TY - GEN
T1 - A comparison of deterministic and stochastic approaches for allocating spatially dependent tasks in micro-aerial vehicle collectives
AU - Dantu, Karthik
AU - Berman, Spring
AU - Kate, Bryan
AU - Nagpal, Radhika
PY - 2012
Y1 - 2012
N2 - We compare our previously developed deterministic [7] and stochastic [3], [4] strategies for allocating tasks in robotic swarms1 consisting of very large populations of highly resource-constrained robots. We study our two task allocation approaches in a simulated scenario in which a collective of insect-inspired micro-aerial vehicles (MAVs) must produce a specified spatial distribution of pollination activity over a crop field. We investigate the approaches' requirements, advantages, and disadvantages under realistic conditions of error in robot localization, navigation, and sensing in simulation. Our results show that the deterministic approach, which requires region-based robot navigation, yields higher task progress in all cases. For robots without such navigation capabilities, the stochastic approach is a feasible alternative, and its resulting task progress is less sensitive to error in localization, error in navigation, and a combination of high error in localization, navigation, and sensing.
AB - We compare our previously developed deterministic [7] and stochastic [3], [4] strategies for allocating tasks in robotic swarms1 consisting of very large populations of highly resource-constrained robots. We study our two task allocation approaches in a simulated scenario in which a collective of insect-inspired micro-aerial vehicles (MAVs) must produce a specified spatial distribution of pollination activity over a crop field. We investigate the approaches' requirements, advantages, and disadvantages under realistic conditions of error in robot localization, navigation, and sensing in simulation. Our results show that the deterministic approach, which requires region-based robot navigation, yields higher task progress in all cases. For robots without such navigation capabilities, the stochastic approach is a feasible alternative, and its resulting task progress is less sensitive to error in localization, error in navigation, and a combination of high error in localization, navigation, and sensing.
UR - http://www.scopus.com/inward/record.url?scp=84872281743&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872281743&partnerID=8YFLogxK
U2 - 10.1109/IROS.2012.6386233
DO - 10.1109/IROS.2012.6386233
M3 - Conference contribution
AN - SCOPUS:84872281743
SN - 9781467317375
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 793
EP - 800
BT - 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2012
T2 - 25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012
Y2 - 7 October 2012 through 12 October 2012
ER -