Dynamic Placement of Rapidly Deployable Mobile Sensor Robots Using Machine Learning and Expected Value of Information

@article{Agogino2021DynamicPO,
  title={Dynamic Placement of Rapidly Deployable Mobile Sensor Robots Using Machine Learning and Expected Value of Information},
  author={Alice M. Agogino and Hae Young Jang and V. Tata Rao and Ritik Batra and Felicity Liao and Rohan Sood and Irving Fang and R. Lily Hu and Emerson Shoichet-Bartus and John Matranga},
  journal={ArXiv},
  year={2021},
  volume={abs/2111.07552}
}
Although the Industrial Internet of Things has increased the number of sensors permanently installed in industrial plants, there will be gaps in coverage due to broken sensors or sparse density in very large plants, such as in the petrochemical industry. Modern emergency response operations are beginning to use Small Unmanned Aerial Systems (sUAS) that have the ability to drop sensor robots to precise locations. sUAS can provide longer-term persistent monitoring that aerial drones are unable… 

References

SHOWING 1-10 OF 51 REFERENCES
Active sensing data collection with autonomous mobile robots
TLDR
This paper presents a system to compute paths for the robot to follow that incorporates the robot's limited expected deployment time, expected measurement value at each location, and a history of when each location was last visited.
Wireless Sensor Network Modeling and Deployment Challenges in Oil and Gas Refinery Plants
TLDR
This paper surveys the most promising wireless technologies for industrial monitoring and control and proposes a novel channel model specifically tailored to predict the quality of the radio signals in environments affected by highly dense metallic building blockage.
Tensegrity Robot Locomotion Under Limited Sensory Inputs via Deep Reinforcement Learning
TLDR
It is demonstrated that in the domain of tensegrity robotics, it is possible to efficiently learn end-to-end locomotion policies using mirror descent guided policy search (MDGPS) even with limited sensory inputs and neural network policies consistently outperform others.
Value of information and mobility constraints for sampling with mobile sensors
Key design of driving industry 4.0: joint energy-efficient deployment and scheduling in group-based industrial wireless sensor networks
TLDR
This work jointly considers deployment and sleep scheduling of sensors in a GIWSN along a production line, and proposes a hybrid harmony search and genetic algorithm, which incorporates Deployment and sleep schedules to reduce energy consumption.
Value of information for spatially distributed systems: Application to sensor placement
Sequential optimal positioning of mobile sensors using mutual information
TLDR
While most mobile sensor strategies designate a trajectory for sensor movement, this work instead employs mutual information, based on Shannon entropy, to choose the next measurement location from a discrete set of design conditions.
Machine Learning to Scale Fault Detection in Smart Energy Generation and Building Systems
  • R. L. Hu
  • Computer Science, Engineering
  • 2016
TLDR
A methodology is proposed that takes advantage of existing sensor data, encodes expert knowledge about the application system, and applies statistical and mathematical methods to reduce the time required for manual configurations to lower cost barriers to widespread deployment.
Industrial Wireless Sensor Networks: Challenges, Design Principles, and Technical Approaches
TLDR
The aim is to provide a contemporary look at the current state of the art in IWSNs and discuss the still-open research issues in this field and to make the decision-making process more effective and direct.
...
1
2
3
4
5
...