Distinguishing Adaptive Search from Random Search in Robots and T cells

Fricke, G. Matthew; Black, Simon; Hecker, Joshua P.; Cannon, Judy L.; Moses, Melanie E.

doi:10.1145/2739480.2754794

Cited by 6 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Desert plants accelerate soil respiration in response to brief episodes of rainfall (Potts et al, 2014). Cytokines create chemical interactions that T cells follow, with the outcome that numbers of T cells increase at a location where pathogens are patchy and concentrated in time and space (Fricke et al, 2015;Chao et al, 2004). An interesting question is whether the reverse is also true.…”

Section: Algorithms Shaped By the Distribution Of Resourcesmentioning

confidence: 99%

The Evolution of the Algorithms for Collective Behavior

Gordon

2016

Cell Systems

View full text Add to dashboard Cite

Collective behavior is the outcome of a network of local interactions. Here, I consider collective behavior as the result of algorithms that have evolved to operate in response to a particular environment and physiological context. I discuss how algorithms are shaped by the costs of operating under the constraints that the environment imposes, the extent to which the environment is stable, and the distribution, in space and time, of resources. I suggest that a focus on the dynamics of the environment may provide new hypotheses for elucidating the algorithms that produce the collective behavior of cellular systems.

show abstract

Section: Algorithms Shaped By the Distribution Of Resourcesmentioning

confidence: 99%

The Evolution of the Algorithms for Collective Behavior

Gordon

2016

Cell Systems

View full text Add to dashboard Cite

show abstract

“…A key feature that influences search strategy is the distribution of resources in time and space (36, 38–40). Targets can be patchy, clustered into one location, or dispersed uniformly at random through the entire search area.…”

Section: Ant Foraging As a Model For T Cell Searchmentioning

confidence: 99%

Distributed Adaptive Search in T Cells: Lessons From Ants

et al. 2019

Self Cite

View full text Add to dashboard Cite

There are striking similarities between the strategies ant colonies use to forage for food and immune systems use to search for pathogens. Searchers (ants and cells) use the appropriate combination of random and directed motion, direct and indirect agent-agent interactions, and traversal of physical structures to solve search problems in a variety of environments. An effective immune response requires immune cells to search efficiently and effectively for diverse types of pathogens in different tissues and organs, just as different species of ants have evolved diverse search strategies to forage effectively for a variety of resources in a variety of habitats. Successful T cell search is required to initiate the adaptive immune response in lymph nodes and to eradicate pathogens at sites of infection in peripheral tissue. Ant search strategies suggest novel predictions about T cell search. In both systems, the distribution of targets in time and space determines the most effective search strategy. We hypothesize that the ability of searchers to sense and adapt to dynamic targets and environmental conditions enhances search effectiveness through adjustments to movement and communication patterns. We also suggest that random motion is a more important component of search strategies than is generally recognized. The behavior we observe in ants reveals general design principles and constraints that govern distributed adaptive search in a wide variety of complex systems, particularly the immune system.

show abstract

“…In [61,62], the authors proposed a search strategy for a team of mobile robotic sensors based on the T cell movement in lymph nodes and they showed that the distributions of the step-sizes taken by T cells are best described by a random walk with the Levy-like distribution. In [162], a bio-inspired random search strategy for the multi-robot system to efficiently localize targets in underwater search scenarios was proposed.…”

Section: Distributed Bio-inspired Random Search For Locating Static T...mentioning

confidence: 99%

“…The process of evolution and natural selection has led animals to optimize their foraging strategies. As a result, in recent years, many search algorithms have been inspired by the Levy walk random search method [61,62].…”

Section: Distributed Bio-inspired Algorithm For Search Of Moving Targetsmentioning

confidence: 99%

Decentralized Control of Three-Dimensional Mobile Robotic Sensor Networks

Nazarzehi¹

2016

Preprint

View full text Add to dashboard Cite

Decentralized control of mobile robotic sensor networks is a fundamental problem in robotics that has attracted intensive research in recent decades. Most of the existing works dealt with two-dimensional spaces. This report is concerned with the problem of decentralized self-deployment of mobile robotic sensor networks for coverage, search, and formation building in three-dimensional environments. The first part of the report investigates the problem of complete sensing coverage in three-dimensional spaces. We propose a decentralized random algorithm to drive mobile robotic sensors on the vertices of a truncated octahedral grid for complete sensing coverage of a bounded 3D area. Then, we develop a decentralized random algorithm for self deployment of mobile robotic sensors to form a desired geometric shape on the vertices of the truncated octahedral grid. The second part of this report studies the problem of search in 3D spaces. We present a distributed random algorithm for search in bounded three dimensional environments. The proposed algorithm utilizes an optimal three dimensional grid for the search task. Third, we study the problem of locating static and mobile targets in a bounded 3D space by a network of mobile robotic sensors. We introduce a novel decentralized bio-inspired random search algorithm for finding static and mobile objects in 3D areas. This algorithm combines the Levy flight random search mechanism with a 3D covering grid. Using this algorithm, the mobile robotic sensors randomly move on the vertices of the covering grid with the length of the movements follow a Levy fight distribution. This report studies the problem of 3D formation building in 3D spaces by a network of mobile robotic sensors. Decentralized consensus-based control law for the multi-robot system which results in forming a given geometric configuration from any initial positions in 3D environments is proposed. Then, a decentralized random motion coordination law for the multi-robot system for the case when the mobile robots are unaware of their positions in the configuration in three dimensional environments is presented. The proposed algorithms use some simple consensus rules for motion coordination and building desired geometric patterns. Convergence of the mobile robotic sensors to the given configurations are shown by extensive simulations. Moreover, a mathematically rigorous proof of convergence of the proposed algorithms to the given configurations are given. i v LIST OF FIGURES vi 4.4 Trajectories of mobile sensors performing bio-inspired Levy flight random search for locating sparsely located targets. mobile sensors' trajectories denoted by -, vertices of the common grid by o, targets by star. .

show abstract

Distinguishing Adaptive Search from Random Search in Robots and T cells

Cited by 6 publications

References 20 publications

The Evolution of the Algorithms for Collective Behavior

The Evolution of the Algorithms for Collective Behavior

Distributed Adaptive Search in T Cells: Lessons From Ants

Decentralized Control of Three-Dimensional Mobile Robotic Sensor Networks

Contact Info

Product

Resources

About