Cooperative Cleaners: A Study in Ant Robotics

Wagner, Israel A.; Altshuler, Yaniv; Yanovski, Vladimir; Bruckstein, Alfred M.

doi:10.1177/0278364907085789

Cited by 100 publications

(57 citation statements)

References 54 publications

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“…Ant behaviors have inspired algorithms for multi-agent problems such as searching and covering; see, e.g., [37]- [38]. Multi-robot formation in continuous and grid environments has been studied recently by Sugihara, Suzuki, Yamashita, and Dumitrescu; see [15], [33], and [34].…”

Section: Related Workmentioning

confidence: 99%

The Freeze-Tag Problem: How to Wake Up a Swarm of Robots

et al. 2006

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An optimization problem that naturally arises in the study of swarm robotics is the Freeze-Tag Problem (FTP) of how to awaken a set of "asleep" robots, by having an awakened robot move to their locations. Once a robot is awake, it can assist in awakening other slumbering robots. The objective is to have all robots awake as early as possible. While the FTP bears some resemblance to problems from areas in combinatorial optimization such as routing, broadcasting, scheduling, and covering, its algorithmic characteristics are surprisingly different.We consider both scenarios on graphs and in geometric environments. In graphs, robots sleep at vertices and there is a length function on the edges. Awake robots travel along edges, with time depending on edge length. For most scenarios, we consider the offline version of the problem, in which each awake robot knows the position of all other robots. We prove that the problem is NP-hard, even for the special case of star graphs. We also establish hardness of approximation, showing that it is NP-hard to obtain an approximation factor better than 5 3 , even for graphs of bounded degree. These lower bounds are complemented with several positive algorithmic results, including:• We show that the natural greedy strategy on star graphs has a tight worst-case performance of 7 3 and give a polynomial-time approximation scheme (PTAS) for star graphs.• We give a simple O(log )-competitive online algorithm for graphs with maximum degree and locally bounded edge weights. • We give a PTAS, running in nearly linear time, for geometrically embedded instances. Introduction.The following problem naturally arises in the study of swarm robotics. Consider a set of n robots, modeled as points in some metric space (e.g., vertices of an edge-weighted graph). Initially, there is one awake or active robot and all other robots are asleep, that is, in a stand-by mode. Our objective is to "wake up" all of the robots as quickly as possible. In order for an active robot to awaken a sleeping robot, the awake robot must travel to the location of the slumbering robot. Once awake, this new robot is available to assist in rousing other robots. The objective is to minimize the makespan, that is, the time when the last robot awakens.This awakening problem is reminiscent of the children's game of "freeze-tag", in which the person who is "it" tags other players to "freeze" them. A player remains "frozen" until an unfrozen player (who is not "it") rescues the frozen player by tagging him and thus unfreezing him. Our problem arises when there are a large number n of frozen players, and one (not "it") unfrozen player, whose goal it is to unfreeze the rest of the players as quickly as possible. (We do not take into consideration the effect of the person who is "it", who is likely running around and re-freezing the players that become defrosted!) As soon as a player becomes unfrozen, he is available to assist in helping other frozen players, so there is a cascading effect. Due to the similarity with this child's game, ...

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Section: Related Workmentioning

confidence: 99%

The Freeze-Tag Problem: How to Wake Up a Swarm of Robots

et al. 2006

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“…Detection of heat/cold or light of color c1 (robot carries an object) or of color c2 (free robot) can be respectively make by temperature or light sensors, detecting if robot marker who has just passed, carried an object or not (Sharkey, 2006) (Wagner et al, 2008). Ambient temperature or extinct light expresses the neutral state for robot-agent.…”

Section: Resultsmentioning

confidence: 99%

Optimization of Clustering Time by a Group of Autonomous Robots Making Use of an Exclusive Multi-Marking

Abdessemed¹

2010

Journal of Computer Science

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Problem statement: For solving complex issues, the current tendency goes towards the swarms behaviors, realized on a basis of collective interactions, which results from a cooperative work favoring exchanges between individuals of a same group at microscopic level and allowing the emergence of complex collective behaviors at macroscopic level. Many models were inspired by these attitudes to find simple rules, guiding mobile, autonomous robots with limited capacities in their environment in order to achieve tasks like those of exploration, self-assembly and gathering. Multimarking technique as indirect communication inside the same robots group can optimize time of such achievements Approach: A method based on the reversed emergence principle combined to a genetic algorithm is presented here, making evolve a global behavior inside simulated robots group called agent-robots, with an aim to find the micro-rules forming a heap according to two approaches. The first approach accomplishes an ordinary grouping and the second one, which we propose, based on the exclusive multi-marking principle. The control device, guiding these robots-agent to succeed this task, functions on a basis of sensor-motor rules being used to arbitrate between a given number of elementary behaviors with which we equip each one of them initially. Results: Simulation results, implemented according to a reactive agent's model, making it possible to show the consistency of the detected rules and the efficient of the proposed approach in comparison with the ordinary one, are provided and commented. The time optimization of grouping by robots like these can have a huge economic and strategic impact in sectors as important as industry, agriculture and military domain. Conclusion:Like examples, we can quote the grouping of goods in a warehouse, the grouping of ores from mines, the grouping of vegetables and fruits in gardens and the recovery of weapons, in real time, from a battle field. This work can be generalized, in the future, to the multi-heap formation to perform the classification task according to given criteria.

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“…Most of these approaches divide the environment into cells, also called regions, that are explored one by one iteratively until the global area is covered. Furthermore, on-line algorithms are comparable to our approach with regard to hardware cost and complexity, since they all make use of small inexpensive robots with few sensors, and are bounded in their exploration time [9].…”

Section: On-line Algorithmsmentioning

confidence: 99%

“…Furthermore, a very accurate formalisation of the online exploration problem has been provided by Wagner et al [9] and it would thus be redundant within the scope of the current work.…”

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confidence: 99%

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Rapid exploration of unknown areas through dynamic deployment of mobile and stationary sensor nodes

Ferranti

Trigoni

Levene

2008

Auton Agent Multi-Agent Syst

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When an emergency occurs within a building, it may be initially safer to send autonomous mobile nodes, instead of human responders, to explore the area and identify hazards and victims. Exploring all the area in the minimum amount of time and reporting back interesting findings to the human personnel outside the building is an essential part of rescue operations. Our assumptions are that the area map is unknown, there is no existing network infrastructure, long-range wireless communication is unreliable and nodes are not locationaware. We take into account these limitations, and propose an architecture consisting of both mobile nodes (robots, called agents) and stationary nodes (inexpensive smart devices, called tags). As agents enter the emergency area, they sprinkle tags within the space to label the environment with states. By reading and updating the state of the local tags, agents are able to coordinate indirectly with each other, without relying on direct agent-to-agent communication. In addition, tags wirelessly exchange local information with nearby tags to further assist agents in their exploration task. Our simulation results show that the proposed algorithm, which exploits both tag-to-tag and agent-to-tag communication, outperforms previous algorithms that rely only on agent-to-tag communication.

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Cooperative Cleaners: A Study in Ant Robotics

Cited by 100 publications

References 54 publications

The Freeze-Tag Problem: How to Wake Up a Swarm of Robots

The Freeze-Tag Problem: How to Wake Up a Swarm of Robots

Optimization of Clustering Time by a Group of Autonomous Robots Making Use of an Exclusive Multi-Marking

Rapid exploration of unknown areas through dynamic deployment of mobile and stationary sensor nodes

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