This paper reports on a study to develop power supplies for small mobile robots performing long duration missions. It investigates the use of fuel cells to achieve this objective, and in particular Proton Exchange Membrane (PEM) fuel cells. It is shown through a representative case study that, in theory, fuel cell based power supplies will provide much longer range than the best current rechargeable battery technology. It also briefly discusses an important limitation that prevents fuel cells from achieving their ideal performance, namely a practical method to store their fuel (hydrogen) in a form that is compatible with small mobile field robots. A very efficient fuel storage concept based on water activated lithium hydride (LiH) is proposed that releases hydrogen on demand. This concept is very attractive because water vapor from the air is passively extracted or waste water from the fuel cell is recycled and transferred to the lithium hydride where the hydrogen is “stripped” from water and is returned to the fuel cell to form more water. This results in higher hydrogen storage efficiencies than conventional storage methods. Experimental results are presented that demonstrate the effectiveness of the approach.

A key challenge in evolving control systems for robots using neural networks is training tractability. Evolving monolithic fixed topology neural networks is shown to be intractable with limited supervision in high dimensional search spaces. Common strategies to overcome this limitation are to provide more supervision by encouraging particular solution strategies, manually decomposing the task and segmenting the search space and network. These strategies require a supervisor with domain knowledge and may not be feasible for difficult tasks where novel concepts are required. The alternate strategy is to use self-organized task decomposition to solve difficult tasks with limited supervision. The artificial neural tissue (ANT) approach presented here uses self-organized task decomposition to solve tasks. ANT inspired by neurobiology combines standard neural networks with a novel wireless signaling scheme modeling chemical diffusion of neurotransmitters. These chemicals are used to dynamically activate and inhibit wired network of neurons using a coarse-coding framework. Using only a global fitness function that does not encourage a predefined solution, modular networks of neurons are shown to self-organize and perform task decomposition. This approach solves the sign-following task found to be intractable with conventional fixed and variable topology networks. In this paper, key attributes of the ANT architecture that perform self-organized task decomposition are shown. The architecture is robust and scalable to number of neurons, synaptic connections, and initialization parameters.

A new speciation method for parallel evolutionary computation is presented, designed specifically to handle high-dimensional data. Taking inspiration from the natural sciences, the Phylogenetic Relations Island Speciation Model (PRISM) uses common ancestry and a novel species barcoding system to detect new species and move them to separate islands. Simulation experiments were performed on Multidimensional Knapsack Problems with different fitness landscapes requiring 100-dimensional genomes. PRISM's performance with various parameter settings and on the various landscapes is analyzed and preliminary results show that PRISM can consistently produce optimal or near-optimal solutions, outperforming the standard Genetic Algorithm and Island Model in all the performed experiments.

We present an "artificial neural tissue" (ANT) architecture as a control system for autonomous multirobot tasks. This architecture combines a typical neural-network structure with a coarse-coding strategy that permits specialized areas to develop in the tissue which in turn allows such emergent capabilities as task decomposition. Only a single global fitness function and a set of allowable basis behaviors need be specified. An evolutionary (Darwinian) selection process is used to derive controllers for the task in simulation. This process results in the emergence of novel functionality through the task decomposition of mission goals. ANT-based controllers are shown to exhibit self-organization, employ stigmergy and make use of templates (unlabeled environmental cues). These controllers have been tested on a multirobot resource-collection task in which teams of robots with no explicit supervision can successfully avoid obstacles, explore terrain, locate resource material and collect it in a designated area by using a light beacon for reference and interpreting unlabeled perimeter markings. The issues of scalability and antagonism are addressed