Exploratory research on the design of a modular autonomous mobile robot controller for steering and speed control is described. The high level control of the robot incorporates a fuzzy logic approach. Steering and spe...
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Exploratory research on the design of a modular autonomous mobile robot controller for steering and speed control is described. The high level control of the robot incorporates a fuzzy logic approach. Steering and speed control are achieved using a three-axis Galil motion controller. The steering mechanism and the speed control involve the parallel control of the robot's two front wheels based on decisions made by the upper level fuzzy logic to guide the robot in a desired direction to follow a specific path and avoid obstacles in that path. The steering motors are Electrocraft brush DC motor. The BDC amplifiers are run in the current loop mode. The overall control is supervised by a personal computer through the multi-axis controller. The system has been simulated on Matlab and Simulink and optimal values for the digital gains were achieved for desired control. Testing of these systems has been done in a laboratory setting as well as on an outside track with positive results.
A truly autonomous robot must sense its environment and react appropriately. These issues attain greater importance in an outdoor, variable environment. Previous mobile robot perception systems have relied on hand-cod...
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A truly autonomous robot must sense its environment and react appropriately. These issues attain greater importance in an outdoor, variable environment. Previous mobile robot perception systems have relied on hand-coded algorithms for processing sensor information. Recent techniques involve the use of artificial neural networks to process sensor data for mobile robot guidance. A comparison of a fuzzy logic control for an AGV and a neural network perception is described in this work. A mobile robot test bed has been constructed using a golf cart base. The test bed has a fuzzy logic controller which uses both vision and obstacle information and provides the steering and speed controls to the robot. A feed-forward neural network is described to guide the robot using vision and range data. Suitable criteria for comparison will be formulated and the hand-coded system compared with a connectionist model. A comparison of the two systems, with performance, efficiency and reliability as the criteria, will be achieved. The significance of this work is that it provides comparative tradeoffs on two important robot guidance methods.
This research focuses on applications based on wireless monitoring and robot control, utilizing motion image and augmented reality. These applications include remote services and surveillance-related functions such as...
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This research focuses on applications based on wireless monitoring and robot control, utilizing motion image and augmented reality. These applications include remote services and surveillance-related functions such as remote monitoring. A remote service can be, for example, a way to deliver products at a hospital or old people's home. Due to the mobile nature of the system, monitoring at places with privacy concerns is possible. On the other hand, mobility demands wireless communications. Suitable and present technologies for wireless video transfer are weighted. Identification of objects with the help of Radio Frequency Identifying (RFID) technology and facial recognition results in intelligent actions, for example, where the control of a robot does not require extensive workload from the user. In other words, tasks can be partially autonomous. RFID can be also used in augmentation of the video view with virtual objects. As a real-life experiment, a prototype environment is being constructed that consists of a robot equipped with a video camera and wireless links to the network and multimedia computer.
We present new test results for our active object recognition algorithms. The algorithms are used to classify and estimate the pose of objects in different stable rest positions and automatically re-position the camer...
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We present new test results for our active object recognition algorithms. The algorithms are used to classify and estimate the pose of objects in different stable rest positions and automatically re-position the camera if the class or pose of an object is ambiguous in a given image. Multiple object views are now used in determining both the final object class and pose estimate;previously, multiple views were used for classification only. A feature space trajectory (FST) in eigenspace is used to represent 3-D distorted views of an object. FSTs are constructed using images rendered from solid models. We discuss lighting and material settings for photorealism in the rendering process. The FSTs are analyzed to determine the camera positions that best resolve ambiguities. Real objects are recognized from intensity images using the FST representation derived from rendered imagery.
A computervision algorithm was developed to detect moving aircraft located in video images. Using a gradient-based approach, the algorithm computes optical flow vectors in each frame of the sequence. Vectors with sim...
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A computervision algorithm was developed to detect moving aircraft located in video images. Using a gradient-based approach, the algorithm computes optical flow vectors in each frame of the sequence. Vectors with similar characteristics (location, magnitude, and direction) are clustered together using a spatial consistency test. Vectors that pass the spatial consistency test are extended temporally to make predictions about the optical flow locations, magnitudes, and directions in subsequent frames. The actual optical flow vectors that are consistent with the predicted vectors are labeled as vectors associated with a moving target. The algorithm was tested on images obtained with a video camera mounted below the nose of a Boeing 737. The algorithm correctly detected an aircraft from a distance of one mile in over 80% of the frames with zero false alarms.
The purpose of this paper is to present a new approach for path planing of a mobile robot in static outdoor environments. A simple sensor model is developed for fast acquisition of environment information. The obstacl...
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The purpose of this paper is to present a new approach for path planing of a mobile robot in static outdoor environments. A simple sensor model is developed for fast acquisition of environment information. The obstacle avoidance system is based on a micro-controller interfaced with multiple ultrasonic transducers with a rotating motor. Using sonar readings and environment knowledge, a local map based on weight evaluation function is built for the robot path planing. The path planner finds the local optimal path using the A* search algorithm. The robot is trained to learn a goal-directed task under adequate supervision. The simulation experiments show that a robot, utilizing our neural network scheme, can learn tasks of obstacle avoidance in the work space of a certain geometrical complexity. The result shows that the proposed algorithm can be efficiently implemented in an outdoor environment.
A single rotating sonar element is used with a restricted angle of sweep to obtain readings to develop a range map for the unobstructed path of an autonomous guided vehicle (AGV). A Polaroid ultrasound transducer elem...
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A single rotating sonar element is used with a restricted angle of sweep to obtain readings to develop a range map for the unobstructed path of an autonomous guided vehicle (AGV). A Polaroid ultrasound transducer element is mounted on a micromotor with an encoder feedback. The motion of this motor is controlled using a Galil DMC 1000 motion control board. The encoder is interfaced with the DMC 1000 board using an intermediate IMC 1100 break-out board. By adjusting the parameters of the Polaroid element, it is possible to obtain range readings at known angles with respect to the center of the robot. The readings are mapped to obtain a range map of the unobstructed path in front of the robot. The idea can be extended to a 360 degree mapping by changing the assembly level programming on the Galil Motion control board. Such a system would be compact and reliable over a range of environments and AGV applications.
For faithful 3-D reconstruction of objects with smooth surfaces, stereo visiontechniques rely heavily on the surface texture. However, most of the real life objects lack this feature. To `sharpen' the textural co...
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For faithful 3-D reconstruction of objects with smooth surfaces, stereo visiontechniques rely heavily on the surface texture. However, most of the real life objects lack this feature. To `sharpen' the textural content of visual surfaces, a structured-light sensing configuration has been used. This technique can be used to enhance the features used in solving the correspondence problem in computational stereo vision. A light pattern is projected to encode the smooth surface of the object. The observed light pattern is then used to compute surface properties. We present a simple design for a trinocular vision system with structured light using off-the-shelf components. The processing pipeline of the system consists of four stages. First, the light pattern is detected in the captured images. Second, the pattern is skeletonized using connected component labeling. Third, a maximum-weighted bipartite technique is used to do the matching. Finally, a global surface fitting technique is used to integrate the reconstruction from different views in one frame and fit a mesh of triangles to the integrated data. Results using real images are promising. The advantages of this system lie in its design simplicity, low cost and the potential for fast and parallel implementation.
Quality control in industrial application has greatly benefited from the development of tools like artificial vision. In order to obtain a good quality image of the object under investigation, the first step is to use...
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Quality control in industrial application has greatly benefited from the development of tools like artificial vision. In order to obtain a good quality image of the object under investigation, the first step is to use a good lighting system. This paper presents a reliable method which allows to compare several lighting with respect to their capabilities of bringing out defects. This study has been led on textured industrial parts on which four types of defects have to be detected: smooth surfaces, bumps, lacks of material and hollows knocked surfaces. The aim is to determine the best lighting among various experimental sets. This method has two stages: the first one is a definition, according to the knowledge and the shape of the defects, of a pertinent attribute vector which components are defect sensitive. In the second step, discrimination power property of this vector is computed and compared under various illumination using Parzen's kernel. This method insures a well-suited illumination in numerous applications in defects detection and leads to an efficient set of lighting system and segmentation's parameters. Work is under way to generalize this method to multidimensional cases in order to allow interactions between components of the attribute vector.
The paper describes an Active vision System whose design assumes a distinction between fast or reactive and slow or background processes. Fast processes need to operate in cycles with critical timeouts that may affect...
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The paper describes an Active vision System whose design assumes a distinction between fast or reactive and slow or background processes. Fast processes need to operate in cycles with critical timeouts that may affect system stability. While slow processes, though necessary, do not compromise system stability if its execution is delayed. Based on this simple taxonomy, a control architecture has been proposed and a prototype implemented that is able to track people in real-time with a robotic head while trying to identify the target. In this system, the tracking module is considered as the reactive part of the system while person identification is considered a background task. This demonstrator has been developed using a new generation DSP (TMS320C80) as a specialized coprocessor to deal with fast processes, and a commercial robotic head with a dedicated DSP-based motor controller. These subsystems are hosted by a standard Pentium-Pro PC running Windows NT where slow processes are executed. The flexibility achieved in the design phase and the preliminary results obtained so far seem to validate the approach followed to integrate time-critical and slow tasks on a heterogeneous hardware platform.
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