The IGVC offers a design experience that is at the very cutting edge of engineering education. It is multidisciplinary, theory-based, hands-on, team implemented, outcome assessed, and based on product realization. It encompasses the very latest technologies impacting industrial development and taps subjects of high interest to students.Students solicit and interact with industrial sponsors who provide component hardware and advice, and in that way get an inside view of industrial design and opportunities for employment.

Problem Statement

A fully autonomous unmanned ground robotic vehicle must negotiate around an outdoor obstacle course under a prescribed time while maintaining a minimum of speed of one mph over a section and a maximum speed limit of five mph, remaining within the lane, negotiating flags and avoiding the obstacles on the course.


Mechanical Design: 4 wheeled differential concept used for motion. Drivetrain comprises of motor, flange coupling, bearing housing and wheels. Adjust height for camera, with suspensions and dampers.
Navigation: A variant of D* algorithm (dynamic A*) is used which demands a grid map to solve and start and end cells in the grid. The module requires the following two inputs: Environment map and Information of next GPS point.There are three heuristics used for doing A* searches namely: Goal cost, Nearest obstacle distance cost and Obstacle boundary cost.
Mapping and Localization: Individual occupancy grid maps generated above are combined together and are stitched to together with a global occupancy grid map. In order to increase the accuracy of our positioning, we use a Kalman Filter to integrate data from the GPS system with data from an INS (Inertial Navigation System) and encoders. Positioning even if no satellite is visible was also possible using this approach.
Vision: The Image Processing module of the vehicle is designed to detect lanes and flags accurately and position them accurately on a local occupancy grid. This occupancy grid is later sent to the mapping module which fuses the grids of Image Processing Module, LIDAR and sends the fused map to the path planning module where the lane information and the flag information is used.
Electrical Design: The vehicle is driven by two 200 W Maxon Motors. A separate power system is there for uninterrupted CPU performance. The same battery system is also used for powering LIDAR and other sensors.


SeDriCa has made India proud after securing 4th position globally and 5th in the Advanced AUTO-NAV Challenge beating teams from top ranked universities like UBC, Georgia Tech and 30 other universities. This has been the best performance by any Asian team till date in this competition.