Abstract

Robots, including UAVs, have found increasing use in helping humans with dangerous and difficult tasks. The number of robots in use is increasing and is likely to continue increasing in the future. As the number of robots increases, human operators will need to coordinate and control the actions of large teams of robots. While multi-robot supervisory control has been widely studied, it requires that an operator divide his or her attention between robots. Consequently, the use of multi-robot supervisory control is limited by the number of robots that a human or team of humans can reasonably control. Swarm robotics -- large numbers of low-cost robots displaying collective behaviors -- offers an alternative approach by providing the operator with a small set of inputs and parameters that alter the behavior of a large number of autonomous or semi-autonomous robots. Researchers have asserted that this approach is more scalable and offers greater promise for managing huge numbers of robots. The emerging field of Human-Swarm Interaction (HSI) deals with the effective management of swarms by human operators. In this thesis we offer foundational work on the effect of HSI (a) on the individual robots, (b) on the group as a whole, and (c) on the workload of the human operator. We (1) show that existing general swarm algorithms are feasible on existing robots and can display collective behaviors as shown in simulations in the literature, (2) analyze the effect of interaction style and neighborhood type on the swarm's topology, (3) demonstrate that operator workload stays stable as the size of the swarm increases, but (4) find that operator workload is influenced by the interaction style. We also present considerations for swarm deployment on real robots.

Degree

MS

College and Department

Physical and Mathematical Sciences; Computer Science

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2013-09-04

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd6511

Keywords

HSI, Swarm, Robotics, HRI, Mental Workload, Human Factors, Topology, Neglect Time

Share

COinS