BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Penn Engineering Events - ECPv6.16.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Penn Engineering Events
X-ORIGINAL-URL:https://seasevents.nmsdev7.com
X-WR-CALDESC:Events for Penn Engineering Events
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250410T100000
DTEND;TZID=America/New_York:20250410T110000
DTSTAMP:20260602T094821
CREATED:20250331T173405Z
LAST-MODIFIED:20250331T173405Z
UID:13749-1744279200-1744282800@seasevents.nmsdev7.com
SUMMARY:MEAM Ph.D. Thesis Defense: "Macroscopic Ensemble Methods for Multi Robot Task Assignment in Dynamic Environments"
DESCRIPTION:With finite resources to complete tasks like monitoring\, coverage\, and search\, the challenge lies in identifying and performing tasks that can change both in frequency and location. One potential solution is teams of robots equipped with the necessary capabilities to complete the desired tasks. However\, robot teams require methods that effectively assign robots to tasks\, which is also known as the Multi Robot Task Allocation (MRTA) problem. Existing MRTA approaches assign each individual robot to a task. This works well if the team is small (less than 20 robots) and if the individual task specification does not change (monitoring a static environment). Unfortunately\, these solutions require solving a combinatorial optimization problem which has poor computational scalability as the team and number of tasks increase\, and is further exacerbated by changing task or environment conditions. An alternative perspective is to look at how biologists study animal resource selection. Instead of posing the problem of assigning animals to resources\, animals randomly select a resource weighted by the perceived resource value resulting in beneficial population configurations. Taking inspiration from biology\, we model team-wide objectives using macroscopic ensemble allocation. These methods allow robots to select stationary task regions\, are known to easily control large robot teams (more than 50 robots)\, and can even describe robot team heterogeneity. Nevertheless\, macroscopic ensemble methods require extensions to effectively solve the MRTA problem for tasks where conditions change\, e.g.\, monitoring spatiotemporal processes. The main contributions of this dissertation include online adaptive macroscopic allocation\, distributed adaptive macroscopic allocation\, and macroscopic allocation via robot-robot collaboration. Our results show robot teams monitoring spatial temporal environments using simulation and robot experiments.
URL:https://seasevents.nmsdev7.com/event/meam-ph-d-thesis-defense-macroscopic-ensemble-methods-for-multi-robot-task-assignment-in-dynamic-environments/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Dissertation or Thesis Defense
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
END:VCALENDAR