ARL 47 – Research Assistant, Optimization And Multi-Agent Controls

Project Name
Agent-Based Modeling and Simulation of Human-Robot Teaming

Project Description
This project aims to create user-friendly simulations of multi-UAV (drone) systems and their human operators. The simulations must be lightweight enough to analyze large numbers (20+) of humans and agents at once, and accurate enough to enable the end user to make system design decisions, such as the number of personnel and quality of robots required to complete a mission. UAV-centered Army missions are used as scenarios for the analysis, and we investigate the performance of current and futuristic technology.

Job Description
The RA will assist the lead by implementing state of the art optimization algorithms, and/or developing new algorithms to optimize multiple objectives. For example, an Army scenario involving UAVs may want to maximize speed, minimize cost, and maximize stealth simultaneously. The RA will also implement and/or develop scalable algorithms for control of multiple simulated robots. Examples include collision avoidance algorithms for UAVs, or task distribution algorithms for teams of humans and UAVs.

Preferred Skills
– Combinatorial optimization (e.g. traveling salesman problem, vehicle routing problem)
– Multi-robot controls (e.g. collision avoidance, path planning)
– Multi-objective optimization
– Programming (Java, Python, C++, and R preferred)
– Tradeoff analysis
– Industrial or Systems Engineering
– Familiarity with agent-based modeling (e.g. NetLogo, MASON, AnyLogic, GAMA, AFSIM)

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