Human-Machine Interaction

Project Objective: The overarching objective is to develop an assistive technology that optimized the performance of human-robot interaction (HRI) in the presence of uncertain automation reliability and human factors, such as cognitive workload and trust in the automation.


  • Human Interactions in Social Networks

​Abstract: By modeling social interactions as a general directed graph, social leaders and followers.

  • Motivated by the non-local property of fractional-order systems, the social response of individuals in the network are modeled by fractional-order dynamics whose states depend on influences from local neighbors and past experience
  • A decentralized influence method is developed to maintain existing social influence between individuals (i.e., without isolating peers in the group) and to influence the social group to a common desired state (i.e., within a convex hull spanned by social leaders).

Related Publications:

[1]. Z. Kan, J. Klotz, E. L. Pasiliao, and W. E. Dixon, "Containment Control for a Social Network with State-Dependent Connectivity," Automatica, Vol. 56, pp. 86-92 (2015)


  • Human-Robot Interaction

​Abstract: Planning and control of single-agent multi-robot systems are investigated with the objective of improving the robots performance and reducing the workload of agents (or human operators).

  • The robots are assumed to have onboard intelligent control systems that allow the robots to autonomously perform cooperative tasks by collaborating with other robots
  • In addition to the onboard intelligence, the robot also receives commands from a single-agent (or operator), allowing the agent to influence the behaviors of the robot, resulting in a shared control scheme with adjustable autonomy
  • Using an identical control input from the operator, the key contribution of the developed control structure is that a single operator can control multiple robots in the same manner that the agent will control a single robot, thus significantly reducing cognitive workload and operator fatigue.

Related Publications:

[1]. Z. Kan, C. Ton, J. W. Curtis, and S. S. Mehta, "Fractional-order System based Planning and Scalar Control in Single-Agent Multi-Robot Systems," IEEE Transactions on Human-Machine System, under review.