Control of Multi-Robot Systems

Goal: Develop scalable control strategies for multi-robot systems with limited information, e.g., no GPS, communication, or prior map of the environment. These include control strategies for collective payload transport, collective construction, swarm deployment, feature mapping, target tracking, and collision-free navigation.

Funding: Boston Fusion Corp.

Environment Mapping, 3D Reconstruction, Object Identification, and Image Classification

Goal: Develop visual inertial odometry and neural network-based techniques for simultaneous localization and mapping (SLAM), 3D reconstruction, and object identification by mobile robots in feature-poor environments, and for automatic classification of histopathological images.

Funding: Boston Fusion Corp., Mitsubishi Electric Research Laboratories, ASU Luminosity Lab

Embodiment of Human Values Profiles in the Control of Autonomous Vehicles (2022-2024)

PI: Prof. Kathryn Johnson (ASU); Other Co-PI (ASU): Prof. Theodore Pavlic

Goal: Develop a framework for generating autonomous vehicle responses to uncertain, dynamic situations, executed at short time scales, that align with human values and moral priorities, using data collected from driving trials in a simulated environment and a small-scale physical testbed with robotic vehicles.

Funding: NSF M3X EAGER Award #2146691

NRT: Citizen-Centered Smart Cities and Smart Living (2018-2024)

PI: Prof. Troy McDaniel (ASU); Co-PIs (all at ASU): Prof. Ram Pendyala, Prof. Cynthia Selin, Prof. Katina Michael

Goal: Train the next generation of master’s and doctoral students to become future Smart City thought leaders, scientists, entrepreneurs, research scholars, policy makers, and engineers through an integrated and interdisciplinary focus on the technological, societal and environmental research aspects of citizen-centered solutions for Smart Cities.

Funding: NSF Research Traineeship (NRT) Program Award #1828010