Sebastian A. Zanlongo

School of Computing and Information Sciences

Lecture Information:
  • November 2, 2018
  • 2:00 PM
  • ECS 349

Speaker Bio

Sebastian Zanlongo is a Ph.D. candidate in the School of Computing and Information Sciences at FIU, and a Department of Energy Fellow. His research interests include motion planning and multi-robot coordination, with applications in nuclear Deactivation & Decommissioning. He has developed novel scheduling and planning algorithms at G2-Inc., multi-robot behavior strategies at Los Alamos National Laboratories, and machine learning techniques for anomaly detection at Sandia National Laboratories. Sebastian has worked with Savannah River National Laboratory to develop methods for robotic adaptive sampling in radioactive environments, and is expanding on this work at the FIU Applied Research Center.


Large quantities of high-level radioactive waste were generated during WWII. This waste is being stored in facilities such as double-shell tanks in Washington, and the Waste Isolation Pilot Plant in New Mexico. The vessels and legacy structures that once processed, or now store this waste are in a monitoring and maintenance phase, to ensure that leaks and possible contamination are quickly resolved. In this work, we provide a set of complementary methodologies to assist in this endeavor.

First, we describe a robot equipped with sensors which uses a modified path-planning algorithm to navigate in a complex environment with a tether constraint. This is then augmented with an adaptive informative path planning approach that uses the assimilated sensor data within a Gaussian Process distribution model. The model’s predictive outputs are used to adaptively plan the robot’s path, to quickly map and localize areas from an unknown field of interest. The work was validated in extensive simulation testing and early hardware tests.

Next, we focus on how to assign tasks to a heterogeneous set of robots. Task assignment is done in a manner which allows for task-robot dependencies, prioritization of tasks, collision checking, and more realistic travel estimates among other improvements from the state-of-the-art. Simulation testing of this work shows an increase in the number of tasks which are completed ahead of a deadline.

Finally, we consider the case where robots are not able to complete tasks fully autonomously and require operator assistance during parts of their plan. Building upon previous work, we present a sampling-based methodology for allocating operator attention across multiple robots, or across different parts of a more sophisticated robot. This allows few operators to oversee large numbers of robots, allowing for a more scalable robotic infrastructure. This work was tested in simulation for both multi-robot deployment, and high degree-of-freedom robots, and was also tested in multi-robot hardware deployments.

The work here can allow robots to carry out complex tasks, autonomously or with operator assistance. Altogether, these three components provide a comprehensive approach towards robotic deployment within the deactivation and decommissioning tasks faced by the Department of Energy.