The purpose of this paper is to introduce a cost-effective way to design robot vision and control software using Matlab for an autonomous robot designed to compete in the 2004 Intelligent Ground Vehicle Competition (IGVC). The goal of the autonomous challenge event is for the robot to autonomously navigate an outdoor obstacle course bounded by solid and dashed lines on the ground. Visual input data is provided by a DV camcorder at 160 x 120 pixel resolution. The design of this system involved writing an image-processing algorithm using hue, satuaration, and brightness (HSB) color filtering and Matlab image processing functions to extract the centroid, area, and orientation of the connected regions from the scene. These feature vectors are then mapped to linguistic variables that describe the objects in the world environment model. The linguistic variables act as inputs to a fuzzy logic controller designed using the Matlab fuzzy logic toolbox, which provides the knowledge and intelligence component necessary to achieve the desired goal. Java provides the central interface to the robot motion control and image acquisition components. Field test results indicate that the Matlab based solution allows for rapid software design, development and modification of our robot system.
KEYWORDS: Data modeling, Logic, 3D modeling, Laser scanners, Visual process modeling, 3D visualizations, Visualization, Machine vision, Data processing, Artificial intelligence
The purpose of this paper is to describe a theory that defines an open method for solving 3D visual data processing and artificial intelligence problems that is independent of hardware or software implementation. The goal of the theory is to generalize and abstract the process of 3D visual processing so that the method can be applied to a wide variety of 3D visual processing problems. Once the theory is described a heuristic derivation is given. Symbolic processing methods can be generalized into an abstract model composed of eight basic components. The symbolic processing model components are: input data; input data interface; symbolic data library; symbolic data environment space; relationship matrix; symbolic logic driver; output data interface and output data. An obstacle detection and avoidance experiment was constructed to demonstrate the symbolic processing method. The results of the robot obstacle avoidance experiment demonstrated that the mobile robot could successfully navigate the obstacle course using symbolic processing methods for the control software. The significance of the symbolic processing approach is that the method arrived at a solution by using a more formal quantifiable process. Some of the practical applications for this theory are: 3D object recognition, obstacle avoidance, and intelligent robot control.
KEYWORDS: Global Positioning System, Satellites, Receivers, Mobile robots, Robots, Navigation systems, Robotics, Control systems, Tolerancing, Signal processing
The purpose of this paper is to describe the use of Global Positioning Systems (GPS) as geographic information and navigational system for a ground based mobile robot. Several low cost wireless systems are now available for a variety of innovative automobile applications including location, messaging and tracking and security. Experiments were conducted with a test bed mobile robot, Bearcat II, for point-to-point motion using a Motorola GPS in June 2001. The Motorola M12 Oncore GPS system is connected to the Bearcat II main control computer through a RS232 interface. A mapping program is used to define a desired route. Then GPS information may be displayed for verification. However, the GPS information is also used to update the control points of the mobile robot using a reinforcement learning method. Local position updates are also used when found in the environment. The significance of the method is in extending the use of GPS to local vehicle control that requires more resolution that is available from the raw data using the adaptive control method.
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