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This article presents a novel bio-inspired navigation principle for swarm robotics that is based on a technique of signal propagation that was inspired by slime mold. We evaluated this strategy in a variety of simulation experiments that simulates a collective cleaning scenario. This scenario includes several sub-tasks like exploration, information propagation and path finding. Using the slime mold-inspired strategy, the simulated robots successfully performed a collective cleaning scenario and showed the ability of finding the shortest path between two target places. Finally, the parameters of the strategy were optimized by artificial evolution and the discovered optima are discussed.

This paper presents a simple adaptation mechanism to automatically adjust the ratio of foragers to resters (division of labour) in a swarm of foraging robots and hence maximise the net energy income to the swarm. Three adaptation rules are introduced based on local sensing and communications. Individual robots use internal cues (successful food retrieval), environmental cues (collisions with teammates while searching for food) and social cues (teammate success in food retrieval) to dynamically vary the time spent foraging or resting. The paper investigates the effectiveness of a number of strategies based upon different combinations of cues, and demonstrates successful adaptive emergent division of labour. Strategies which employ the social cues are shown to lead to the fastest adaptation to changes in food density and we see that social cues have most impact when food density is low: robots need to cooperate more when energy is scarce.

We study the self-organized aggregation of a swarm of robots in a closed arena. We assume that the perceptual range of the robots are smaller than the size of the arena and the robots do not have information on the size of the swarm or the arena. Using a probabilistic aggregation behavior model inspired from studies of social insects, we propose a macroscopic model for predicting the final distribution of aggregates in terms of the parameters of the aggregation behavior, the arena size and the sensing characteristics of the robots. Specifically, we use the partition concept, developed in number theory, and its related results to build a discrete-time, non-spatial model of aggregation in swarm robotic systems under a number of simplifying assumptions. We provide simplistic simulations of self-organized aggregation using the aggregation behavior with different parameters and arena sizes. The results show that, despite the fact that the simulations did not explicitly enforce to satisfy the assumptions put forward by the macroscopic model, the final aggregate distributions predicted by the macroscopic model and obtained from simulations match.

The foraging scenario is important in robotics, because it has many different applications and demands several fundamental skills from a group of robots, such as collective exploration, shortest path finding, and efficient task allocation. Particularly for large groups of robots emergent behaviors are desired that are decentralized and based on local information only. But the design of such behaviors proved to be difficult because of the absence of a theoretical basis. In this paper, we present a macroscopic model based on partial differential equations for the foraging scenario with virtual pheromones as the medium for communication. From the model, the robot density, the food flow and a quantity describing qualitatively the stability of the behavior can be extracted. The mathematical model is validated in a simulation with a large number of robots. The predictions of the model correspond well to the simulation.

We present a methodology for characterizing, analyzing, and synthesizing swarm behaviors using both a macroscopic continuous model that represents a swarm as a continuum and a macroscopic discrete model that enumerates individual agents. Our methodology is applied to a dynamical model of ant house hunting, a decentralized process in which a colony attempts to emigrate to the best site among several alternatives. The model is hybrid because the colony switches between different sets of behaviors, or modes, during this process. Using the model in [1], we investigate the relation of site population growth to initial system state with an algorithm called Multi-Affine Reachability analysis using Conical Overapproximations () [2]. We then derive a microscopic hybrid dynamical model of an agent that respects the specifications of the global behavior at the continuous level. Our multi-level simulations demonstrate that we have produced a rigorously correct microscopic model from the macroscopic descriptions.

The field of multi-agent dynamic systems is an inter-disciplinary research field that has become very popular in the recent years in parallel with the significant interest in the practical applications of such systems in various areas including robotics. In this article we give a relatively short review of this field from the system dynamics and control perspective. We first focus on mathematical modelling of multi-agent systems paying particular attention on the agent dynamics models available in the literature. Then we present a number of problems on coordination and control of multi-agent systems which have gained significant attention recently and various approaches to these problems. Relevant to these problems and approaches, we summarize some of the recent results on stability, robustness, and performance of multi-agent dynamic systems which appeared in the literature. The article is concluded with some remarks on the implementation and application side of the control designs developed for multi-agent systems.

Swarm intelligence, and swarm robotics in particular, are reaching a point where leveraging the potential of communication within an artificial system promises to uncover new and varied directions for interesting research without compromising the key properties of swarm- intelligent systems such as self-organization, scalability, and robustness. However, the physical constraints of using radios in a robotic swarm are hardly obvious, and the intuitive models often used for describing such systems do not always capture them with adequate accuracy. In order to demonstrate this effectively in the classroom, certain tools can be used, including simulation and real robots. Most instructors currently focus on simulation, as it requires significantly less investment of time, money, and maintenance—but to really understand the differences between simulation and reality, it is also necessary to work with the real platforms from time to time. To our knowledge, our course may be the only one in the world where individual students are consistently afforded the opportunity to work with a networked multi-robot system on a tabletop. The e-Puck, a low-cost small-scale mobile robotic platform designed for educational use, allows us bringing real robotic hardware into the classroom in numbers sufficient to demonstrate and teach swarm-robotic concepts. We present here a custom module for local radio communication as a stackable extension board for the e-Puck, enabling information exchange between robots and also with any other IEEE 802.15.4-compatible devices. Transmission power can be modified in software to yield effective communication ranges as small as fifteen centimeters. This intentionally small range allows us to demonstrate interesting collective behavior based on information and control in a limited amount of physical space, where ordinary radios would typically result in a completely connected network. Here we show the use of this module facilitating a collective decision among a group of 10 robots.

We describe further progress towards the development of a MAV (micro aerial vehicle) designed as an enabling tool to investigate aerial flocking. Our research focuses on the use of low cost off the shelf vehicles and sensors to enable fast prototyping and to reduce development costs. Details on the design of the embedded electronics and the modification of the chosen toy helicopter are presented, and the technique used for state estimation is described. The fusion of inertial data through an unscented Kalman filter is used to estimate the helicopter’s state, and this forms the main input to the control system. Since no detailed dynamic model of the helicopter in use is available, a method is proposed for automated system identification, and for subsequent controller design based on artificial evolution. Preliminary results obtained with a dynamic simulator of a helicopter are reported, along with some encouraging results for tackling the problem of flocking.

The ability of robots to quickly and accurately localize their neighbors is extremely important in swarm robotics. Prior approaches generally rely either on global information provided by GPS, beacons, and landmarks, or complex local information provided by vision systems. In this paper we provide a new technique, based on trilateration. This system is fully distributed, inexpensive, scalable, and robust. In addition, the system provides a unified framework that merges localization with information exchange between robots. The usefulness of this framework is illustrated on a number of applications.

In swarm robotics, hundreds or thousands of robots have to reach a common goal autonomously. Usually, the robots are small and their abilities are very limited. The autonomy of the robots requires that the robots’ behaviors are purely based on their local perceptions, which are usually rather limited. If the robot swarm is able to join multiple instances of individual perceptions to one big global picture (e.g. to collectively construct a sort of map), then the swarm can perform efficiently and such a swarm can target complex tasks. We here present two approaches to realize ‘collective perception’ in a robot swarm. Both require only limited abilities in communication and in calculation. We compare these strategies in different environments and evaluate the swarm’s performance in simulations of fluctuating environmental conditions and with varying parameter settings.