Information Based Distributed Control for Biochemical Source Detection and Localization

The paper proposes several improvements on the Direction of Gradient (DOG) algorithm proposed in [1] for detecting and localizing a biochemical source with moving sensors. In particular, we show that the DOG algorithm can be turned into a distributed control scheme for a mobile sensing network, and that the maximum likelihood estimation proposed in the original algorithm can be replaced with more computationally efficient numerical procedures. Simulations on a single sensor and on a group of mobile sensors are provided that show that the proposed modifications simplify the original algorithm and improve its performance.

Authors

• Panos Tzanos
• Milos Zefran
• Arye Nehorai

Keywords

• Distributed control
• Biosensors
• Maximum likelihood estimation
• Vehicles
• Robot sensing systems
• Robot kinematics
• Sensor phenomena and characterization
• Chemical sensors
• Sensor arrays
• Mobile robots
• Gradient Direction
• Original Algorithm
• Single Sensor
• Group Of Sensors
• Parameter Estimates
• Function Of Time
• Column Vector
• General Case
• Random Walk
• Unknown Parameters
• Vector-based
• First Approximation
• Taylor Series
• Distribution Network
• Taylor Expansion
• Local Estimates
• Neighborhood Relationship
• Cramer-Rao Lower Bound
• First-order Taylor Series
• Around A Circle
• Single Vehicle
• Newton-Raphson Algorithm
• Variable Step Size
• Robotic Group
• Localization Error
• Taylor Series Approximation
• Substances Of Interest
• Projection Matrix
• Biochemical source localization
• mobile sensor networks
• Cramér-Rao bound (CRB)
• robot coordination algorithms