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Full-Text Articles in Physical Sciences and Mathematics
Design And Construction Of The Aerobot Robotic Manipulator (Arm), William L. Cochran
Design And Construction Of The Aerobot Robotic Manipulator (Arm), William L. Cochran
Theses and Dissertations
This thesis designed, constructed, and tested a robotic arm for the Aerobot Aerial Robot. The main purpose of the ARM is to enable the Aerobot to retrieve objects for use in an annual robotics competition. Design of the ARM involved synthesizing the characteristics of simplicity, weight, strength, and size. The result was a three-degree-of-freedom manipulator that uses electric motors, cable linkages, and telescoping tubes to access a work space below the Aerobot. Forward and inverse kinematics were investigated to enable automation of the ARM. Data was collected from infrared sensors to validate the model. Manipulation of the ARM is presently …
Discovery Learning In Autonomous Agents Using Genetic Algorithms, Edward O. Gordon
Discovery Learning In Autonomous Agents Using Genetic Algorithms, Edward O. Gordon
Theses and Dissertations
As the new Distributed Interactive Simulation (DIS) draft standard evolves into a useful document and distributed simulations begin to emerge that implement parts of the standard, there is renewed interest in available methods to effectively control autonomous aircraft agents in such a simulated environment. This investigation examines the use of a genetics-based classifier system for agent control. These are robust learning systems that use the adaptive search mechanisms of genetic algorithms to guide the learning system in forming new concepts (decision rules) about its environment. By allowing the rule base to evolve, it adapts agent behavior to environmental changes. Addressed …
Using Discovery-Based Learning To Prove The Behavior Of An Autonomous Agent, David N. Mezera
Using Discovery-Based Learning To Prove The Behavior Of An Autonomous Agent, David N. Mezera
Theses and Dissertations
Computer-generated autonomous agents in simulation often behave predictably and unrealistically. These characteristics make them easy to spot and exploit by human participants in the simulation, when we would prefer the behavior of the agent to be indistinguishable from human behavior. An improvement in behavior might be possible by enlarging the library of responses, giving the agent a richer assortment of tactics to employ during a combat scenario. Machine learning offers an exciting alternative to constructing additional responses by hand by instead allowing the system to improve its own performance with experience. This thesis presents NOSTRUM, a discovery-based learning DBL system …