Swarm robotics: a review from the swarm engineering perspective <s> Collective exploration Description <s> This paper considers the problem of deploying a mobile sensor network in an unknown environment. A mobile sensor network is composed of a distributed collection of nodes, each of which has sensing, computation, communication and locomotion capabilities. Such networks are capable of self-deployment; i.e., starting from some compact initial configuration, the nodes in the network can spread out such that the area ‘covered’ by the network is maximized. In this paper, we present a potential-field-based approach to deployment. The fields are constructed such that each node is repelled by both obstacles and by other nodes, thereby forcing the network to spread itself throughout the environment. The approach is both distributed and scalable. <s> BIB001 </s> Swarm robotics: a review from the swarm engineering perspective <s> Collective exploration Description <s> We describe techniques for coordinating the actions of large numbers of small-scale robots to achieve useful large-scale results in surveillance, reconnaissance, hazard detection, and path finding. We exploit the biologically inspired notion of a “virtual pheromone,” implemented using simple transceivers mounted atop each robot. Unlike the chemical markers used by insect colonies for communication and coordination, our virtual pheromones are symbolic messages tied to the robots themselves rather than to fixed locations in the environment. This enables our robot collective to become a distributed computing mesh embedded within the environment, while simultaneously acting as a physical embodiment of the user interface. This leads to notions of world-embedded computation and world-embedded displays that provide different ways to think about robot colonies and the types of distributed computations that such colonies might perform. <s> BIB002 </s> Swarm robotics: a review from the swarm engineering perspective <s> Collective exploration Description <s> The emerging technology of wireless sensor network (WSN) is expected to provide a broad range of applications, such as battlefield surveillance, environmental monitoring, smart spaces and so on. The coverage problem is a fundamental issue in WSN, which mainly concerns with a fundamental question: How well a sensor field is observed by the deployed sensors? To optimize network coverage, the traditional approach is to deploy a large amount of stationary sensor nodes and then to schedule their sensing activities in an efficient way. Recently, mobile sensor nodes have received many attentions since network performance can be greatly improved by using just a few of mobile nodes. We categorize three typical application scenarios of applying mobile nodes, and we then highlight the issues and identify the objectives of optimizing network coverage by leveraging mobile nodes. We further survey recent contributions on the design of nodes' movement strategy to optimize network coverage in each application scenario. <s> BIB003 </s> Swarm robotics: a review from the swarm engineering perspective <s> Collective exploration Description <s> Swarm robotics draws inspiration from decentralized self-organizing biological systems in general and from the collective behavior of social insects in particular. In social insect colonies, many tasks are performed by higher order group or team entities, whose task-solving capacities transcend those of the individual participants. In this paper, we investigate the emergence of such higher order entities. We report on an experimental study in which a team of physical robots performs a foraging task. The robots are "identical" in hardware and control. They make little use of memory and take actions purely on the basis of local information. Our study advances the current state of the art in swarm robotics with respect to the number of real-world robots engaging in teamwork (up to 12 robots in the most challenging experiment). To the best of our knowledge, in this paper we present the first self-organized system of robots that displays a dynamical hierarchy of teamwork (with cooperation also occurring among higher order entities). Our study shows that teamwork requires neither individual recognition nor differences between individuals. This result might also contribute to the ongoing debate on the role of these characteristics in the division of labor in social insects. <s> BIB004 </s> Swarm robotics: a review from the swarm engineering perspective <s> Collective exploration Description <s> We consider a swarm of robots equipped with an infrared range and bearing device that is able both to make estimates of the relative distance and angle between two robots in line-of-sight and to transfer data between them. Through the infrared range and bearing device, the robots create a line-of-sight mobile ad hoc network. We investigate different ways to implement a swarm-level distributed navigation function exploiting the routing information gathered within this network. In the scenario we consider, a number of different events present themselves in different locations. To be serviced, each event requires that a robot with the appropriate skills comes to its location. We present two swarm-level solutions for guiding the navigation of the selected robots towards the events. We use a bio-inspired ad hoc network routing protocol to dynamically find and maintain paths between a robot and an event location in the mobile line-of-sight network, and use them to guide the robot to its goal. The performance of the two approaches is studied in a number of network scenarios presenting different density, mobility, and bandwidth availability. <s> BIB005 </s> Swarm robotics: a review from the swarm engineering perspective <s> Collective exploration Description <s> This paper introduces three strategies to deploy a swarm of robots in unknown environments for a search task, aiming to reduce the total swarm energy cost with rapid operation for applications such as disaster mitigation. We are motivated by current research on flying robot swarms. <s> BIB006 </s> Swarm robotics: a review from the swarm engineering perspective <s> Collective exploration Description <s> We present a communication based navigation algorithm for robotic swarms. It lets robots guide each other's navigation by exchanging messages containing navigation information through the wireless network formed among the swarm. We study the use of this algorithm in two different scenarios. In the first scenario, the swarm guides a single robot to a target, while in the second, all robots of the swarm navigate back and forth between two targets. In both cases, the algorithm provides efficient navigation, while being robust to failures of robots in the swarm. Moreover, we show that in the latter case, the system lets the swarm self-organize into a robust dynamic structure. This self-organization further improves navigation efficiency, and is able to find shortest paths in cluttered environments. We test our system both in simulation and on real robots. <s> BIB007 </s> Swarm robotics: a review from the swarm engineering perspective <s> Collective exploration Description <s> We study self-organized cooperation between heterogeneous robotic swarms. The robots of each swarm play distinct roles based on their different characteristics. We investigate how the use of simple local interactions between the robots of the different swarms can let the swarms cooperate in order to solve complex tasks. We focus on an indoor navigation task, in which we use a swarm of wheeled robots, called foot-bots, and a swarm of flying robots that can attach to the ceiling, called eye-bots. The task of the foot-bots is to move back and forth between a source and a target location. The role of the eye-bots is to guide foot-bots: they choose positions at the ceiling and from there give local directional instructions to foot-bots passing by. To obtain efficient paths for foot-bot navigation, eye-bots need on the one hand to choose good positions and on the other hand learn the right instructions to give. We investigate each of these aspects. Our solution is based on a process of mutual adaptation, in which foot-bots execute instructions given by eye-bots, and eye-bots observe the behavior of foot-bots to adapt their position and the instructions they give. Our approach is inspired by pheromone mediated navigation of ants, as eye-bots serve as stigmergic markers for foot-bot navigation. Through simulation, we show how this system is able to find efficient paths in complex environments, and to display different kinds of complex and scalable self-organized behaviors, such as shortest path finding and automatic traffic spreading. <s> BIB008
In this section, we analyze two collective behaviors that, together, can be used to achieve collective exploration of an environment: area coverage and swarm-guided navigation. The goal of area coverage is to deploy robots in an environment in order to create a regular or irregular grid of communicating robots. The obtained grid can be employed, for example, to monitor the environment for hazardous leaks or to guide other robots. We call the behavior necessary to guide the navigation of other robots swarm-guided navigation. Since the two behaviors are strongly linked, many works focus on both at the same time. Source of inspiration Area coverage and navigation are common behaviors of social animals. For example, ants use pheromone trails to find the shortest route between two points and bees directly communicate destinations in the environment by means of dances  . Area coverage has been intensively studied also by the wireless sensor networks (WSN) community. A survey of area coverage behaviors in WSN was conducted by BIB003 . Approaches In swarm robotics, the most common way to tackle area coverage is to use virtual physics-based design to obtain a grid covering the environment. Works on swarmguided navigation instead focus on communication, thus usually employ probabilistic finite state machines and take inspiration either from network routing protocols or natural systems. Results BIB002 used robots as "virtual pheromones". Some robots, which are already deployed, are able to create a gradient between the source and the target by exchanging messages. This gradient can then be exploited for navigation by other robots or by a human. BIB001 developed a behavior using virtual physics-based design. Each robot is repelled by other robots and by obstacles. This approach allows the robots to maximize the area covered and form a connected communication network (see Fig. 9(a) ). O'Hara and Balch (2007) presented a behavior that exploits pre-deployed sensors to perform foraging in an environment that can change over time. Through a distributed BellmanFord algorithm, a navigation route towards a specific goal is found and then followed by the mobile robots. BIB004 used chain formation (see Sect. 3.1.3) to link an object in the environment with the robot nest. The robots in the chain display a pattern of repeating colors to indicate in which direction is the nest and in which direction is the object. This information is used by the other robots to navigate the environment. Di BIB005 presented a work in which robots are able to navigate from a source to a target location. The proposed behavior is based on communication with other passive robots already available in the environment. These passive robots are assumed busy with other collective behaviors but are able to guide the navigating robots. BIB006 used a swarm of flying robots to achieve area coverage. In their work, the robots are deployed sequentially and each robot determines its position according to the position of the previously deployed robots. Only one or few robots, called explorers, are flying at the same time, whereas the great majority is attached to the ceiling and act as communication relay. One particular aspect of Stirling and Floreano's approach is the ability to explore an environment with a limited number of robots, as the robots can leave an area once it has been visited. This work was developed for the Swarmanoid project . BIB007 proposed a collective behavior based on network routing, capable of guiding a robot from a source area to a target. Similarly to what happens in packet routing, the robots keep a table of the distance of other robots with respect to the target. A robot can then use the entries in the table and reach the target. Ducatelle et al. studied two different experimental setups. In the first, only a single robot is moving while the others act as beacons to guide it. In the second, all the robots are moving between two points, creating a dynamic chain. BIB008 studied collective exploration using a heterogeneous robotic swarm (see Fig. 9(b) ). They tackled an indoor navigation task, in which a swarm of wheeled robots move back and forth between a source and a target location. The path of the wheeled robots are guided by a swarm of flying robots that can attach to the ceiling and overview the progress of the wheeled robots. Their solution is based on mutual adaptation: wheeled robots execute instructions given by flying robots, and flying robots observe the behavior of wheeled robots to adapt their position and the instructions they give. Ducatelle et al. developed the system using a probabilistic finite state machine together with techniques from network routing protocols.