<p>In this work new artificial learning and innate control mechanisms are proposed for application</p><p>in autonomous behavioral systems for mobile robots. An autonomous system (for mobile robots)</p><p>existent in the literature is enhanced with respect to its capacity of exploring the environment and</p><p>avoiding risky configurations (that lead to collisions with obstacles even after learning). The</p><p>particular autonomous system is based on modular hierarchical neural networks. Initially,the</p><p>autonomous system does not have any knowledge suitable for exploring the environment (and</p><p>capture targets œ foraging). After a period of learning,the system generates efficientobstacle</p><p>avoid ance and target seeking behaviors. Two particular deficiencies of the forme rautonomous</p><p>system (tendency to generate unsuitable cyclic trajectories and ineffectiveness in risky</p><p>configurations) are discussed and the new learning and controltechniques (applied to the</p><p>autonomous system) are verified through simulations. It is shown the effectiveness of the</p><p>proposals: theautonomous system is able to detect unsuitable behaviors (cyclic trajectories) and</p><p>decrease their probability of appearance in the future and the number of collisions in risky</p><p>situations is significantly decreased. Experiments also consider maze environments (with targets</p><p>distant from each other) and dynamic environments (with moving objects).</p>

In this work new artificial learning and innate control mechanisms are proposed for application in autonomous behavioral systems for mobile robots. An autonomous system (for mobile robots) existent in the literature is enhanced with respect to its capacity of exploring the environment and avoiding risky configurations (that lead to collisions with obstacles even after learning). The particular autonomous system is based on modular hierarchical neural networks. Initially,the autonomous system does not have any knowledge suitable for exploring the environment (and capture targets œ foraging). After a period of learning,the system generates efficientobstacle avoid ance and target seeking behaviors. Two particular deficiencies of the forme rautonomous system (tendency to generate unsuitable cyclic trajectories and ineffectiveness in risky configurations) are discussed and the new learning and controltechniques (applied to the autonomous system) are verified through simulations. It is shown the effectiveness of the proposals: theautonomous system is able to detect unsuitable behaviors (cyclic trajectories) and decrease their probability of appearance in the future and the number of collisions in risky situations is significantly decreased. Experiments also consider maze environments (with targets distant from each other) and dynamic environments (with moving objects).

<p>This Master’s thesis describes an evaluation of the stream-based knowledge pro-cessing middleware framework DyKnow in multi-UAV traffic monitoring applica-tions performed at Saab Aerosystems. The purpose of DyKnow is “to providegeneric and well-structured software support for the processes involved in gen-erating state, object, and event abstractions about the environments of complexsystems." It does this by providing the concepts of streams, sources, computa-tional units (CUs), entity frames and chronicles.</p><p>This evaluation is divided into three parts: A general quality evaluation ofDyKnow using the ISO 9126-1 quality model, a discussion of a series of questionsregarding the specific use and functionality of DyKnow and last, a performanceevaluation. To perform parts of this evaluation, a test application implementinga traffic monitoring scenario was developed using DyKnow and the Java AgentDEvelopment Framework (JADE).</p><p>The quality evaluation shows that while DyKnow suffers on the usability side,the suitability, accuracy and interoperability were all given high marks.</p><p>The results of the performance evaluation high-lights the factors that affect thememory and CPU requirements of DyKnow. It is shown that the most significantfactor in the demand placed on the CPU is the number of CUs and streams. Italso shows that DyKnow may suffer dataloss and severe slowdown if the CPU istoo heavily utilized. However, a reasonably sized DyKnow application, such as thescenario implemented in this report, should run without problems on systems atleast half as fast as the one used in the tests.</p>

This Master’s thesis describes an evaluation of the stream-based knowledge pro-cessing middleware framework DyKnow in multi-UAV traffic monitoring applica-tions performed at Saab Aerosystems. The purpose of DyKnow is “to providegeneric and well-structured software support for the processes involved in gen-erating state, object, and event abstractions about the environments of complexsystems." It does this by providing the concepts of streams, sources, computa-tional units (CUs), entity frames and chronicles. This evaluation is divided into three parts: A general quality evaluation ofDyKnow using the ISO 9126-1 quality model, a discussion of a series of questionsregarding the specific use and functionality of DyKnow and last, a performanceevaluation. To perform parts of this evaluation, a test application implementinga traffic monitoring scenario was developed using DyKnow and the Java AgentDEvelopment Framework (JADE). The quality evaluation shows that while DyKnow suffers on the usability side,the suitability, accuracy and interoperability were all given high marks. The results of the performance evaluation high-lights the factors that affect thememory and CPU requirements of DyKnow. It is shown that the most significantfactor in the demand placed on the CPU is the number of CUs and streams. Italso shows that DyKnow may suffer dataloss and severe slowdown if the CPU istoo heavily utilized. However, a reasonably sized DyKnow application, such as thescenario implemented in this report, should run without problems on systems atleast half as fast as the one used in the tests.