Ananthan's Blog

In order to counter the rotating torque from the previous single rotor version, one more propeller is added. The second propeller rotates counter clockwise and both torques will cancel each other (Only if both propellers are rotating at same speed). This the video of it flying with restriction. I have attached a a small weight to its bottom. Even though the counter torque is cancelled, it can't be controlled. Very small variation in alignment will drive the MAV crazy. This is an attempt to control the drone remotely with a joystick. Had changed the body from plastic (from plastic IC tube) to general purpose PCB board. The individual propellers are controlled from a joystick (logitech f710). The controller is an esp8266 es-11 WiFi chip. The telemetry data are send using a python program that run on my computer. Both the WiFi chip and my computer are on the same network.

This is a small MAV (Micro Areal Vehicle) I created out of simple household stuffs. Though this one can't be controlled, it is fun to watch it fly. :) :) :) I have loosely connected the power supply so that once it start flying, the counter rotating torque forces the whole MAV to rotate. So after a few sec the wire connecting power supply (battery) will automatically get removed and I can catch the MAV before it crash into walls. :) This one is a slow motion video of it flying. It is hard to video it while flying. Since it is not stable it will crash itself into walls after a few seconds. I am trying to get it fly vertically upwards without any deviation. See this instructable ( https://www.instructables.com/id/Home-Made-Micro-Aerial-Vehicle-MAV-in-5-Min/ ) for more videos/photos.

This is a project I had completed while doing internship at EY. The idea is to automate inventory and the stock checking process without any human intervention. Today these are all done using human resource. This is a really tiresome work because it involves manually inspecting each pallet (box) and read the QR/Bar code associated with it.  Instead consider a swarm of drones (a dozen or a hundred drones) go and check each pallet and complete the whole inventory and stock checking process in just a few minutes. This is a proof of concept design in which a drone uses a blueprint to travel a particular path and then reads and analyse each stock item and update it in the warehouse management system. All these things are done in real time. The drone used here is Parrot bebop and is implemented in ROS. Instead of actual boxes, QR code is fixed to symbolise each palette. The ROS implementation makes the project scalable. The video of the project is shown below.

Parrot Bebop drone is much stable while comparing with the most widely used AR Drone. Due to ROS support  and its open API AR Drone was widely used in the research community. Autonomy Lab had released the Bebop driver for ROS support and am planning to do some fun projects with it. This video is showing bebop taking off and landing where the commands are send from ROS. Bebop is fun to work with but flight time of just 10 min is really annoying (Bebop 2 has twice the battery life but doesn't have one to try out).

The drone is able to search and find the landing platform. Once it finds the landing platform it will try to orient itself and land in the platform. Both of these are implemented using Neural Network. The tag used in the landing platform is from the ros package ar_track_alvar.

This video is a part of an AR Drone autonomous landing project. Here the AR Drone is controlling itself so that the landing platform is exactly in the middle so that it can  initiate a proper landing. Here AR Drone is also tracking the Tag embedded on the landing platform (Not using the inbuilt tag detection. The tag that is being used here is from the ar_track_alvar package.). This is implemented using Neural Network. The video is not a part of a completed project. (The video is playing at a slightly faster rate.) [NB: As you can see the drone is not orienting itself as it is trying to reach just above the tag. This is only because I didn't train it to do so.] Packages used: ardrone_autonomy, tum_simulator, ar_track_alvar, custom packages (some packages I created for collecting data for training, republishing twist cmd_vel with time stamp, implementation of NN, custom launch files and worlds.)