Distributed Search and Rescue with Robot and Sensor Teams
Summary (2 min read)
Introduction
- An ad-hoc network is formed by a group of mobile hosts upon a wireless local network interface.
- It is a temporary network formed without the aid of any established infrastructure or centralized administration.
- The authors combine networking, sensing, and control to control the flow of information in search and rescue in unknown environments.
1 Motivation
- The authors consider search and rescue applications in which heterogeneous groups of agents (humans, robots, static and mobile sensors) enter an unknown building and disperse while following gradients in temperature and concentration of toxins, and looking for immobile humans.
- The agents deploy the static sensors and maintain line of sight visibility and communication connectivity whenever possible.
- It is a temporary network formed without the aid of any established infrastructure or centralized administration.
- A sensor network consists of a collection of sensors and distributed over some area that form an ad-hoc network.
- The authors combine networking, sensing, and control to control the flow of information in search and rescue in unknown environments.
2 Localization
- 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.
- 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.
- 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].
- The primary difficulty stems from the annular distribution of potential relative locations that results from a range only measurement.
- Markov methods (probability grids) and Monte Carlo methods (particle filtering) have the flexibility to handle annular distributions.
3 Information Flow
- Sensors detect information about the area they cover.
- Users of the network (robots or people) can use this information as they traverse the network.
- Figure 1 shows the layout of a room in which a fire was started.
- The sensors computed multi-hop communication paths to a base station placed at the door.
- For each interaction, the user did a rotation scan until the Flashlight was pointed in the direction computed from the sensor data.
4 Control of a Network of Robots
- Robots augment the surveillance capabilities of a sensor network by using mobility.
- 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.
- The authors seek abstractions and control laws that allow partial state information to be used effectively and in a scalable manner.
- A Mote runs for approximately one month on two AA batteries.
- Between the potential fields (or temperature gradients) computed and stored in the sensor network (see Figure 2).
5 User Feedback
- When robots or people interact with the sensor network, it becomes an extension of their capabilities, basically extending their sensory systems and ability to act over a much large range.
- The authors have developed software that allows an intuitive, immersive display of environments.
- Using, panoramic imaging sensors that can be carried by small robots into the heart of a damaged structure, the display can be coupled to head mounted, head tracking sensors that enable a remote operator to look around in the environment without the delay associated with mechanical pan and tilt mechanisms.
- The data collected from imaging systems such as visible cameras and IR cameras are displayed on a wearable computer to give the responder the most accurate and current information.
- Distributed protocols collect data from the geographically dispersed sensor network and integrate this data into a global map such as a temperature gradient that can also be displayed on a wearable computer to the user.
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Citations
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References
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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.