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Title
Abstract
Figures
Introduction
Related Work 1) Robotic Covering
Problem Definition and Summary of Results
Analysis of the ANT-WALK-1 Algorithm
Analysis of ANT-WALK-2
Discussion
References
All Topics
Engineering
Mechanical Engineering

Distributed covering by ant-robots using evaporating traces

Profile image of Michael LindenbaumMichael Lindenbaum

1999, IEEE Transactions on Robotics and Automation

https://doi.org/10.1109/70.795795
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16 pages

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Abstract

Ants and other insects are known to use chemicals called pheromones for various communication and coordination tasks. In this paper, we investigate the ability of a group of robots, that communicate by leaving traces, to perform the task of cleaning the floor of an un-mapped building, or any task that requires the traversal of an unknown region. More specifically, we consider robots which leave chemical odor traces that evaporate with time, and are able to evaluate the strength of smell at every point they reach, with some measurement error. Our abstract model is a decentralized multiagent adaptive system with a shared memory, moving on a graph whose vertices are the floor-tiles. We describe three methods of covering a graph in a distributed fashion, using smell traces that gradually vanish with time, and show that they all result in eventual task completion, two of them in a time polynomial in the number of tiles. As opposed to existing traversal methods (e.g., depth first search), our algorithms are adaptive: they will complete the traversal of the graph even if some of the a(ge)nts die or the graph changes (edges/vertices added or deleted) during the execution, as long as the graph stays connected. Another advantage of our agent interaction processes is the ability of agents to use noisy information at the cost of longer cover time.

Figures (15)
Fig. 1. System of rooms divided into square tiles with a dynamic obstacle. Two cleaning robots are shown with their directional smell traces. Note that the traces degrade with time.
Fig. 1. System of rooms divided into square tiles with a dynamic obstacle. Two cleaning robots are shown with their directional smell traces. Note that the traces degrade with time.
Fig. 2. Four cycles OF ANT-WALK-1 are needed to traverse a floor with four tiles. Also shown is the corresponding directed graph with the smell-labels on its edges. Note that there are two labels on each edge, to designate the trace intensity in each direction of the edge.
Fig. 2. Four cycles OF ANT-WALK-1 are needed to traverse a floor with four tiles. Also shown is the corresponding directed graph with the smell-labels on its edges. Note that there are two labels on each edge, to designate the trace intensity in each direction of the edge.
Fig. 3. Hard case for the ANT-WALK-1 rule. There are n vertices, and about 1.25n edges. The diameter is about 0.8, and the time needed to traverse it may be as long as O(n"). The dotted arrows show the worst case where each triangle of vertices is a “trap” that causes the ant to go back to its starting point.
Fig. 3. Hard case for the ANT-WALK-1 rule. There are n vertices, and about 1.25n edges. The diameter is about 0.8, and the time needed to traverse it may be as long as O(n"). The dotted arrows show the worst case where each triangle of vertices is a “trap” that causes the ant to go back to its starting point.
Covering by ANT-WALK-1, shape #3, on a 14 x 14 matrix Num of Ants = 3; Total area = 161; Cleaned area = 161 Fig. 4. Simulated evolution of three agents, oriented by the ANT-WALK-1 rule. The gray-level is proportional to the number of visits in each tile. Note that most (90%) of the area is cleaned within 80% of the time.
Covering by ANT-WALK-1, shape #3, on a 14 x 14 matrix Num of Ants = 3; Total area = 161; Cleaned area = 161 Fig. 4. Simulated evolution of three agents, oriented by the ANT-WALK-1 rule. The gray-level is proportional to the number of visits in each tile. Note that most (90%) of the area is cleaned within 80% of the time.
Covering by ANT-WALK-1, shape #3, on a 14 x 14 matrix
Covering by ANT-WALK-1, shape #3, on a 14 x 14 matrix
Covering by ANT-WALK-2, shape #3, on a 14 x 14 matrix
Covering by ANT-WALK-2, shape #3, on a 14 x 14 matrix
Covering by ANT-WALK-2, shape #3, on a 14 x 14 matrix
Covering by ANT-WALK-2, shape #3, on a 14 x 14 matrix
Fig. 8. Time of covering t;, versus k—the number of robots, with varying amount of sensing-noise, for the three ANT-WALK algorithms. Note the different scales on the vertical axis. It can be seen that ANT-WALK-2 has best performance in the given test case. Fig. 7. Three agents, oriented by the ANT-WALK-2 rule, with sensory-noise level of ten units. Note that even with noise, this algorithm is much faster than ANT-WALK-1; it is also advantageous over the ordinary DFS which is extremely vulnerable to sensing errors.
Fig. 8. Time of covering t;, versus k—the number of robots, with varying amount of sensing-noise, for the three ANT-WALK algorithms. Note the different scales on the vertical axis. It can be seen that ANT-WALK-2 has best performance in the given test case. Fig. 7. Three agents, oriented by the ANT-WALK-2 rule, with sensory-noise level of ten units. Note that even with noise, this algorithm is much faster than ANT-WALK-1; it is also advantageous over the ordinary DFS which is extremely vulnerable to sensing errors.
Fig. 9. The same data of the previous figure is here sorted by noise-level. It clearly shows the VERTEX-ANT-WALK is best in the absence of noise, while ANT-WALK-2 wins in noisy conditions.
Fig. 9. The same data of the previous figure is here sorted by noise-level. It clearly shows the VERTEX-ANT-WALK is best in the absence of noise, while ANT-WALK-2 wins in noisy conditions.
Fig. 10. Time of covering t;, versus k—the number of robots, with varying amount of sensing-noise, for the three ANT-WALK algorithms, averaging on 10 coverings.
Fig. 10. Time of covering t;, versus k—the number of robots, with varying amount of sensing-noise, for the three ANT-WALK algorithms, averaging on 10 coverings.
Fig. 11. Same data (average of 10 covers) of the previous figure, sorted by noise-level. Here again ANT-WALK-2 wins with noisy sensors.
Fig. 11. Same data (average of 10 covers) of the previous figure, sorted by noise-level. Here again ANT-WALK-2 wins with noisy sensors.
Covering by ANT-WALK, shape #5, on a 30 x 30 matrix Num of Ants = 10; Total area = 338; Cleaned area =
Covering by ANT-WALK, shape #5, on a 30 x 30 matrix Num of Ants = 10; Total area = 338; Cleaned area =
Covering by VERTEX~ANT-WALK, shape #0, on a 8 x 8 matrix
Covering by VERTEX~ANT-WALK, shape #0, on a 8 x 8 matrix
Covering by VERTEX-ANT-WALK, shape #0, on a 8 x» 8 matrix
Covering by VERTEX-ANT-WALK, shape #0, on a 8 x» 8 matrix
Covering by VERTEX-ANT-WALK, shape #0, on a 8 x 8 matrix
Covering by VERTEX-ANT-WALK, shape #0, on a 8 x 8 matrix

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    Amal El Fallah Seghrouchni

    PRIMA 2012: Principles and Practice of Multi-Agent Systems, 2012

    The Multi-Agent Patrolling task constitutes a challenging issue for the MAS field and has the potential to cover a variety of domains ranging from agent-based simulations to distributed system design. Several techniques have been proposed in the last few years to address the basic multi-agent patrolling task. Recently, a variation of this task was proposed, in which agents can enter or leave the task at will: the patrolling task with an open system setting. A few centralized strategies were also described to address this new problem. In this article, we propose to adapt to a dynamic population a completely decentralized strategy that was proposed for the original basic patrolling task: an auction-based strategy in which agents trade the nodes they have to visit. We describe and compare several entry and exit algorithms on various graph topologies and show the interest of basing these mechanisms on geographical proximity. Finally, we compare this strategy to the centralized ones on simulations with multiple variations in the population of agents, and show that it provides a strong stability and reactivity to changes in the population of agents.

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    Chaotic Traversal (CHAT): Very Large Graphs Traversal Using Chaotic Dynamics
    Kittichai Lavangnananda

    International Journal of Bifurcation and Chaos

    Graph Traversal algorithms can find their applications in various fields such as routing problems, natural language processing or even database querying. The exploration can be considered as a first stepping stone into knowledge extraction from the graph which is now a popular topic. Classical solutions such as Breadth First Search (BFS) and Depth First Search (DFS) require huge amounts of memory for exploring very large graphs. In this research, we present a novel memoryless graph traversal algorithm, Chaotic Traversal (CHAT) which integrates chaotic dynamics to traverse large unknown graphs via the Lozi map and the Rössler system. To compare various dynamics effects on our algorithm, we present an original way to perform the exploration of a parameter space using a bifurcation diagram with respect to the topological structure of attractors. The resulting algorithm is an efficient and nonresource demanding algorithm, and is therefore very suitable for partial traversal of very larg...

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    Biologically Inspired Intelligence with Applications on Robot Navigation
    Chaomin Luo

    Artificial Intelligence - Emerging Trends and Applications, 2018

    Biologically inspired intelligence technique, an important embranchment of series on computational intelligence, plays a crucial role for robotics. The autonomous robot and vehicle industry has had an immense impact on our economy and society and this trend will continue with biologically inspired neural network techniques. In this chapter, multiple robots cooperate to achieve a common coverage goal efficiently, which can improve the work capacity, share the coverage tasks, and reduce the completion time by a biologically inspired intelligence technique, is addressed. In many real-world applications, the coverage task has to be completed without any prior knowledge of the environment. In this chapter, a neural dynamics approach is proposed for complete area coverage by multiple robots. A bio-inspired neural network is designed to model the dynamic environment and to guide a team of robots for the coverage task. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting neural equation. Each mobile robot treats the other robots as moving obstacles. Each robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot position. The proposed model algorithm is computationally simple. The feasibility is validated by four simulation studies.

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    Self-Adaptation of a Heterogeneous Swarm of Mobile Robots to a Covered Area
    Marek Bundzel

    Applied Sciences, 2020

    An original swarm-based method for coordination of groups of mobile robots with a focus on the self-organization and self-adaptation of the groups is presented in this paper. The method is a nature-inspired decentralized algorithm that uses artificial pheromone marks and enables the cooperation of different types of independent reactive agents that operate in the air, on the ground, or in the water. The advantages of our solution include scalability, adaptability, and robustness. The algorithm worked with variable numbers of agents in the groups. It was resistant against failures of the individual robots. A transportation control algorithm that ensured the spreading of different types of agents across exploration space with different types of environments was introduced and tested. We established that our swarm control algorithm was able to successfully control three basic behaviors: space exploration, population management, and transportation. The behaviors were able to run simulta...

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