Bringing Together Tomorrow\u27s Leader\u27s, Today

Dr. Sanza Kazadi is the founder, president, and chief scientist of the Jisan Research Institute, “the only professional research laboratory for students (ages 13-18).” JRI students conduct research in swarm engineering, evolutionary computation and sustainable energy systems, present their work in professional scientific conferences and journals, and contribute to new technologies some of which are patented

Conjugate Schema and Basis Representation of Crossover and Mutation Operators

In genetic search algorithms and optimization routines, the representation of the mutation and crossover operators are typically defaulted to the canonical basis. We show that this can be influential in the usefulness of the search algorithm. We then pose the question of how to find a basis for which the search algorithm is most useful. The conjugate schema is introduced as a general mathematical construct and is shown to separate a function into smaller dimensional functions whose sum is the original function. It is shown that conjugate schema, when used on a test suite of functions, improves the performance of the search algorithm on 10 out of 12 of these functions. Finally, a rigorous but abbreviated mathematical derivation is given in the appendices

Conjugate Schema and Basis Representation of Crossover and Mutation Operators

In genetic search algorithms and optimization routines, the representation of the mutation and crossover operators are typically defaulted to the canonical basis. We show that this can be influential in the usefulness of the search algorithm. We then pose the question of how to find a basis for which the search algorithm is most useful. The conjugate schema is introduced as a general mathematical construct and is shown to separate a function into smaller dimensional functions whose sum is the original function. It is shown that conjugate schema, when used on a test suite of functions, improves the performance of the search algorithm on 10 out of 12 of these functions. Finally, a rigorous but abbreviated mathematical derivation is given in the appendices

Generating swarm solution classes using the Hamiltonian Method of swarm design

We utilize a swarm design methodology that enables us to develop classes of swarm solutions to specific specifications. The method utilizes metrics devised to evaluate the swarm’s progress – the global variables – along with the set of available technologies in order to answer varied questions surrounding a swarm design for the task. These questions include the question of whether or not a swarm is necessary for a given task. The Jacobian matrix, here identified as the technology matrix, is created from the global variables. This matrix may be interpreted in a way that allows the identification of classes of technologies required to complete the task. This approach allows us to create a class of solutions that are all suitable for accomplishing the task. We demonstrate this capability for accumulation swarms, generating several configurations that can be applied to complete the task. If the technology required to complete the task either cannot be implemented on a single agent or is unavailable, it may be possible to utilize a swarm to generate the capability in a distributed way. We demonstrate this using a gradient-based search task in which a minimal swarm is designed along with two additional swarms, all of which extend the agents’ capabilities and successfully accomplish the task

Swarm engineering

Swarm engineering is the natural evolution of the use of swarm-based techniques in the accomplishment of high level tasks using a number of simple robots. In this approach, one seeks not to generate a class of behaviors designed to accomplish a given global goal, as is the approach typically found in mainstream robotics. Once the class of behaviors has been understood and decided upon, specific behaviors designed to accomplish this goal may be generated that will complete the desired task without any concern about whether or not the final goal will actually be completed. As long as the generated behaviors satisfy a set of conditions generated in the initial investigation, the desired goal will be accomplished. This approach is investigated in terms of three specific practical problems. First we apply swarm engineering to plume tracking, utilizing both real and simulated experiments. We initially decide whether or not this is a problem that swarm engineering should be applied to at all, A careful investigation of two plume-tracking algorithms yields the weakness of single-robot plume tracking — the tracking of low density plumes. Application of swarm engineering to this sub-problem yields performance that is capable of tracking plumes that are significantly more diffuse than those which can be tracked by single plume trackers. The second problem is position-independent clustering, in which a cluster of objects is constructed from many initial clusters, without predetermination of the final cluster's location. Although we have completed a robotic instantiation of this type of system, the bulk of our work is theoretical and verification with simulation. A minimal condition is derived which guarantees this final global outcome, in both an unrealistic case and a case that is more realistic for real robots. Methods of generating efficiency improvements are derived. The application of this formalism to real robots is discussed. Finally, conditions are derived and demonstrated leading to stable arrangements of multiple clusters, a precursor of distributed construction. The third and last problem is the traveling salesman problem (TSP). Marco Dorigo (see, for instance, Dorigo 1996) generated much interest in the use of "ants" on the problem. We theoretically demonstrate that Dorigo's ants satisfy a swarm engineering condition generated for this problem. However, the result of Dorigo's work is also demonstrably fragile under random fluctuations. A second more robust ant-based method is generated and tested on a small number of standard test problems taken from TSPLIB, a source of many well-known TSP instantiations. In all cases, the new algorithm is capable of reliably finding the minimum distance path. This thesis makes a number of contributions to the literature. First, we study two systems that take advantage of the embodiment and interaction dynamics of the robot to accomplish the single robot goal. We then extend this work to answer two questions: 1. Is it possible to apply swarm engineering to plume tracking? 2. How can one apply swarm engineering to plume tracking? We demonstrate that is indeed possible to extend one of the two previous plume tracking systems to a swarm. We demonstrate that the swarm is capable of successfully tracking plumes that are significantly more diffuse than those capable of being tracked by individual agents. Next, we derive a formal condition which must be satisfied in order for a system made up of many clusters and robots to become a system of one cluster and many robots, if the robots do not have global information. We extend this work by deriving ways of generating more efficient paradigms, and demonstrate how to use the theory in the design of individual robots. Finally, we modify our theory to include systems generating multiple clusters, and demonstrate how to generate clusters of predefined relative sizes. Our third contribution is a formalism for ant-based TSP algorithms. This allows us to understand why Dorigo's algorithms are relatively unstable. We create a completely new system of traveling agents that also solve TSP by having the ability to "die along the way" and to self replicate in a specific fashion. Perhaps the most important contribution here is an alternative approach to the design of swarm systems. This allows us to explore the three problems of interest here by first determining a condition that allows the completion of the task, and then allows the generation of behaviors satisfying the minimal condition. This view to system design has the advantage of allowing the design of systems that will generate the desired behavior, concommittantly reducing the danger of generating unwanted emergent behavior

Workshop 1C: An Introduction to Engineering Swarms

Swarm engineering is a discipline which envisions the creation of engineered solutions to problems requiring multiple interacting agents. The field is vast, though historically it has focused on robotics and computer programming. It has applications in government, sociology, economics, business, and tangential applications to traditional science and theoretical research. Presentations given during part of the workshop will describe some of the work going on at IMSA around state-of-the-art tools being developed here to address swarm design problems and some of the problems we\u27re investigating at the moment. These tools include theoretical tools and IMSA\u27s own IMSAbot robotic platform under development in the Swarm Engineering Laboratory. As a way of getting a feel the complexity of designing a swarm, participants will generate a human swarm that solves a real-life swarm problem that we\u27re investigating in theory and simulation. Participants will become the swarm elements and learn how their individual actions and interactions can generate a specific, desired global goal

Workshop 2C: Learning to Pull Energy Right Out of the Air

Entrochemical systems were developed during the late 2000\u27s and early 2010\u27s. Generally speaking, the systems involve a spontaneous heat pump stage that moves heat from a lower temperature stage to a higher temperature stage. This heat can be used to drive many thermal processes. A second stage utilizes environmental thermal energy to recharge the fluids in the entrochemical system once they\u27ve become exhausted. This means that the energy that drives this process can be largely derived from environmental energy. Among the processes enabled by entrochemical systems is desalination, an important part of the water infrastructure of the future. One of the limiting factors in desalination is the availability of energy. We will report on some of the work we\u27re doing to make this technology a viable solution for desalination. Participants will assemble and run their own entrochemical systems during the workshop

A Floating Axle-Based Vertical Axle Wind Turbine

Wind turbines have been a part of human energy acquisition for millennia. In recent decades, a flurry of activity in wind power has driven a rapid growth in the use of wind mills in generating electrical power. Among recent improvements are the shape of the blades, the required start-up torque, and a transition to a direct drive system. Most wind turbines are the horizontal axis, meaning that the turbine rotates around a horizontal axle. Recently, a levitating axle was developed that enables a virtually friction free wind turbine that has a vertical axis. This axle uses magnetic repulsion to stabilize one end while the other end is mechanically stabilized. Careful balance of the weight against the magnetic repulsion can lead to very small frictional forces, enabling virtually limitless operation. Participants will apply this to the construction of a wind turbine, and will build and take home a virtually frictionless wind turbine.by recording how they perform on these tests as compared to the control flies. At the end, there will be open discussion in which all students can participate