Maryland Robotics Student Seminar: Path Planning for Information When Hazards Corelate with Targets
Friday, February 11, 2022
301 405 8870
Path Planning for maximizing information gathered when hazards correlate with targets
Alkesh Kumar Srivastava
Advisor: Michael Otte
Bayesian inference is a statistical tool widely used in probabilistic roadmap planning. We investigate Bayesian inference and Shannon Information gathering in the special case of a correlation between targets and hazards in the environment. In this talk, I will discuss a proposed methodology for path planning in order to maximize the information gathered about both targets and hazards despite the possibility of annihilation in the hostile environment. This work is ongoing and the talk will cover recent progress.
About the Robotics Student Seminars
The Robotics Student Seminars at the University of Maryland College Park are a student-run series of talks given by current robotics students.
The purpose of these talks is to:
- Encourage interaction between Robotics students from different subfields;
- Provide an opportunity for Robotics students to be aware of and possibly get involved in the research their peers are conducting;
- Provide an opportunity for Robotics students to receive feedback on their current research;
- Provide speaking opportunities for Robotics students.