TY - GEN
T1 - A distributed multi-robot adaptive sampling scheme for complex field estimation
AU - Mysorewala, Muhammad F.
AU - Cheded, Lahouari
AU - Baig, Mirza Salman
AU - Popa, Dan O.
PY - 2010
Y1 - 2010
N2 - Monitoring widespread environmental fields is a complex task that is of great use in many areas, such as building models of natural phenomenon: e.g. moisture in a crop field, oil reservoirs, etc. A successful monitoring of such spatio-temporally distributed fields hinges upon the use of wireless sensor networks which, through their distributed nature, allow for an effective adaptive sampling procedure to gather the statistical information necessary for field density estimation. The adaptive nature of the sampling procedure used embodies a strategy which selects the next sampling location based on the gathered statistical information, and which evolves with past measurements. This paper presents a novel distributed multi-robot "Adaptive sampling algorithm", which is an extension of the algorithm proposed earlier for complex field estimation using a single-robot only. New formulations of sensor fusion in a centralized, decentralized, federated-decentralized, and distributed sensor network are presented for field density estimation, and not just cloud boundary determination. A comparison of the various computational loads involved is included. Simulation results show that adding an efficient partitioning of the sampling area and parallel multi-robot sampling improves the field reconstruction time. With N robots, more than an N-fold reduction in the number of sampling times is observed. The federated and distributed scheme also leads to an improved communication and computational efficiency.
AB - Monitoring widespread environmental fields is a complex task that is of great use in many areas, such as building models of natural phenomenon: e.g. moisture in a crop field, oil reservoirs, etc. A successful monitoring of such spatio-temporally distributed fields hinges upon the use of wireless sensor networks which, through their distributed nature, allow for an effective adaptive sampling procedure to gather the statistical information necessary for field density estimation. The adaptive nature of the sampling procedure used embodies a strategy which selects the next sampling location based on the gathered statistical information, and which evolves with past measurements. This paper presents a novel distributed multi-robot "Adaptive sampling algorithm", which is an extension of the algorithm proposed earlier for complex field estimation using a single-robot only. New formulations of sensor fusion in a centralized, decentralized, federated-decentralized, and distributed sensor network are presented for field density estimation, and not just cloud boundary determination. A comparison of the various computational loads involved is included. Simulation results show that adding an efficient partitioning of the sampling area and parallel multi-robot sampling improves the field reconstruction time. With N robots, more than an N-fold reduction in the number of sampling times is observed. The federated and distributed scheme also leads to an improved communication and computational efficiency.
KW - Adaptive sampling
KW - Environmental monitoring
KW - Extended Kalman filter
KW - Mobile WSN
KW - Sensor fusion
UR - https://www.scopus.com/pages/publications/79952388681
U2 - 10.1109/ICARCV.2010.5707823
DO - 10.1109/ICARCV.2010.5707823
M3 - Conference contribution
AN - SCOPUS:79952388681
SN - 9781424478132
T3 - 11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010
SP - 2466
EP - 2471
BT - 11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010
ER -