TY - GEN

T1 - A particle swarm-based mobile sensor network for odor source localization in a dynamic environment

AU - Jatmiko, Wisnu

AU - Sekiyama, Kosuke

AU - Fukuda, Toshio

PY - 2006

Y1 - 2006

N2 - This paper addresses the problem of odor source localization in a dynamic environment, which means the odor distribution is changing over time. Modification Particle Swarm Optimization is a well-known algorithm, which can continuously track a changing optimum over time. PSO can be improved or adapted by incorporating the change detection and responding mechanisms for solving dynamic problems. Charge PSO, which is another extension of the PSO has also been applied to solve dynamic problem. Odor source localization is an interesting application in dynamic problem. We will adopt two types of PSO modification concepts to develop a new algorithm in order to control autonomous vehicles. Before applying the algorithm in a real implementation, some important hardware parameters must be considered. Firstly, to reduce the possibility of robots leaving the search space it is needed to limit the value of vector velocity. The value of vector velocity can be clamped to the range [-V max, Vmax]; in our case for the MK-01 Robot, the maximum velocity is 0.05 m/s. Secondly, in PSO algorithm standard there is no collision avoidance mechanism. To avoid the collision among robot we add some collision avoidance functions. Finally, we also add some sensor noise, delay and threshold value to model the sensor response. Then we develop odor localization algorithm, and simulations to show that the new approach can solve such a kind of dynamic environment problem.

AB - This paper addresses the problem of odor source localization in a dynamic environment, which means the odor distribution is changing over time. Modification Particle Swarm Optimization is a well-known algorithm, which can continuously track a changing optimum over time. PSO can be improved or adapted by incorporating the change detection and responding mechanisms for solving dynamic problems. Charge PSO, which is another extension of the PSO has also been applied to solve dynamic problem. Odor source localization is an interesting application in dynamic problem. We will adopt two types of PSO modification concepts to develop a new algorithm in order to control autonomous vehicles. Before applying the algorithm in a real implementation, some important hardware parameters must be considered. Firstly, to reduce the possibility of robots leaving the search space it is needed to limit the value of vector velocity. The value of vector velocity can be clamped to the range [-V max, Vmax]; in our case for the MK-01 Robot, the maximum velocity is 0.05 m/s. Secondly, in PSO algorithm standard there is no collision avoidance mechanism. To avoid the collision among robot we add some collision avoidance functions. Finally, we also add some sensor noise, delay and threshold value to model the sensor response. Then we develop odor localization algorithm, and simulations to show that the new approach can solve such a kind of dynamic environment problem.

KW - Dynamic environment

KW - Odor source localization

KW - Particle Swarm Optimization

UR - http://www.scopus.com/inward/record.url?scp=70350349842&partnerID=8YFLogxK

U2 - 10.1007/4-431-35881-1_8

DO - 10.1007/4-431-35881-1_8

M3 - Conference contribution

AN - SCOPUS:70350349842

SN - 4431358781

SN - 9784431358787

T3 - Distributed Autonomous Robotic Systems 7

SP - 71

EP - 80

BT - Distributed Autonomous Robotic Systems 7

PB - Springer Publishing Company

T2 - 8th Symposium on Distributed Autonomous Robotic Systems, DARS 2008

Y2 - 12 July 2006 through 14 July 2006

ER -