Distributed Search and Rescue with Robot and Sensor Teams
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Citations
Telepresence across the Ocean
Communication assisted Localization and Navigation for Networked Robots
Distributed Topology Correction for Flexible Connectivity Maintenance in Multi-Robot Systems
Multi-Robot Path Deconfliction through Prioritization by Path Prospects
Interacting particles with L\'{e}vy strategies: limits of transport equations for swarm robotic systems
References
Distributed algorithms for guiding navigation across a sensor network
Cooperative localization and control for multi-robot manipulation
Preliminary results in range-only localization and mapping
Experimental results in range-only localization with radio
Interacting with a Sensor Network
Related Papers (5)
Frequently Asked Questions (15)
Q2. What are the applications of this work?
Applications of this work cover search and rescue for first responders, monitoring and surveillance, and infrastructure protection.
Q3. What is the purpose of this paper?
Since different agents have different sensors and therefore different pieces of information, communication is necessary for tasking the network, sharing information, and for control.
Q4. What is the primary difficulty of SLAM?
The primary difficulty stems from the annular distribution of potential relative locations that results from a range only measurement.
Q5. How did the user navigate the fire?
Using an interactive device that can transmit directional feedback called a Flashlight [PR02] a human user was directed across the field.
Q6. What is needed to solve the problem of robot localization from rangeonly measurements?
What is needed is a compact way to represent annular distributions together with a computationally efficient way of combining annular distributions with each other and with Gaussian distributions.
Q7. What are the main problems of SLAM?
In truth, Markov and Monte Carlo methods have much more flexibility than the authors need; they can represent arbitrary distributions while the authors need only to deal with very well structured annular distributions.
Q8. How many sensors can be used to control a robot?
The robots can also switch1Each Mote sensor (http://today.CS.Berkeley.EDU/tos/) consists of an Atmel ATMega128 microcontroller a 916 MHz RF transceiver a UART and a 4Mbit serial flash.
Q9. What are the main topics of this paper?
The authors have been investigating the use of low cost radio beacons that can be placed in the environment by rescue personnel or carried by robots.
Q10. What are the main advantages of Markov and Monte Carlo methods?
In theory, Markov methods (probability grids) and Monte Carlo methods (particle filtering) have the flexibility to handle annular distributions.
Q11. How long do the robots stay in the building?
They stay there until they are asked to evacuate the building, at which point they use the original potential field to find the exit.
Q12. What is the problem of formation control?
The authors treat this as a problem of formation control where the motion of the team is modeled as an element of a Lie group, while the shape of the formation is a point in shape space.
Q13. How do the authors solve the problem of robot localization from rangeonly measurements?
The authors have adapted the well-known estimation techniques of Kalman filtering, Markov methods, and Monte Carlo localization to solve the problem of robot localization from rangeonly measurements [KS02] [SKS02].
Q14. What is the main purpose of this paper?
Localization in dynamic environments such as posed by search and rescue operations is difficult because no infrastructure can be presumed and because simple assumptions such as line of sight to known features can not be guaranteed.
Q15. What is the purpose of the research?
Each robot must use partial state information derived from its sensors and from the communication network to control in cooperation with other robots the distribution of robots and the motion of the team.