Implementation Of Autonomous Navigation And Obstacle Avoidance On An Unmanned Ground Vehicle
dc.contributor | Desai, Pranav Naresh | en_US |
dc.date.accessioned | 2010-07-19T19:54:21Z | |
dc.date.accessioned | 2011-08-24T21:43:36Z | |
dc.date.available | 2010-07-19T19:54:21Z | |
dc.date.available | 2011-08-24T21:43:36Z | |
dc.date.issued | 2010-07-19 | |
dc.date.submitted | January 2009 | en_US |
dc.description.abstract | This thesis presents the implementation of a novel distributed embedded systems approach to real-time obstacle avoidance and guidance for an Unmanned Ground Vehicle (UGV). The mobility, real-time, and limited size requirements of UGVs, result in computationally limited and resource constrained hardware platform. The use of distributed computational resources, such as multiple embedded micro-controllers, enables the distribution of the computing resources for obstacle avoidance and guidance system functionalities. The resulting system's complexity is significantly greater than that of a single high performance processor performing all of the above functions. The hardware platform is integrated with sensors and micro-controllers to function as the real-time obstacle avoidance and guidance system for a UGV. The sensors include: a GPS receiver, a digital compass, rotary encoders, and a scanning laser range finder. All sensors have been calibrated and characterized for accuracy and reliability. The obstacle avoidance and guidance functionality executes on MPC555 micro-controller. The data strings from the sensors are parsed on IsoPod, PlugaPod micro-controllers. The required sensor data are passed over to the MPC555 over CAN network as part of a distributed computing architecture. A simulation model consisting of the guidance and navigation algorithm along with the tank model was developed. The simulation model performs obstacle avoidance and waypoint navigation successfully. A real-time model to perform obstacle avoidance and waypoint navigation was developed. The real-time model takes inputs as sensor data, constructs a dynamic map of the environment and outputs control signals to navigate the vehicle through obstacles and towards waypoints. The real-time system successfully performs waypoint navigation. The real-time systems constructs an inaccurate local map in real-time environment. An accurate local map is successfully constructed in simulation from the real world data. Due to the erroneous map constructed in real-time, the real-time system does not successfully navigate through the obstacles. | en_US |
dc.identifier.uri | http://hdl.handle.net/10106/4855 | |
dc.language.iso | EN | en_US |
dc.publisher | Electrical Engineering | en_US |
dc.title | Implementation Of Autonomous Navigation And Obstacle Avoidance On An Unmanned Ground Vehicle | en_US |
dc.type | M.S. | en_US |