Beatriz A. Asfora — accepted AI-conference papers

2021

Exploiting Natural Language for Efficient Risk-Aware Multi-Robot SaR Planning

Vikram Shree, Beatriz A. Asfora, Rachel Zheng, Samantha Hong, Jacopo Banfi, Mark Campbell

RA-L 2021

The ability to develop a high-level understanding of a scene, such as perceiving danger levels, can prove valuable in planning multi-robot search and rescue (SaR) missions. In this work, we propose to uniquely leverage natural language descriptions from the mission commander in chief and image data

Cited by 14 Source ↗code ↗Scholar ↗

2020

Mixed-Integer Linear Programming Models for Multi-Robot Non-Adversarial Search

Beatriz A. Asfora, Jacopo Banfi, Mark E. Campbell

RA-L 2020

In this letter, we consider the Multi-Robot Efficient Search Path Planning (MESPP) problem, where a team of robots is deployed in a graph-represented environment to capture a moving target within a given deadline. We prove this problem to be NP-hard, and present the first set of Mixed-Integer Linear

Cited by 22 Source ↗code ↗Scholar ↗