Kilorobot Search and Rescue Using an Immunologically Inspired Approach. Singh, S. P. N. and Thayer, S. In Distributed Autonomous Robotic Systems (DARS), volume 5, pages 300-305, 2002.
abstract   bibtex   
This paper presents a new concept and simulated results for the distributed coordination of autonomous robot teams via the Immunology-derived Distributed Autonomous Robotics Architecture (IDARA) to perform select search and rescue (SAR) operations autonomously. Primarily designed for the coordination and control of �kilorobot� colonies, this architecture incorporates the immune system�s stochastic learning and reaction mechanisms to yield astute and adaptive responses to dynamic environmental conditions. This allows the architecture to vary actions from reactive to deliberative to result in a guided, yet stochastic, method that is ideal for dynamic operations such as terrain searching. IDARA was evaluated in a variety of SAR problem domains via computer simulations. These tests show that the IDARA framework is robust to noise and does not degrade when coordinating large colonies of robots consisting of up to 1,500 robots. By providing new levels of scalability in noisy environments, IDARA enables the full potential of micro-scale robotic for intelligent exploration, mapping, and SAR operations in a manner not afforded by traditional methods.
@INPROCEEDINGS{dars2002,
  author = {S. P. N. Singh and S. Thayer},
  title = {{Kilorobot Search and Rescue Using an Immunologically Inspired Approach}},
  booktitle = {{Distributed Autonomous Robotic Systems ({DARS})}},
  year = {2002},
  volume = {5},
  pages = {300-305},
  abstract = {{This paper presents a new concept and simulated results for the distributed
	
	coordination of autonomous robot teams via the Immunology-derived
	Distributed
	
	Autonomous Robotics Architecture (IDARA) to perform select search
	
	and rescue (SAR) operations autonomously. Primarily designed for the
	coordination
	
	and control of �kilorobot� colonies, this architecture incorporates
	the immune
	
	system�s stochastic learning and reaction mechanisms to yield astute
	and adaptive
	
	responses to dynamic environmental conditions. This allows the architecture
	to
	
	vary actions from reactive to deliberative to result in a guided,
	yet stochastic,
	
	method that is ideal for dynamic operations such as terrain searching.
	IDARA
	
	was evaluated in a variety of SAR problem domains via computer simulations.
	
	These tests show that the IDARA framework is robust to noise and does
	not degrade
	
	when coordinating large colonies of robots consisting of up to 1,500
	robots.
	
	By providing new levels of scalability in noisy environments, IDARA
	enables the
	
	full potential of micro-scale robotic for intelligent exploration,
	mapping, and SAR
	
	operations in a manner not afforded by traditional methods.}},
  owner = {SPNSPUB}
}
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