Event-based motion control for mobile-sensor networks

2003 ◽  
Vol 2 (4) ◽  
pp. 34-42 ◽  
Author(s):  
Z. Butler ◽  
D. Rus
Author(s):  
J. Karl Hedrick ◽  
Brandon Basso ◽  
Joshua Love ◽  
Anouck R. Girard ◽  
Andrew T. Klesh

This paper presents a state-of-the-art survey in the broad area of Mobile Sensor Networks (MSNs). There is currently a great deal of interest in having autonomous vehicles carrying sensors and communication devices that can conduct ISR (intelligence, surveillance and reconnaissance) operations. Although this paper will discuss issues common to mobile sensor networks, the applications will generally be associated with autonomous vehicles. Areas that are addressed are: 1. Mission definition languages that allow the human to compose a mission defined in terms of tasks; 2. Communication issues including hardware, software, and network connectivity; 3. Task allocation among the assets generally by a market-based approach; 4. Path planning for individual agents; and 5. Platform motion control using autopilots with and without GPS signals and including collision avoidance.


Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2547 ◽  
Author(s):  
Yu Hu ◽  
Qiang Lu ◽  
Yanzhu Hu

This paper deals with the problem of environmental monitoring by designing a cooperative control scheme for mobile sensor networks. The proposed cooperative control scheme includes three main modules: a wireless communication module, a direction decision module, and a motion control module. In the wireless communication module, an event-based communication rule is proposed, which means that mobile sensor nodes do not send their positions, velocities, and the data of environmental attributes to the other sensor nodes in real-time for the coordination and control of mobile sensor networks. Due to using the event-based communication rule, the communication bandwidth can be saved. In the direction decision module, a radial basis function network is used to model the monitored environment and is updated in terms of the sampled environmental data and the environmental data from the other sensor nodes by the wireless communication module. The updated environment model is used to guide the mobile sensor network to move towards the region of interest in order to efficiently model the distribution map of environmental attributes, such as temperature, salinity, and pH values for the monitored environment. In the motion control module, a finite-time consensus control approach is proposed to enable the sensor nodes to quickly change their movement directions in light of the gradient information from the environment model. As a result of using the event-based communication rule in the wireless communication module, the proposed control approach can also lower the updating times of the control signal. In particular, the proposed cooperative control scheme is still efficient under the directed wireless communication situation. Finally, the effectiveness of the proposed cooperative control scheme is illustrated for the problem of environmental monitoring.


2010 ◽  
Vol 21 (3) ◽  
pp. 490-504 ◽  
Author(s):  
Fu-Long XU ◽  
Ming LIU ◽  
Hai-Gang GONG ◽  
Gui-Hai CHEN ◽  
Jian-Ping LI ◽  
...  

2012 ◽  
Vol 23 (3) ◽  
pp. 629-647 ◽  
Author(s):  
Lei WU ◽  
Xiao-Min WANG ◽  
Ming LIU ◽  
Gui-Hai CHEN ◽  
Hai-Gang GONG

Author(s):  
Jongeun Choi ◽  
Dejan Milutinović

This tutorial paper presents the expositions of stochastic optimal feedback control theory and Bayesian spatiotemporal models in the context of robotics applications. The presented material is self-contained so that readers can grasp the most important concepts and acquire knowledge needed to jump-start their research. To facilitate this, we provide a series of educational examples from robotics and mobile sensor networks.


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