Implementation of autonomous navigation algorithms on an urban robot (iRobot's packbot)

The concept of an Autonomous Mobile Robot has been around for some years. The proof of concept and some prototypes already exist. But still, the goals and parameters are mostly entered by the human operators. Determining and planning a safe path among obstacles is the key to autonomous vehicles....

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Bibliographic Details
Main Author: Chan, Jian Wee.
Other Authors: Wijerupage Sardha Wijesoma
Format: Final Year Project
Language:English
Published: 2010
Subjects:
Online Access:http://hdl.handle.net/10356/40798
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Institution: Nanyang Technological University
Language: English
Description
Summary:The concept of an Autonomous Mobile Robot has been around for some years. The proof of concept and some prototypes already exist. But still, the goals and parameters are mostly entered by the human operators. Determining and planning a safe path among obstacles is the key to autonomous vehicles. Motion-path planning algorithms can be classified into two types: global path planning and local path planning. Global path planning more known information is needed and the environment needs to be definite. Local path planning itself requires less information, and the environment can vary. Among the existing local path planning algorithm, Vector Field Histogram (VFH) uses statistical representation of the vehicle’s environment through a histogram grid. An improved method, VFH+, takes into decision of the size and shape of the vehicle, and also consideres the smoothness of the path taken while sending steering commands to the onboard steering and drive controller. The objective of this project is to implement VFH+ algorithm in an open sourced program named Orca Robotics, running in conjunction with the Player Project (another open sourced robotic development tool), to test it for any weakness, to streamline and improve it if possible. This report will present information on the software used, configurations implemented, steps taken in the testing and weakness found, if any. This report will also include any streamlining done or improvements done to the algorithm.