Collaborative Foraging Using a new Pheromone and Behavioral Model

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

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

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

Social Odometry: Imitation Based Odometry in Collective Robotics

The improvement of odometry systems in collective robotics remains an important challenge for several applications. Social odometry is an online social dynamics which confers the robots the possibility to learn from the others. Robots neither share any movement constraint nor access to centralized information. Each robot has an estimate of its own location and an associated confidence level that decreases with distance traveled. Social odometry guides a robot to its goal by imitating estimated locations, confidence levels and actual locations of its neighbors. This simple online social form of odometry is shown to produce a self-organized collective pattern which allows a group of robots to both increase the quality of individuals’ estimates and efficiently improve their collective performanc

Efficient exploration of unknown indoor environments using a team of mobile robots

Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robots. To achieve efficient terrain coverage with the sensors of the robots, one first needs to identify unknown areas in the environment. Second, one has to assign target locations to the individual robots so that they gather new and relevant information about the environment with their sensors. This assignment should lead to a distribution of the robots over the environment in a way that they avoid redundant work and do not interfere with each other by, for example, blocking their paths. In this paper, we address the problem of efficiently coordinating a large team of mobile robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). This knowledge allows us to improve the distribution of robots over the environment compared to approaches lacking this capability. To autonomously determine the type of a place, we apply a classifier learned using the AdaBoost algorithm. The resulting classifier takes laser range data as input and is able to classify the current location with high accuracy. We additionally use a hidden Markov model to consider the spatial dependencies between nearby locations. Our approach to incorporate the information about the type of places in the assignment process has been implemented and tested in different environments. The experiments illustrate that our system effectively distributes the robots over the environment and allows them to accomplish their mission faster compared to approaches that ignore the place labels

Social odometry in populations of autonomous robots

Abstract. The improvement of odometry systems in collective robotics remains an important challenge for several applications. In this work, we propose a localisation strategy in which robots have no access to centralised information. Each robot has an estimate of its own location and an associated confidence level that decreases with distance travelled. Robots use estimates advertised by neighbouring robots to correct their own location estimates at each time-step. This simple online social form of odometry is shown to allow a group of robots to both increase the quality of individuals' estimates and efficiently improve their collective performance. Furthermore, social odometry produces a successful selforganised collective pattern

Towards Swarm Calculus: Urn Models of Collective Decisions and Universal Properties of Swarm Performance

Methods of general applicability are searched for in swarm intelligence with the aim of gaining new insights about natural swarms and to develop design methodologies for artificial swarms. An ideal solution could be a `swarm calculus' that allows to calculate key features of swarms such as expected swarm performance and robustness based on only a few parameters. To work towards this ideal, one needs to find methods and models with high degrees of generality. In this paper, we report two models that might be examples of exceptional generality. First, an abstract model is presented that describes swarm performance depending on swarm density based on the dichotomy between cooperation and interference. Typical swarm experiments are given as examples to show how the model fits to several different results. Second, we give an abstract model of collective decision making that is inspired by urn models. The effects of positive feedback probability, that is increasing over time in a decision making system, are understood by the help of a parameter that controls the feedback based on the swarm's current consensus. Several applicable methods, such as the description as Markov process, calculation of splitting probabilities, mean first passage times, and measurements of positive feedback, are discussed and applications to artificial and natural swarms are reported

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