Combining a Modified Vector Field Histogram Algorithm and Real- time Image Processing for Unknown Environment Navigation

Q is an unmanned ground vehicle designed to compete in the Autonomous and Navigation Challenges of the AUVSI Intelligent Ground Vehicle Competition (IGVC). Built on a base platform of a modified PerMobil Trax off-road wheel chair frame, and running off a Dell Inspiron D820 laptop with an Intel t7400 Core 2 Duo Processor, Q gathers information from a SICK laser range finder (LRF), video cameras, differential GPS, and digital compass to localize its behavior and map out its navigational path. This behavior is handled by intelligent closed loop speed control and robust sensor data processing algorithms. In the Autonomous challenge, data taken from two IEEE 1394 cameras and the LRF are integrated and plotted on a custom-defined occupancy grid and converted into a histogram which is analyzed for openings between obstacles. The image processing algorithm consists of a series of steps involving plane extraction, normalizing of the image histogram for an effective dynamic thresholding, texture and morphological analysis and particle filtering to allow optimum operation at varying ambient conditions. In the Navigation Challenge, a modified Vector Field Histogram (VFH) algorithm is combined with an auto-regressive path planning model for obstacle avoidance and better localization. Also, Q features the Joint Architecture for Unmanned Systems (JAUS) Level 3 compliance. All algorithms are developed and implemented using National Instruments (NI) hardware and LabVIEW software. The paper will focus on explaining the various algorithms that make up Q’’s intelligence and the different ways and modes of their implementation.