Master of Science / Department of Mechanical & Nuclear Engineering / Dale Schinstock / This thesis describes the design, development, and initial testing of the Mobile Reduced Gravity Simulator (MoRGS). MoRGS is a hoist with active force control, to be used in terrestrial environments with human test subjects for the simulation of partial gravity or zero gravity environments. It is to be used with the subject performing activities while being harnessed to the hoist. The following work here describes the mechanical design, structural and dynamic analyses, simulations used to aid in the control design and component selection, the development of unique control algorithms tailored to the objectives and uncommon dynamics of MoRGS, and initial testing performed without the use of human subjects. Major components of the MoRGS system include: AC servo motor, gearbox, custom-designed drum, pneumatic muscle, load cell, and a microprocessor. The system is designed to track the motion of the test subject over several meters of vertical travel at speeds of up to 2 Gs of acceleration. This allows for high performance during subject’s physical tests, including running on a treadmill and a climbing ladder. It is capable of offloading 50 lb. to 600 lb. and the level of desired reduced gravity is programmable. Results from testing of the system demonstrate that MoRGS system achieves its goals. It performs well, and the sensitivity of the force controller enables it to compensate for the most minute human motion disturbance.

Doctor of Philosophy / Department of Computing and Information Sciences / Scott A. DeLoach / There is a growing demand for more effective integration of humans and computing systems, specifically in multiagent and multirobot systems. There are two aspects to consider in human integration: (1) the ability to control an arbitrary number of robots (particularly heterogeneous robots) and (2) integrating humans as peers in computing systems instead of being just users or supervisors. With traditional supervisory control of multirobot systems, the number of robots that a human can manage effectively is between four and six [17]. A limitation of traditional supervisory control is that the human must interact individually with each robot, which limits the upper-bound on the number of robots that a human can control effectively. In this work, I define the concept of "organizational control" together with an autonomous mechanism that can perform task allocation and other low-level housekeeping duties, which significantly reduces the need for the human to interact with individual robots. Humans are very versatile and robust in the types of tasks they can accomplish. However, failures in computing systems are common and thus redundancies are included to mitigate the chance of failure. When all redundancies have failed, system failure will occur and the computing system will be unable to accomplish its tasks. One way to further reduce the chance of a system failure is to integrate humans as peer "agents" in the computing system. As part of the system, humans can be assigned tasks that would have been impossible to complete due to failures.

Doctor of Philosophy / Department of Computing and Information Sciences / David Gustafson / We use probabilistic graphs to predict the location of swarms over 100 steps in simulations in grid worlds. One graph can be used to make predictions for worlds of different dimensions. The worlds are constructed from a single 5x5 square pattern, each square of which may be either unoccupied or occupied by an obstacle or a target. Simulated robots move through the worlds avoiding the obstacles and tagging the targets. The interactions between the robots and the robots and the environment lead to behavior that, even in deterministic simulations, can be difficult to anticipate. The graphs capture the local rate and direction of swarm movement through the pattern. The graphs are used to create a transition matrix, which along with an occupancy matrix, can be used to predict the occupancy in the patterns in the 100 steps using 100 matrix multiplications. In the future, the graphs could be used to predict the movement of physical swarms though patterned environments such as city blocks in applications such as disaster response search and rescue. The predictions could assist in the design and deployment of such swarms and help rule out undesirable behavior.