Grants and Contributions:

Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023)

Work in dangerous environments such as Urban Search and Rescue ( USAR , the post-disaster exploration and mapping of structures, searching for victims and aiding disaster response) represents an extreme challenge to teams of intelligent robots. The world is complex and unpredictable, robots can be damaged or destroyed, and communication is interfered with due to damaged infrastructure. Teams consisting of various types of robots (heterogeneous teams) are required, because the risk involved in losing units can be better amortized with larger numbers of more expendable individuals. Under such conditions, teams must form and reorganize dynamically around the mix of individuals available at any time, doing useful work with the skills available, actively looking for new individuals to supplement skills, and making use of damaged individuals to the degree practicable (including assisting these if possible). Individuals must similarly adapt to damage (e.g. make use of grasping limbs if no longer mobile) and work with a team as well as possible. Permeating all of this, risk must be actively balanced with the value of carrying out any task for the team, and these tasks must all be accomplished under rapid change and limited communication.

The program of research proposed here will advance the state of the art in dynamic heterogeneous robotic teamwork in dangerous environments, incorporating and extending all of the above themes. Work has been done in these themes individually (e.g. task allocation on changing teams, recruiting agents) but a deployable decentralized solution supporting a broad collection of robots for a domain as dangerous as USAR is still elusive. In recent work my students and I have developed a framework for this purpose that includes accomplishing tasks under changing team membership, adapting to robot loss and recruiting new individuals. We have also been working on improved robot control and planning for complex domains. I will be leveraging this experience to more broadly advance dynamic heterogeneous teamwork, encompassing major issues such as managing damaged robots to the best extent possible, assisting damaged robots, balancing task risk vs. value, and developing team strategies to manage risk. The outcome of this will be a software system that will be physically demonstrated using teams of robots in the real world. We will be exploring the use of humanoid robots in particular on teams under these conditions, since the nature of this form supports a broad range of activities despite damage (e.g. humanoids can crawl or drag themselves if unable to walk). This work is applicable in a broad range of dangerous domains beyond USAR, such as mining, space exploration, and defense.

The proposed research will provide valuable training for four PhD students and nine MSc students in artificial intelligence, complex software development, and robotics: skills that are vitally needed by industry.