thoughts
 
-This project will still be useful even when the Coronavirus crisis will be over. It can deliver some small objects, like cosmetics or shaving blades to the customers' door, the task that currently is done by a human courier. This service (with shaving blades), when a guy comes and brings a new set of shaving blades every week is popular in US and UK, so why not try to automate it a bit. The working principle and hardware will remain the same, but software will need an update.
+We all hope that Corona crisis will soon be over, and when it will be finally over, our drones will be still useful. We can deliver some small objects, like cosmetics or shaving blades to the customers' door, the task that currently is done by a human courier. This service (with shaving blades), when a guy comes and brings a new set of shaving blades every week is very popular in US and UK, so why not try to automate it.
+
+This is an MVP, so some improvements are to be done. For example, payload refill and battery swaps are done manually for now, but this task should be automated. Actually, some work is already done in this direction:
+
+<img src="../assets/anticorona/charging_drone.jpg" title="Mask release manufactured" width=300 class="zoom center">
+
+Drone lands on a landing platform with special hooks which will connect to four metallic contacts (highlighted) and charge the battery. In future we want to change battery, not just charge it, but the mechanism is very complex and requires making custom batteries and battery mounts, also, it constraints placement of cameras, payload and so on.
+
+For the general-purpose delivery drone, the working principle and hardware will remain the same, but software (neural net detector) will need an update.
 
 Stay safe folks!
+
+## References
+
+[^1]: Massagué Respall, Victor & Devitt, Dmitry & Fedorenko, Roman. (2020). Unmanned Aerial Vehicle Path Planning for Exploration Mapping. 1-6. 10.1109/NIR50484.2020.9290232.
+[^2]: Nisarg Pethani & Harshal Vora. (2020) https://github.com/NisargPethani/Face-Mask-Detection-using-YOLO-v3