Swarm robotics: a review from the swarm engineering perspective <s> Collective transport <s> In Donald (1995), we described a manipulation task for coop erating mobile robots that can push large, heavy objects. There, we asked whether explicit local and global communication between the agents can be removed from a family of pushing protocols. In this article, we answer in the affirmative. We do so by using the general methods of Donald (1995), analyzing information invariants.We discuss several measures for the information complexity of the task: (1) How much internal state should the robot retain? (2) How many cooperating agents are required, and how much communication between them is necessary? (3) How can the robot change (side effect) the environment to record state or sensory information for performing a task? (4) How much information is provided by sensors? and (5) How much computation is required by the robot? To answer these questions, we develop a notion of information invariants. We develop a technique whereby one sensor can be constructed from others by adding, deleting, and reallocati... <s> BIB001 </s> Swarm robotics: a review from the swarm engineering perspective <s> Collective transport <s> In several species of ants, workers cooperate to retrieve large prey. Usually, one ant finds a prey item, tries to move it, and, when unsucessful for some time, recuits nestmates through direct contact or chemical marking. When a group of ants tries to move large prey, the ants change position and alignment until the prey can be moved toward the nest. A robotic implementation of this phenomenon is described. Although the robotic system may not appear to be very efficient, it is an interesting example of decentralized problem-solving by a group of robots, and it provides the first formalized model of cooperative transport in ants. Published in Robotics and Autonomous Systems30 (2000), 85-101. <s> BIB002 </s> Swarm robotics: a review from the swarm engineering perspective <s> Collective transport <s> Swarm robotics is a relatively new approach to the coordination of a system composed of a large number of autonomous robots. The coordination among the robots is achieved in a self-organised manner: the collective behaviour of the robots is the result of local interactions among robots, and between the robots and the environment. Each single robot typically has limited sensing, acting and computing abilities. The strength of swarm robotics lies in the properties of robustness, adaptivity and scalability of the group [5]. Foraging is a typical task considered in swarm robotics [4]. It can be decomposed in an exploration subtask followed by a transport subtask. The robotic metaphor consists in the search and retrieval of an object. Therefore, the nest is the metaphorical term for the goal and the prey is synonymous of the object to transport. Examples of applications of foraging are toxic waste cleanup, search and rescue, demining and collection of terrain samples. In our work, we address the case in which all robots completely lose sight of the nest during the exploration subtask of foraging. We assume that the robots have partial knowledge of the goal direction. For instance, they may have perceived the nest earlier and kept track of its approximate direction by means of odometry [2]. Using this approximate knowledge, if several robots attempt to transport a heavy prey in different directions they may fail to move the prey at all. Therefore, we introduce a mechanism to let the robots negotiate the goal direction. In order to meet the general principles of swarm robotics [5], this system is fully distributed and makes use of local communication only. <s> BIB003 </s> Swarm robotics: a review from the swarm engineering perspective <s> Collective transport <s> Distributed coordination of groups of individuals accomplishing a common task without leaders, with little communication, and on the basis of self-organizing principles, is an important research issue within the study of collective behavior of animals, humans, and robots. The article shows how distributed coordination allows a group of evolved, physically linked simulated robots (inspired by a robot under construction) to display a variety of highly coordinated basic behaviors such as collective motion, collective obstacle avoidance, and collective approach to light, and to integrate them in a coherent fashion. In this way the group is capable of searching and approaching a lighted target in an environment scattered with obstacles, furrows, and holes, where robots acting individually fail. The article shows how the emerged coordination of the group relies upon robust self-organizing principles (e.g., positive feedback) based on a novel sensor that allows the single robots to perceive the group's “average” motion direction. The article also presents a robust solution to a difficult coordination problem, which might also be encountered by some organisms, caused by the fact that the robots have to be capable of moving in any direction while being physically connected. Finally, the article shows how the evolved distributed coordination mechanisms scale very well with respect to the number of robots, the way in which robots are assembled, the structure of the environment, and several other aspects. <s> BIB004 </s> Swarm robotics: a review from the swarm engineering perspective <s> Collective transport <s> We examine the ability of a swarm robotic system to transport cooperatively objects of different shapes and sizes. We simulate a group of autonomous mobile robots that can physically connect to each other and to the transported object. Controllers – artificial neural networks – are synthesised by an evolutionary algorithm. They are trained to let the robots self-assemble, that is, organise into collective physical structures and transport the object towards a target location. We quantify the performance and the behaviour of the group. We show that the group can cope fairly well with objects of different geometries as well as with sudden changes in the target location. Moreover, we show that larger groups, which are made of up to 16 robots, make possible the transport of heavier objects. Finally, we discuss the limitations of the system in terms of task complexity, scalability and fault tolerance and point out potential directions for future research. <s> BIB005 </s> Swarm robotics: a review from the swarm engineering perspective <s> Collective transport <s> In this paper, we present a novel method for performing collective transport in the presence of obstacles. Three robots are physically connected to an object to be transported from a start to a goal location. The task is particularly challenging because the robots have a heterogeneous perception of the environment. In fact, the goal and the obstacles can be perceived only by some of the robots. Hence, the task requires appropriate negotiation of the direction among the robots. We developed a novel negotiation strategy in order to tackle this challenge.We perform experiments in simulation. In the experiments,we analyze efficiency in an environment with only one obstacle, and robustness in an environment with several obstacles. <s> BIB006 </s> Swarm robotics: a review from the swarm engineering perspective <s> Collective transport <s> Group food retrieval in some ant species serves as a useful paradigm for multirobot collective transport strategies that are decentralized, scalable, and do not require a priori information about the payload. We present a comprehensive overview of group retrieval in ants and investigate this phenomenon in Aphaenogaster cockerelli in order to extract the ants' roles during transport, the rules that govern their actions, and the individual forces that they apply to guide a food item to their nest. To measure these forces, we fabricated elastic structures with calibrated stiffness properties, induced ants to retrieve the structures, and tracked the resulting deformations with a camera. We then developed a hybrid system model of the ant behaviors that were observed in the experiments. We conducted simulations of the behavioral model that incorporate a quasi-static model of planar manipulation with compliant attachment points. Our simulations qualitatively replicate individual ant activity as well as certain macroscopic features of the transport. <s> BIB007
Description Collective transport, also known as group prey retrieval, is a collective behavior in which a group of robots has to cooperate in order to transport an object. In general, the object is heavy and cannot be moved by a single robot, making cooperation necessary. The robots need to agree on a common direction in order to effectively move the object towards a target. Source of inspiration Ants often carry prey cooperatively. BIB002 analyzed how cooperative transport is achieved in ant colonies. When ants find their target, they physically attach to it and then start to pull and push. If they do not perceive any movement after a while, they change the orientation of their body and try again. If even this does not work, they detach, re-attach at a different point and try again. BIB007 studied the same behavior observing how ants interact with fabricated elastic structures. Using the data retrieved from these observations, the authors developed a mathematical model of how collective transport is performed by ants. Approaches In swarm robotics, collective transport behaviors are obtained by using probabilistic finite state machines or artificial evolution. Cooperation is obtained either through explicit communication of the desired motion direction, or through indirect communication, that is, by measuring the force applied to the carried object by the other robots. Results BIB001 proposed three behaviors based respectively on: force sensing, position sensing and orientation sensing. This work was one of the first works aimed at studying collective transport without a centralized controller and with limited communication. BIB003 proposed a collective behavior in which the robots end up moving in a common direction at the beginning of the experiment by communicating their individual direction (see Fig. 11(a) ). In this way the robots are able to drag an object towards a goal area, even if this area is not perceived by all robots. BIB005 used artificial evolution to tune the parameters of a neural network to achieve collective transport. The obtained behavior was able to cope with different prey sizes and prey weights as well as with different numbers of robots (from 4 to 16). Various metrics and extensive simulations were used to validate the results. In their work, Groß and Dorigo were able to obtain three different transport strategies. In the first, the robot directly connects to the prey and move it. In the second, the robots connect to each other and then to the prey to move it. In the third and last strategy, the robots are not directly connected to the prey but form a circle around the prey and push it. BIB004 used artificial evolution and neural networks to perform collective transport (see Fig. 11(b) ). The obtained behavior exploits a sensor able to perceive the force applied by other robots on the chassis. With this sensor, the robots are able to perform collective obstacle avoidance while going towards a target area. BIB006 developed a collective transport behavior in which, through communication, a group of robots can agree on a common moving direction towards a goal by averaging the individual desired direction. The proposed solution is able to make robots move towards a common goal while avoiding obstacles. This work was developed for the Swarmanoid project ).