Heterogeneous Sensor-Robot Team Positioning and Mixed Strategy Scheduling

Hartman, Benjamin T.; Tatum, Richard; Bays, Matthew J.

doi:10.1109/iros.2018.8594263

Cited by 2 publications

(2 citation statements)

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“…2. This is the type of environment found in [14], where the sensor performance varies as function of the environment with an assumed constant sensor radius R. We now describe how to represent the sensor performance for such environments, as well as how to implement the configuration conditions given by ( 4)- (7).…”

Section: Heterogeneous Environmentmentioning

confidence: 99%

“…Three facets to the problem of traffic monitoring are where to place the sensors, how to detect traffic distribution changes, and how to schedule sensors in response to traffic distribution changes. With respect to sensor placement, this problem is either solved as an area coverage problem [6,7,11,19] or path coverage problem [14,20]. As for the remaining facets, McIntyre and Hintz use information gain to determine sensor schedules for searching for unknown entities [17].…”

Section: Introductionmentioning

confidence: 99%

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Traffic monitoring using an adaptive sensor power scheduling algorithm

et al. 2019

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Using sensors to monitor surface or subsurface traffic requires sensor placement, detection of traffic changes, and sensor power scheduling for improved efficiency. Of these capabilities, sensor power scheduling is one of the most important as the appropriate sensors must be selected for activation to respond to changes in the traffic. We present an adaptive power scheduling algorithm that uses the homogeneous equilibrium of a potential-field-based dynamical system to determine which sensors should be active. Our algorithm assumes a nearest neighbor topology, which makes additional assumptions about the placement of sensors. We formalize these conditions and construct a sensor placement algorithm to support our scheduling algorithm. To demonstrate the efficacy of our scheduling approach, we provide two distinctive traffic detection algorithms that we combine with our placement and scheduling algorithm to test via simulation. We provide the simulation results that show in both cases, the adaptive scheduling algorithm behaves efficiently as compared to an area coverage approach , as well as an all-active path coverage approach.

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Section: Heterogeneous Environmentmentioning

confidence: 99%