25 research outputs found

    Collaborative Foraging Using a new Pheromone and Behavioral Model

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    International audiencewe consider the problem of foraging with multiple agents, in which agents must collect disseminate resources in an unknown and complex environment. An efficient foraging should benefit from the presence of multiple agents, where cooperation between agents is a key issue for improvements. To do so, we propose a new distributed foraging mechanism. The aim is to adopt a new behavioral model regarding sources' affluence and pheromone's management. Simulations are done by considering agents as autonomous robots with goods transportation capacity, up to swarms that consist of 160 robots. Results demonstrate that the proposed model gives better results than c-marking agent model

    A Decentralized Ant Colony Foraging Model Using Only Stigmergic Communication

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    International audienceThis paper addresses the problem of foraging by a coordinated team of robots. This coordination is achieved by markers deposited by robots. In this paper, we present a novel decentralized behavioral model for multi robot foraging named cooperative c-marking agent model. In such model, each robot makes a decision according to the affluence of resource locations, either to spread information on a large scale in order to attract more agents or the opposite. Simulation results show that the proposed model outperforms the well-known c-marking agent model

    Cooperative c-Marking Agents for the Foraging Problem

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    International audienceWe consider the problem of foraging with multiple agents, in which agents must collect disseminate resources in an unknown and complex environment. So far, reactive multi-agent systems have been proposed, where agents can perform simultaneously exploration and path planning. In this work, we aim to decrease exploration and foraging time by increasing the level of cooperation between agents; to this end, we present in this paper a novel pheromone modeling in which pheromone's propagation and evaporation are managed by agents. As in c-marking agents, our agents are provided with very limited perceptions, and they can mark their environment. Simulation results demonstrate that the proposed model outperforms the c-marking agent-based systems in a foraging mission

    An ACO-Inspired, Probabilistic, Greedy Approach to the Drone Traveling Salesman Problem

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    In recent years, major companies have done research on using drones for parcel delivery. Research has shown that this can result in significant savings, which has led to the formulation of various truck and drone routing and scheduling optimization problems. This paper explains and analyzes a new approach to the Drone Traveling Salesman Problem (DTSP) based on ant colony optimization (ACO). The ACO-based approach has an acceptance policy that maximizes the usage of the drone. The results reveal that the pheromone causes the algorithm to converge quickly to the best solution. The algorithm performs comparably to the MIP model, CP model, and EA of Rich & Ham (2018), especially in instances with a larger number of stops

    Human Swarm Interaction: An Experimental Study of Two Types of Interaction with Foraging Swarms

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    In this paper we present the first study of human-swarm interaction comparing two fundamental types of interaction, coined intermittent and environmental. These types are exemplified by two control methods, selection and beacon control, made available to a human operator to control a foraging swarm of robots. Selection and beacon control differ with respect to their temporal and spatial influence on the swarm and enable an operator to generate different strategies from the basic behaviors of the swarm. Selection control requires an active selection of groups of robots while beacon control exerts an influence on nearby robots within a set range. Both control methods are implemented in a testbed in which operators solve an information foraging problem by utilizing a set of swarm behaviors. The robotic swarm has only local communication and sensing capabilities. The number of robots in the swarm range from 50 to 200. Operator performance for each control method is compared in a series of missions in different environments with no obstacles up to cluttered and structured obstacles. In addition, performance is compared to simple and advanced autonomous swarms. Thirty-two participants were recruited for participation in the study. Autonomous swarm algorithms were tested in repeated simulations. Our results showed that selection control scales better to larger swarms and generally outperforms beacon control. Operators utilized different swarm behaviors with different frequency across control methods, suggesting an adaptation to different strategies induced by choice of control method. Simple autonomous swarms outperformed human operators in open environments, but operators adapted better to complex environments with obstacles. Human controlled swarms fell short of task-specific benchmarks under all conditions. Our results reinforce the importance of understanding and choosing appropriate types of human-swarm interaction when designing swarm systems, in addition to choosing appropriate swarm behaviors

    Collective construction of numerical potential fields for the foraging problem

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    We consider the problem of deploying a team of agents (robots) for the foraging problem. In this problem agents have to collect disseminated resources in an unknown environment. They must therefore be endowed with exploration and path-planning abilities. This paper presents a reactive multiagent system that is able to simultaneously perform the two desired activities~ - exploration and path-planning - in unknown and complex environments. To develop this multiagent system, we have designed a distributed and asynchronous version of Barraquand's algorithm that builds an optimal Artificial Potential Field (APF). Our algorithm relies on agents with very limited perceptions that only mark their environment with integer values. The algorithm does not require any costly mechanism to be present in the environment to manage dynamic phenomena such as evaporation or propagation. We show that the APF built by our algorithm converges to optimal paths. The model is extended to deal with the multi-sources foraging problem. Simulations show that it is more time-efficient than the standard pheromone-based ant algorithm. Moreover, our approach is also able to address the problem in any kind of environment such as mazes

    Swarm Robotics: An Extensive Research Review

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    Mean Field Behaviour of Collaborative Multi-Agent Foragers

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    Collaborative multi-agent robotic systems where agents coordinate by modifying a shared environment often result in undesired dynamical couplings that complicate the analysis and experiments when solving a specific problem or task. Simultaneously, biologically-inspired robotics rely on simplifying agents and increasing their number to obtain more efficient solutions to such problems, drawing similarities with natural processes. In this work we focus on the problem of a biologically-inspired multi-agent system solving collaborative foraging. We show how mean field techniques can be used to re-formulate such a stochastic multi-agent problem into a deterministic autonomous system. This de-couples agent dynamics, enabling the computation of limit behaviours and the analysis of optimality guarantees. Furthermore, we analyse how having finite number of agents affects the performance when compared to the mean field limit and we discuss the implications of such limit approximations in this multi-agent system, which have impact on more general collaborative stochastic problems

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

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    International audienceThe 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. 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. 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
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