Abstract
Collaboration in teams is essential in robot collectives. In order to achieve goals, individual robots would otherwise not be able to accomplish. In a such a distributed and highly dynamic system, a global coordination might not be possible. In this paper, we analyse static team affiliations, defined at deployment time, and compare its efficiency against dynamic team affiliations generated during runtime using random selection. Since operators might not be able to determine all dynamic aspects of the given environment at the time of deployment, we further propose a novel, goal-aware approach to affiliate each robot with a team. This approach brings together insights from biology, sociology, and psychology. In this novel approach, robots only operate on aggregated information from the network which is potentially changing during runtime. Finally, we also introduce an approach to select a team affiliation during runtime using machine learning techniques. Using 60,000 randomised scenarios, we analyse the efficiency and further discuss the different benefits and drawbacks of the proposed approaches.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 12th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2018 |
| Publisher | IEEE |
| Pages | 90-99 |
| Number of pages | 10 |
| Volume | 2018-September |
| ISBN (Electronic) | 9781538651728 |
| ISBN (Print) | 978-1-5386-5173-5 |
| DOIs | |
| Publication status | Published - 15 Jan 2019 |
| Event | 12th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2018 - Trento, Italy Duration: 3 Sep 2018 → 7 Sep 2018 |
Publication series
| Name | 2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO) |
|---|---|
| Publisher | IEEE |
| ISSN (Print) | 1949-3673 |
| ISSN (Electronic) | 1949-3681 |
Conference
| Conference | 12th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2018 |
|---|---|
| Country | Italy |
| City | Trento |
| Period | 3/09/18 → 7/09/18 |
Fingerprint
Keywords
- collaboration
- collectives
- cooperation
- division of labour
- goal awareness
- robotic systems
- team affiliation
Cite this
}
Goal-aware team affiliation in collectives of autonomous robots. / Esterle, Lukas.
Proceedings - 12th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2018. Vol. 2018-September IEEE, 2019. p. 90-99 8614283 (2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Goal-aware team affiliation in collectives of autonomous robots
AU - Esterle, Lukas
PY - 2019/1/15
Y1 - 2019/1/15
N2 - Collaboration in teams is essential in robot collectives. In order to achieve goals, individual robots would otherwise not be able to accomplish. In a such a distributed and highly dynamic system, a global coordination might not be possible. In this paper, we analyse static team affiliations, defined at deployment time, and compare its efficiency against dynamic team affiliations generated during runtime using random selection. Since operators might not be able to determine all dynamic aspects of the given environment at the time of deployment, we further propose a novel, goal-aware approach to affiliate each robot with a team. This approach brings together insights from biology, sociology, and psychology. In this novel approach, robots only operate on aggregated information from the network which is potentially changing during runtime. Finally, we also introduce an approach to select a team affiliation during runtime using machine learning techniques. Using 60,000 randomised scenarios, we analyse the efficiency and further discuss the different benefits and drawbacks of the proposed approaches.
AB - Collaboration in teams is essential in robot collectives. In order to achieve goals, individual robots would otherwise not be able to accomplish. In a such a distributed and highly dynamic system, a global coordination might not be possible. In this paper, we analyse static team affiliations, defined at deployment time, and compare its efficiency against dynamic team affiliations generated during runtime using random selection. Since operators might not be able to determine all dynamic aspects of the given environment at the time of deployment, we further propose a novel, goal-aware approach to affiliate each robot with a team. This approach brings together insights from biology, sociology, and psychology. In this novel approach, robots only operate on aggregated information from the network which is potentially changing during runtime. Finally, we also introduce an approach to select a team affiliation during runtime using machine learning techniques. Using 60,000 randomised scenarios, we analyse the efficiency and further discuss the different benefits and drawbacks of the proposed approaches.
KW - collaboration
KW - collectives
KW - cooperation
KW - division of labour
KW - goal awareness
KW - robotic systems
KW - team affiliation
UR - http://www.scopus.com/inward/record.url?scp=85061902983&partnerID=8YFLogxK
UR - https://ieeexplore.ieee.org/document/8614283
U2 - 10.1109/SASO.2018.00020
DO - 10.1109/SASO.2018.00020
M3 - Conference contribution
AN - SCOPUS:85061902983
SN - 978-1-5386-5173-5
VL - 2018-September
T3 - 2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)
SP - 90
EP - 99
BT - Proceedings - 12th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2018
PB - IEEE
ER -