Distributed Colony-Level Algorithm Switching for Robot Swarm Foraging

Hoff, Nicholas R.; Wood, Robert J.; Nagpal, Radhika

doi:10.1007/978-3-642-32723-0_30

Cited by 36 publications

(37 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It differs from VP in that the values the beacons store are integers (called cardinalities). They also propose three foraging algorithms in Hoff et al (2013) called gradient, sweeper and adaptive. In gradient algorithm, agents broadcast information about gradient to nest or food, they switch from beacons (beacons transmit values) to walkers (walkers use those values to decide where to move) according to some criteria.…”

Section: Proposed Taxonomymentioning

confidence: 99%

Multi-Agent Foraging: state-of-the-art and research challenges

Zedadra

Jouandeau²,

Séridi

et al. 2017

Complex Adapt Syst Model

View full text Add to dashboard Cite

BackgroundSwarm intelligence provides the design and implementation of systems composed of many simple individuals who interact locally and produce remarkable behavior as a whole (Dudek et al. 1996). It provides multiple benefits such as robustness where the performance of the system is not affected significantly with the failure of individuals, simplicity of computational and perceptual capabilities of individuals but still allowing global complex behaviors and scalability of the control mechanism that does not depend on the number of agents (Mitton and Simplot-Ryl 2014). The application of swarm intelligence to collective robotics is identified as Swarm Robotics in El Zoghby et al. (2014). Many artificial systems such as distributed computing systems and artificial intelligence systems are characterized by complex behaviors that emerge as a result of the nonlinear spatio-temporal interactions among a large number of system components at different levels of organization. These systems are known as Complex Adaptive Systems (CAS) as stated by Lansing (2003). Holland (2006) also considers CAS as dynamic systems able to adapt in and evolve with a changing environment. MAF problem is a benchmark problem for swarm robotics. It can be seen as a CAS and defined like in Niazi and Hussain Abstract Background: The foraging task is one of the canonical testbeds for cooperative robotics, in which a collection of robots has to search and transport objects to specific storage point(s). In this paper, we investigate the Multi-Agent Foraging (MAF) problem from several perspectives that we analyze in depth.Results: First, we define the Foraging Problem according to literature definitions. Then we analyze previously proposed taxonomies, and propose a new foraging taxonomy characterized by four principal axes: Environment, Collective, Strategy and Simulation, summarize related foraging works and classify them through our new foraging taxonomy. Then, we discuss the real implementation of MAF and present a comparison between some related foraging works considering important features that show extensibility, reliability and scalability of MAF systems Conclusions:Finally we present and discuss recent trends in this field, emphasizing the various challenges that could enhance the existing MAF solutions and make them realistic. Zedadra et al. Complex Adapt Syst Model (2017) et al. Complex Adapt Syst Model (2017) 5:3 (2012) as a system made up of multiple simple individuals which interact in a nonlinear fashion, thereby giving rise to global and often unpredictable behaviors. A good way to understand a CAS is to study them in special cases, thus to simulate dedicated behavior from particular perspectives. Holland (2006) states that the analysis of CAS is done through a combination of applied, theoretical and experimental methods (e.g. mathematics and computer simulations). Authors in Fortino and North (2013) state that Agent-Based Modeling (ABM) has proven to provide an effective set of tools for modeling and simulating different ...

show abstract

Section: Proposed Taxonomymentioning

confidence: 99%

Multi-Agent Foraging: state-of-the-art and research challenges

Zedadra

Jouandeau²,

Séridi

et al. 2017

Complex Adapt Syst Model

View full text Add to dashboard Cite

show abstract

“…A widely-used example is exploration, in which robots rely on a set of fundamental behaviors that help them spreading and dealing properly with tasks that are scattered across the unknown environment, e.g., to locate items that are of a particular interest [5], [6]. This behavior is mostly inspired from the well-known foraging behavior, which is observed in social colonies e.g., ants [7], [8]. Another fundamental behavior is to achieve a good coverage across large terrains.…”

Section: Related Workmentioning

confidence: 99%

Edge Detection in Static and Dynamic Environments using Robot Swarms

Khaluf

2017

2017 IEEE 11th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)

View full text Add to dashboard Cite

Abstract-Robotic systems offer an attractive solution for a large spectrum of real-world applications that are hosted in dangerous or inaccessible areas for humans. In such applications, one of the fundamental tasks is to detect and mark particular features (e.g., pollution areas) in order to develop a proper response. In this paper, we focus on the specific problem of environmental edge detection using a swarm of homogeneous robots that can sense and act distributively using a large number of individuals. In our study, we consider both static and dynamic 2D environments, in which the spatial distribution of the feature(s) may change over time. We verify our results using a set of physics-based simulations.

show abstract

“…A distributed adaptive foraging algorithm was proposed from two other ones [33], where robots can cooperate to choose the best algorithm. It adopts the principle of switching within the same algorithm [34].…”

Section: Related Workmentioning

confidence: 99%

A Cooperative Switching Algorithm for Multi-Agent Foraging

Zedadra

Séridi

Jouandeau

et al. 2016

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

Foraging is the act of searching for, and when found transporting objects to one or more sinks. Although foraging can be accomplished by one single robot operating individually, it can be supported more efficiently by multiple robots operating collectively. The efficiency of the group of robots can be sufficiently improved through coordination. Pheromone-based communication is well suited for the coordination of swarm of robots. It is one of the main subjects of swarm intelligence that provides intelligent global behaviors from simple local behaviors. The goal of this work, is to study the benefit of collective foraging via the implicit recruitment when one agent alerts the others to the location of food. We present therefore, a multi-agent foraging algorithm named Cooperative Switching Algorithm for Foraging (C-SAF) inspired from the classical ant system, where agents use pheromone to collectively search and avoid already visited cells, and to communicate the food location. C-SAF provides a quick search, optimal paths to return to nest and quick exploitation of food. We then generalize the algorithm to produce a flexible and extensible framework for foraging, that can be reused or extended by other foraging algorithms. A qualitative comparison with some related search and foraging algorithms is presented in this paper. A quantitative comparison shows that our algorithm outperforms the reference c-marking algorithm across a range of scenarios that differ in terms of agent, environment and food parameters.

show abstract

Distributed Colony-Level Algorithm Switching for Robot Swarm Foraging

Cited by 36 publications

References 20 publications

Multi-Agent Foraging: state-of-the-art and research challenges

Multi-Agent Foraging: state-of-the-art and research challenges

Edge Detection in Static and Dynamic Environments using Robot Swarms

A Cooperative Switching Algorithm for Multi-Agent Foraging

Contact Info

Product

Resources

About