Matthew A. Vavrina

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In low-thrust, gravity-assist trajectory design, two objectives are often equally important: maximization of final spacecraft mass and minimization of time-of-flight. Generally, these objectives are coupled and competing. Designing the trajectory that is best-suited for a mission typically requires a compromise between the objectives. However, optimizing(More)
This paper introduces a hardware platform for automated battery changing and charging for multiple UAV agents. The automated station holds a buffer of 8 batteries in a novel dual-drum structure that enables a “hot” battery swap, thus allowing the vehicle to remain powered on throughout the battery changing process. Each drum consists of four battery bays,(More)
This paper presents an extension of our previous work on the persistent surveillance problem. An extended problem formulation incorporates real-time changes in agent capabilities as estimated by an onboard health monitoring system in addition to the existing communication constraints, stochastic sensor failure and fuel flow models, and the basic constraints(More)
This paper extends prior work on the persistent mission problem where real-time changes in agent capability are included in the problem formulation. Here, we couple the mission planner with a low-level adaptive controller in realtime to: (1) Provide robustness against actuator degradations and (2) Use parameters internal to the adaptive controller to(More)
To expand mission capabilities needed for exploration of the Solar System, optimal lowthrust trajectories must be found. However, low-thrust, multiple gravity-assist trajectories pose significant optimization challenges because of their expansive, multimodal design space. Here, a novel technique is developed for global, low-thrust, interplanetary trajectory(More)
Vavrina, Matthew A. M.S.A.A., Purdue University, December, 2008. A Hybrid Genetic Algorithm Approach to Global Low-Thrust Trajectory Optimization. Major Professor: Kathleen C. Howell. To expand mission capabilities that are required for exploration of the solar system, methodologies to design optimal low-thrust trajectories must be developed. However,(More)
Preliminary design of high‐thrust interplanetary missions is a highly complex process. The mission designer must choose discrete parameters such as the number of flybys and the bodies at which those flybys are performed. For some missions, such as surveys of small bodies, the mission designer also contributes to target selection. In addition, real‐valued(More)
Trajectory design for missions to small bodies is tightly coupled both with the selection of targets for the mission and with the choice of spacecraft power, propulsion, and other hardware. Traditional methods of trajectory optimization have focused on finding the optimal trajectory for an a priori selection of destinations and spacecraft parameters. Recent(More)
This paper introduces and demonstrates a full hardware testbed for research in multi-agent planning and learning for long-duration missions. The testbed includes an automated battery changing/charging platform and multiple UAV/UGV agents. The planner for each agent was formulated as a decentralised multi-agent Markov decision process and implemented using a(More)