Distributed Search and Rescue with Robot and Sensor Teams

Kantor, George; Singh, Sanjiv; Peterson, Ronald; Rus, Daniela; Das, Arindam; Kumar, Vijay; Pereira, Guilherme A. S.; Spletzer, John

doi:10.1007/10991459_51

Cited by 125 publications

(98 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are several works describing complex approaches with cooperating sensor networks and robot groups (e.g., [12]). The sensor network measures the temperature gradient and communicates within the robot swarm.…”

Section: Derivation Of the Bio-inspired Solutionmentioning

confidence: 99%

Embodiment of Honeybee’s Thermotaxis in a Mobile Robot Swarm

Kengyel

Schmickl

Hamann

et al. 2011

Advances in Artificial Life. Darwin Meets Von Neumann

View full text Add to dashboard Cite

Abstract. Searching an area of interest based on environmental cues is a challenging benchmark task for an autonomous robot. It gets even harder to achieve if the goal is to aggregate a whole swarm of robots at such a target site after exhaustive exploration of the whole environment. When searching gas leakages or heat sources, swarm robotic approaches have been evaluated in recent years, which were, in part, inspired by biologically motivated control algorithms. Here we present a bio-inspired control program for swarm robots, which collectively explore the environment for a heat source to aggregate. Behaviours of young honeybees were embodied on a robot by adding thermosensors in 'virtual antennae'. This enables the robot to perform thermotaxis, which was evaluated in a comparative study of an egoistic versus a collective swarm approach.

show abstract

Section: Derivation Of the Bio-inspired Solutionmentioning

confidence: 99%

Embodiment of Honeybee’s Thermotaxis in a Mobile Robot Swarm

Kengyel

Schmickl

Hamann

et al. 2011

Advances in Artificial Life. Darwin Meets Von Neumann

View full text Add to dashboard Cite

show abstract

“…Multi-Agent Systems are a suitable approach to develop many multi-robot distributed applications such as: mine detecting [1] [2], search in damaged buildings [3] [4], fire fighting [5], and exploration of spaces [6] [7], where neither a map, nor a Global Switching Algorithm for Foraging (NC-SAF) which is the non cooperative version of C-SAF, c-marking which is a cooperative multi-agent foraging algorithm inspired from ant systems, based on the Artificial Potential Field (APF) and uses pseudo random walk for search task; and Non-Cooperative c-marking (NC-c-marking) which is the non cooperative version of the c-marking algorithm, is presented in Section 4. In Section 5, we present the proposed foraging framework.…”

Section: Introductionmentioning

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

“…Those applications vary from mine detecting [2] [3], search in damaged buildings [4] [5], fire fighting [6], and exploration of spaces [7] [8], where neither a map, nor a Global Positioning System (GPS) are available [9]. The random walk is the best option when there is some degree of uncertainty in the environment and a reduced perceptual capabilities [10] because it is simple, needs no memory and self-stabilizes.…”

Section: Introductionmentioning

confidence: 99%

Stigmergic MASA: A Stigmergy Based Algorithm for Multi-Target Search

Zedadra¹,

Séridi²,

Jouandeau³

et al. 2014

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-We explore the on-line problem of coverage where multiple agents have to find a target whose position is unknown, and without a prior global information about the environment. In this paper a novel algorithm for multi-target search is described, it is inspired from water vortex dynamics and based on the principle of pheromone-based communication. According to this algorithm, called S-MASA (Stigmergic Multi Ant Search Area), the agents search nearby their base incrementally using turns around their center and around each other, until the target is found, with only a group of simple distributed cooperative Ant like agents, which communicate indirectly via depositing/detecting markers. This work improves the search performance in comparison with random walk and S-random walk (stigmergic random walk) strategies, we show the obtained results using computer simulations.

show abstract

Distributed Search and Rescue with Robot and Sensor Teams

Cited by 125 publications

References 5 publications

Embodiment of Honeybee’s Thermotaxis in a Mobile Robot Swarm

Embodiment of Honeybee’s Thermotaxis in a Mobile Robot Swarm

A Cooperative Switching Algorithm for Multi-Agent Foraging

Stigmergic MASA: A Stigmergy Based Algorithm for Multi-Target Search

Contact Info

Product

Resources

About