Click Here for Abhyast Phase VII Problem Statement
Abhyast is an autonomous navigation vehicle. In previous phases, Autonomous robots have been developed which followed a particular direction and avoid all obstacles that come in its route. Various methods and algorithms have been implemented to improve the navigation through unknown environment. This time, the localization and mapping of the area would be done through an autonomous swarm of aerial vehicles. The ground station will utilize this data to identify the suspicious objects and plan an optimal path over which the controller will maneuver the ground robot using the live feed obtained from the cameras onboard, to acheive the required task.
The AGV derives its modular design from the famous Arthon R4075A. Each module would consist of a pair of wheels. While the master robot will have a six wheel mechanism, the two slave robots would have four wheel drive. Doing so provides the vehicles a better capability to overcome variety of obstacles and climb stairs.
Quadcopter is a multirotor helicopter that can be lifted and propelled using four rotors. From a mechanical and con-trols perspective, key emphasis lies on improving the load sensitivity so that better processors and sensors can be in-stalled along with altitude stabilization to minimize sensor noise.
a. On Quadrotor:
i. Laser range finder: For obstacles avoidance and indoor 3-D map building.
ii. Inertial Measurement Unit: For measurements corresponding to orientation.
b. On AGV:
i. Wheel encoders on AGV: For odometry data to be used in motion planning.
ii. Digital compass: To improve estimate on robot position.
2. Computing: To carry out real-time control and decision-making, the QR and AVGs would have on-board microprocessor.
3. Communication: For the QR to interact with AVGs via a ground station, Wi-Fi modules would be used rather than X-Bees as the former provides better range and lesser lag.
Three algorithms can be implemented for optimal path for traversal.
SELF(Sparse Information Extended Filter) SLAM algorithm uses sensor information and scanners to store data in a sparse in-formation matrix and records a distance vector.
RRT(Rapidly Exploring Random Tree) randomly build a space tree from samples drawn from the search space where nodes represent the safe traversal locations for the bot.
A* is used in path-finding and graph tra-versal. It is used in traversal of path for robot between multiple nodes.