Joshua P. Hecker

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Evolutionary algorithms can adapt the behavior of individual agents to maximize the fitness of populations of agents. We use a genetic algorithm (GA) to optimize behavior in a team of simulated robots that mimic foraging ants. We introduce positional and resource detection error models into this simulation, emulating the sensor error characterized by our(More)
BACKGROUND Many protein regions and some entire proteins have no definite tertiary structure, existing instead as dynamic, disorder ensembles under different physiochemical circumstances. Identification of these protein disorder regions is important for protein production, protein structure prediction and determination, and protein function annotation. A(More)
Ants use individual memory and pheromone communication to achieve effective collective foraging. We implement these strategies as distributed search algorithms in robotic swarms. Swarms of simple robots are robust, scalable and capable of exploring for resources in unmapped environments. We test the ability of individual robots and teams of three robots to(More)
Evolutionary algorithms can adapt the behavior of individuals to maximize the fitness of cooperative multi-agent teams. We use a genetic algorithm (GA) to optimize behavior in a team of simulated robots that mimic foraging ants, then transfer the evolved behaviors into physical iAnt robots. We introduce positional and resource detection error models into(More)
In order to trigger an adaptive immune response, T cells move through lymph nodes searching for dendritic cells that carry antigens indicative of infection. We observe T cell movement in lymph nodes and implement those movement patterns as a search strategy in a team of simulated robots. We find that the distribution of step-sizes taken by T cells are best(More)
In order to trigger an adaptive immune response, T cells move through lymph nodes (LNs) searching for dendritic cells (DCs) that carry antigens indicative of infection. We hypothesize that T cells adapt to cues in the (LN) environment to increase search efficiency. We test this hypothesis by identifying locations that are visited by T cells more frequently(More)
For robot swarms to operate outside of the laboratory in complex real-world environments, they require the kind of error tolerance, flexibility, and scalability seen in living systems. While robot swarms are often designed to mimic some aspect of the behavior of social insects or other organisms, no systems have yet addressed all of these capabilities in a(More)
In this paper we consider the problem of coordinating robotic systems with different kinematics, sensing and vision capabilities to achieve certain mission goals. An approach that makes use of a heterogeneous team of agents has several advantages when cost, integration of capabilities, or large search areas need to be considered. A heterogeneous team allows(More)
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