Stefan Neusser

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The complex parameterbtion and the nonlinear system dynamics of vehicles make the development of a controller by conventional system-theoretical methods difficult. Furthermore, this effort must be spent by experts and be repeated for each new kind of vehicle. We propose a novel solution t o w d autonomous lateral vehicle guidance using a neurocontrolkr.(More)
Two novel neural chips SAND (Simple Applicable Neural Device) and SIOP (Serial Input Operating Parallel) are described. Both are highly usable for hardware triggers in particle physics. The chips are optimized for a high input data rate at a very low cost basis. The performance of a single SAND chip is 200 MOPS due to four parallel 16 bit multipliers and 40(More)
The authors present a neural and fuzzy solution to the collision avoidance problem of an automated guided vehicle (AGV). They describe the AGV and its sensor characteristics. Two methods based on neural networks and fuzzy logic, respectively, have been developed. The advantages and problems of each approach are evaluated. Experiments showed that the(More)
Within the SENROB-project2 (sensor driven robot-vision system), a robot-supported image analysis system has been developed that is able to recognize arbitrarily oriented and positioned workpieces with the help of a camera mounted on a gripping device. To achieve a high recognition rate even in case of lateral displacements, e.g. fovealisation failures, a(More)
An approach to autonomous vehicle navigation using neural networks to imitate human driving behavior is presented. The results are based on the measured actions of a real human driver in a real car. The car environment and the information that is recorded during driving are described. The problem that should be solved by the neural network is discussed. The(More)
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