TY - GEN
T1 - Broadening applicability of swarm-robotic foraging through constraint relaxation
AU - Harwell, John
AU - Gini, Maria L
PY - 2018/6/8
Y1 - 2018/6/8
N2 - Swarm robotics (SR) offers promising solutions to real-world problems that can be modeled as foraging tasks, e.g. disaster/trash cleanup or object gathering for construction. Yet current SR foraging approaches make limiting assumptions that restrict their applicability to selected real-world environments. We propose an improved self-organized task allocation method based on task partitioning that removes restrictions such as: (1) a priori knowledge of foraging environment, and (2) strict limitations on intermediate drop/pickup site behavior. With experiments in simulation, we show that under the proposed constraint relaxation, our approach still provides performance increases when compared to an unpartitioned strategy within some combinations of swarm sizes, robot capabilities, and environmental conditions. This work broadens the applicability of SR foraging approaches, showing that they can be effective under ideal conditions while continuing to perform robustly in more volatile/challenging environments.
AB - Swarm robotics (SR) offers promising solutions to real-world problems that can be modeled as foraging tasks, e.g. disaster/trash cleanup or object gathering for construction. Yet current SR foraging approaches make limiting assumptions that restrict their applicability to selected real-world environments. We propose an improved self-organized task allocation method based on task partitioning that removes restrictions such as: (1) a priori knowledge of foraging environment, and (2) strict limitations on intermediate drop/pickup site behavior. With experiments in simulation, we show that under the proposed constraint relaxation, our approach still provides performance increases when compared to an unpartitioned strategy within some combinations of swarm sizes, robot capabilities, and environmental conditions. This work broadens the applicability of SR foraging approaches, showing that they can be effective under ideal conditions while continuing to perform robustly in more volatile/challenging environments.
UR - http://www.scopus.com/inward/record.url?scp=85049879767&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049879767&partnerID=8YFLogxK
U2 - 10.1109/SIMPAR.2018.8376280
DO - 10.1109/SIMPAR.2018.8376280
M3 - Conference contribution
AN - SCOPUS:85049879767
T3 - 2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2018
SP - 116
EP - 122
BT - 2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2018
A2 - Ye, Nan
A2 - Kurniawati, Hanna
A2 - MacDonald, Bruce
A2 - Drumwright, Evan
A2 - Fraichard, Thierry
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2018
Y2 - 16 May 2018 through 19 May 2018
ER -