Projects

This project was part of the solution developed for the mid prep problem statement in Inter IIT Tech Meet 2022 given by Jaguar Land Rover company. The aim was to develop a practical CAD design in SOLIDWORKS for the autonomous battery swapping problem for electric vehicles. The arm had to be developed for given dimensions choosing the right working space, the constraints like the cost, practicality, size, etc and many other factors suggested by the company too. Also control systems had to be implemented in MATLAB and Simulink and the plots were explained in the final design report of the robotic arm designed. The animation video for this project is also available on the YouTube channel.

This project was part of the eYRC IITB 2022-23 competition where the team reached the finals completing the final task 6. We had to develop the drone was remote rescue operations identifying anomalies like yellow colored boxes randomly searching a given search space (arena) and take its images, save them and send to the ground control station which would then do the feature matching with a .tif satellite image and finally georeference the image giving the right longitude and latitude coordinates and plotting them in QGIS.

This project was developed for autonomous warehouse management where in pick and place operations could be done autonomusly. This powerful bot weighed about 3 kilograms with a scissor jack weld on top powered by a portable powerful gear motor. The localisation was done through computer vision using overhead cameras in the arena and the Aruco markers were used for pose estimation and navigation setting target waypoints for the robot.

An autonomous four wheeled ground robot was used to test Google Cartographer SLAM method in indoor environment in simulation using ROS and Gazebo. The aim was to choose the best 2D LiDAR SLAM method in practice for four wheeled ground robots. GMapping, Karto SLAM, Hector SLAM and Google Cartographer SLAM were tested in the same conditions out of which Google Cartographer SLAM performed the best though it required LIDAR, Odometry, Encoder, etc data for proper results according to literature.

Terrace Farming Robot is an autonomous robot which can assist farmers of hilly areas in terrace farming. It was built essentially as an Autonomous Stair Climbing Robot which could ascend and descend stair of the height of about 40cm high using a Chain Slider Mechanism. It can autonomously also perform ploughing, seeding and harvesting in the steps of terrace farming.

This project initially started off as a python script which out capture the faces of a 2x2x2 Rubik’s Cube and output the moves required to solve it. But soon, it felt incomplete and thus, the electronic and mechanical components were added.
The bot houses a camera at the back which captures the faces of the cube and an Arduino board which communicates with the python interface and drives the grippers accordingly. Each gripper is capable of responding to three types of commands: grip, ungrip and rotate. The face detection algorithm is independent of the colour scheme of the cube. The solution is computed by using a graph based search, the shortest solution is computed (11 face-turns of the cube, at most) for any given state of the cube.

Aerial Imaging plays an important role in military surveillance as well as in search and rescue operations. This project aims to develop an autonomous navigation system for aerial vehicles. Sensors like inertial measurement unit and GPS help in achieving flight stability and immunity to drift due to winds. An efficient embedded system needs to be designed in order to incorporate neural networks and image processing techniques.
Autonomous navigation demands a real-time spatial (object detection) and temporal (object tracking) analysis of the video feed. The You Only Look Once (YOLO) algorithm based on the Darknet Neural Network has been used for object detection. Channel and Spatial Reliability Tracker (CSRT) and Kalmann Filters have been used for object tracking. Occlusions have been handled by matching features extracted from the object being tracked. The neural network has been trained on the CARPK Dataset which contains aerial images and annotations for parked cars

Most of the crutches available in the market have many drawbacks of its own. The challenge was to design a new set of ergonomic crutches which can solve the problems like a disabled person with regular crutches cannot climb stairs easily. No hands-free movements are possible, making it difficult to grab anything else, prolong use of crutches cause muscle pain and even muscle paralysis, portability issues and many more.
The new design of the ergonomic crutches uniquely tackle this problems. The armrest is at an angle to distribute the load evenly along the forearm. The pivot-less cuff allows full range of motion while maintaining one-piece design. The handle has an adjustable length which slides into the armrest providing a single solution for people with different arm length. The thigh cuff offers additional support to the body by reducing body load directly from the forearm and redistributing it through the thigh and transferring back to the ground. This reduces the stress from the forearm, and it prevents hand segs. Since this crutches design provides support at two points, a person can easily climb the stairs with comfort and ease without losing balance. Its design permits the person to fold it to half of its length, which makes it easy to store and portable. Through adjustment, pin person can adjust the height of the clutches for his/her needs

This bot was specifically made for the event IARC of Techkriti '19. The problem statement was to solve a maze with additional functions like reading data from black and white grids called nodes, controlling speed of the bot as per the data or choosing the right path using the data, stopping at red signals and obstacles. We used 2 IR sensors to solve the maze using the left-hand algorithm. Four IRs were used to read data from nodes and 4 more were used to distinguish a node from a path. We used an RGB colour sensor to detect Red LED signals and an Ultrasonic sensor to detect obstacles. We used a hall-effect sensor and magnets to calculate the bot's speed and adjusted it to the desired speed using PWM. Finally, a compass-sensor was used to calculate the angle turned by our bot and turn until we reached the correct angle where our correct path was. We also used an LCD display to display the values we read from a node.