Artificial Pheromone for Path Selection by a Foraging Swarm of Robots

Foraging robots involved in a search and retrieval task may create paths to navigate faster in their environment. In this context, a swarm of robots that has found several resources and created different paths may benefit strongly from path selection. Path selection enhances the foraging behavior by allowing the swarm to focus on the most profitable resource with the possibility for unused robots to stop participating in the path maintenance and to switch to another task. In order to achieve path selection, we implement virtual ants that lay artificial pheromone inside a network of robots. Virtual ants are local messages transmitted by robots; they travel along chains of robots and deposit artificial pheromone on the robots that are literally forming the chain and indicating the path. The concentration of artificial pheromone on the robots allows them to decide whether they are part of a selected path. We parameterize the mechanism with a mathematical model and provide an experimental validation using a swarm of 20 real robots. We show that our mechanism favors the selection of the closest resource is able to select a new path if a selected resource becomes unavailable and selects a newly detected and better resource when possible. As robots use very simple messages and behaviors, the system would be particularly well suited for swarms of microrobots with minimal abilitie

Negotiation of goal direction for cooperative transport

In this paper, we study the cooperative transport of a heavy object by a group of robots towards a goal. We investigate the case in which robots have partial and noisy knowledge of the goal direction and can not perceive the goal itself. The robots have to coordinate their motion to apply enough force on the object to move it. Furthermore, the robots should share knowledge in order to collectively improve their estimate of the goal direction and transport the object as fast and as accurately as possible towards the goal. We propose a bio-inspired mechanism of negotiation of direction that is fully distributed. Four different strategies are implemented and their performances are compared on a group of four real robots, varying the goal direction and the level of noise. We identify a strategy that enables effcient coordination of motion of the robots. Moreover, this strategy lets the robots improve their knowledge of the goal direction. Despite significant noise in the robots' communication, we achieve effective cooperative transport towards the goal and observe that the negotiation of direction entails interesting properties of robustness

Teamwork in a swarm of robots: an experiment in search and retrieval

In this thesis, we investigate the problem of path formation and prey retrieval in a swarm of robots. We present two swarm intelligence control mechanisms used for distributed robot path formation. In the first, the robots form linear chains. We study three variants of robot chains, which vary in the degree of motion allowedto the chain structure. The second mechanism is called vectorfield. In this case,the robots form a pattern that globally indicates the direction towards a goal orhome location. Both algorithms were designed following the swarm robotics controlprinciples: simplicity of control, locality of sensing and communication, homogeneityand distributedness.We test each controller on a task that consists in forming a path between twoobjects—the prey and the nest—and to retrieve the prey to the nest. The difficultyof the task is given by four constraints. First, the prey requires concurrent, physicalhandling by multiple robots to be moved. Second, each robot’s perceptual rangeis small when compared to the distance between the nest and the prey; moreover,perception is unreliable. Third, no robot has any explicit knowledge about theenvironment beyond its perceptual range. Fourth, communication among robots isunreliable and limited to a small set of simple signals that are locally broadcast.In simulation experiments we test our controllers under a wide range of conditions,changing the distance between nest and prey, varying the number of robotsused, and introducing different obstacle configurations in the environment. Furthermore,we tested the controllers for robustness by adding noise to the different sensors,and for fault tolerance by completely removing a sensor or actuator. We validate thechain controller in experiments with up to twelve physical robots. We believe thatthese experiments are among the most sophisticated examples of self-organisationin robotics to date.Doctorat en Sciences de l'ingénieurinfo:eu-repo/semantics/nonPublishe

Teamwork in a swarm of robots: an experiment in search and retrieval

In this thesis, we investigate the problem of path formation and prey retrieval in a swarm of robots. We present two swarm intelligence control mechanisms used for distributed robot path formation. In the first, the robots form linear chains. We study three variants of robot chains, which vary in the degree of motion allowedto the chain structure. The second mechanism is called vectorfield. In this case,the robots form a pattern that globally indicates the direction towards a goal orhome location. Both algorithms were designed following the swarm robotics controlprinciples: simplicity of control, locality of sensing and communication, homogeneityand distributedness.We test each controller on a task that consists in forming a path between twoobjects—the prey and the nest—and to retrieve the prey to the nest. The difficultyof the task is given by four constraints. First, the prey requires concurrent, physicalhandling by multiple robots to be moved. Second, each robot’s perceptual rangeis small when compared to the distance between the nest and the prey; moreover,perception is unreliable. Third, no robot has any explicit knowledge about theenvironment beyond its perceptual range. Fourth, communication among robots isunreliable and limited to a small set of simple signals that are locally broadcast.In simulation experiments we test our controllers under a wide range of conditions,changing the distance between nest and prey, varying the number of robotsused, and introducing different obstacle configurations in the environment. Furthermore,we tested the controllers for robustness by adding noise to the different sensors,and for fault tolerance by completely removing a sensor or actuator. We validate thechain controller in experiments with up to twelve physical robots. We believe thatthese experiments are among the most sophisticated examples of self-organisationin robotics to date.Doctorat en Sciences de l'ingénieurinfo:eu-repo/semantics/nonPublishe

Agent-based approach to dynamic task allocation

We investigate a multi-agent algorithm inspired by the task allocation behavior of social insects for the solution of dynamic task allocation problems in stochastic environments. The problems consist of a certain number of machines and different kinds of tasks. The machines are identical and able to carry out each task. A setup, which is linked to a fixed cost, is required to switch from one task to another. Agents, which are inspired by the model of division of labour in social insects, are in charge of the machines. Our work is based on previously introduced models described by Cicirello et al. [7] and by Campos et al. [6]. Improvements and their effect on the results are highlighted.SCOPUS: cp.kinfo:eu-repo/semantics/publishe

Swarm Intelligence manuscript No. (will be inserted by the editor) Path Formation in a Robot Swarm Self-Organized Strategies to Find Your Way Home

Abstract We present two swarm intelligence control mechanisms used for distributed robot path formation. In our first approach the robots form linear chains. We study three variants of robot chains, which vary in the degree of motion allowed to the chain structure. Our second approach is called forcefield. In this case, the robots form a pattern that globally indicates the direction towards a goal or home location. We test each controller on a task that consists in forming a path between two objects which an individual robot cannot perceive simultaneously. Our simulation experiments show promising results. All the controllers are able to form paths also in complex obstacle environments, and exhibit very good scalability. Additionally, we observe that chains perform better for smaller robot group sizes, while forcefield performs better for larger groups.

An insect-based algorithm for the dynamic task allocation problem

info:eu-repo/semantics/publishe

Heterogeneous Dynamic Task Allocation Problem

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