More robotics + machine learning next Thursday, another job talk.

---------- Forwarded message ---------- From: Pierre McKenzie mckenzie@iro.umontreal.ca Date: 2017-01-20 11:55 GMT-05:00 Subject: Colloque DIRO, jeudi 26 janvier, Pooyan Fazli (Cleveland State U.) To: seminaires@iro.umontreal.ca

*Distributed Robotics: Algorithms and Applications *

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* Pooyan Fazli *

Cleveland State University

*Jeudi 26 janvier, 15:30-16:30*, *Salle 6214*, Pavillon André-Aisenstadt

Université de Montréal, 2920 Chemin de la Tour

Café avant 15:00-15:30

*Cette présentation sera donnée en anglais.*

*Résumé:*

Future robots will have to work in dynamic, uncertain, adversarial, and multi-goal environments. Thus, the integration of the advanced physical and cognitive systems required by the next generation of robots is a challenging task. It is not feasible to design a ‘universal’ robot capable of working across a wide range of applications. The robots of today are examples of the challenges of cost, long training period, and limited functionality, which will apply to robots of the near future as well. Given these challenges, multi-robot systems may be more suitable than single-robot systems in many real world applications.

In this talk, I shall discuss two challenging problems in distributed robotics:

1) Service robots frequently face similar tasks. However, they are still not able to share their knowledge efficiently on how to accomplish those tasks. We introduce a new framework, which allows remote and heterogeneous robots to share plans and instructions on the tasks assigned to them. We demonstrate the feasibility of the framework with experiments between two geographically distributed robots and analyze the performance of the framework quantitatively.

2) In distributed coverage, a team of robots is deployed to move around a target area to perform sensing, monitoring, data collection, search, or distributed servicing tasks. We focus on the problem of multi-robot coverage and patrolling of the boundaries of a target area and the structures inside it. Events may occur at any position on the boundaries, and the goal of the robot team is to maximize the total detection reward of the events. To this end, we introduce an online, distributed algorithm, in which each robot autonomously learns the event distribution on the boundaries and then plans accordingly to visit the most promising parts of the target area. We also investigate how robots’ visual range and communication among the robots affect the performance of the robot team in the coverage problem.

*Biographie:*

Pooyan Fazli is an assistant professor and the founding director of the People and Robots Laboratory (PeRL) in the Electrical Engineering and Computer Science Department at Cleveland State University. He received his Ph.D. in Computer Science from the University of British Columbia and his M.Sc. and B.Sc. in Computer Science from Amirkabir University of Technology. Prior to joining CSU, he was a postdoctoral fellow in the CORAL Research Group at Carnegie Mellon University and in the Laboratory for Computational Intelligence at the University of British Columbia. His research focuses on artificial intelligence, autonomous robots, multi-robot systems, human-robot teams/interaction, machine learning, and robot vision and perception.