Selected Publications
Cluster Space Formation Control of Multirobot Systems: The Lab is the home of the cluster space multirobot formation control technique. This is a full degree-of-freedom operational space control strategy with formalized mathematics, provably stable behavior, and applicability to a wide range of land/sea/air systems. The technique envisions a group of robots as a virtual articulating mechanism and allows a single pilot or a higher-level automated controller to specify desired motions and the geometric characteristics of the group in a simple, intuitive manner. Key publications include:
C. Kitts and I. Mas, “Cluster Space Specification and Control of Multi-Robot Systems,” IEEE/ASME Trans. On Mechatronics, v14 n2, pp. 207-218, 2009.
I. Mas and C. Kitts, “Dynamic Control of Mobile Multirobot Systems,” IEEE Access, v2, pp. 558-570, 2014.
Adaptive navigation of environmental scalar fields: The Lab is a leader in multirobot techniques to adaptively locate and move along points of interest in an environmental scalar field (e.g., a region over which a parameter varies, such as temperature, radiation level, or the concentration of a pollutant). In particular, we have developed a hierarchical control technique based on our cluster space formation controller that allows us to use multirobot clusters to find these interesting points without exhaustively mapping the entire region. Capabilities of interest include locating the max/min points in a field, moving along contours, moving down/up ridge/trench formations, locating saddle points, and moving along frontlines. These capabilities are fundamental for applications such as disaster response, environmental monitoring/characterization, exploration and security.
T. Adamek, C. Kitts, and I. Mas, “Gradient-based Cluster Space Navigation for Autonomous Surface Vessels,” IEEE/ASME Trans. on Mechatronics, vol 20 no 2, pp. 506-518, 2014.
C. Kitts, R. McDonald, M. Neumann, “Adaptive Navigation Control Primitives for Multirobot Clusters,” IEEE Access, vol 6, pp. 17625-17642, 2018.