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Abstract — Tracking of miniature robotic platforms involves major challenges in image recognition and data association. We present our 3-year effort into developing the platform-independent, easy-to-use, and robust tracking software SwisTrack, which is tailored to research in swarm robotics and behavioral biology. We demonstrate the software and algorithms abilities using two case studies, tracking of a swarm of cockroaches, and a swarm-robotic inspection task, while outlining hard problems in tracking and data-association of marker-less objects. Tracking accuracy of a moving robot with respect to camera noise and the calibration model are calculated experimentally. Its open, platform-independent architecture, and easy-to-use interfaces (Matlab TM,Java TM, and C++), allowing for (distributed) post-processing of trajectory data online, make the software highly adaptive to particular research projects without changes to the source code. SwisTrack is publicly available on Sourceforge.net under the OSI Adaptive License and contributions from the robotics and biology community are encouraged. I.

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In certain multi-robot systems, the physical limitations of the individual robots can be overcome using self-assembly—the autonomous creation of physical connections between individual robots to form a larger composite robotic entity. However, existing robotic systems capable of self-assembly have little or no control over the morphology of the self-assembled entities. This restricts the adaptability of such systems, since robots can carry out certain tasks more efficiently if their morphology is specialized to the task. In this paper, we extend the distributed mechanism presented in (Christensen et al. in IEEE Robot. Autom. Mag. 14(4):18–25, 2007) that allows autonomous mobile robots to self-assemble into specific morphologies. We present a simple language, SWARMORPH-script, that allows for concise descriptions of the rules that govern the distributed morphology growth process. Local visual communication allows physically connected robots to send and receive strings. A string can be a rule identifier that triggers execution of predefined logic for extending a morphology. Alternatively, whole scripts can be communicated and subsequently executed on the receiving robot. On real self-propelled robots capable of self-assembly, we demonstrate how specific morphologies can be constructed, how the size of a morphology can be regulated, and how multiple morphologies can be assembled. We also show how the transmission of entire scripts gives the robots the capacity to participate in the formation of morphologies of which they had no a priori knowledge.

In this paper, we study a new approach to fault detection for autonomous robots. Our hypothesis is that hardware faults change the flow of sensory data and the actions performed by the control program. By detecting these changes, the presence of faults can be inferred. In order to test our hypothesis, we collect data from three different tasks performed by real robots. During a number of training runs, we record sensory data from the robots while they are operating normally and after a fault has been injected. We use back-propagation neural networks to synthesize fault detection components based on the data collected in the training runs. We evaluate the performance of the trained fault detectors in terms of number of false positives and time it takes to detect a fault. The results show that good fault detectors can be obtained. We extend the set of possible faults and go on to show that a single fault detector can be trained to detect several faults in both a robot’s sensors and actuators. We show that fault detectors can be synthesized that are robust to variations in the task, and we show how a fault detector can be trained to allow one robot to detect faults that occur in another robot.

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Abstract. Communication is a point of central importance in swarms of robots. This paper describes a set of simulations in which artificial evolution is used as a means to engineer robot neuro-controllers capable of guiding groups of robots in a categorisation task by producing appropriate actions. Communicative behaviour emerges, notwithstanding the absence of explicit selective pressure (coded into the fitness function) to favour signalling over non-signalling groups. Post-evaluation analyses illustrate the adaptive function of the evolved signals and show that they are tightly linked to the behavioural repertoire of the agents. Finally, our approach for developing controllers is validated by successfully porting one evolved controller on real robots. 1

Abstract This article describes a simulation model in which artificial evolution is used to design homogeneous control structures and adaptive communication protocols for a group of three autonomous simulated robots. The agents are required to cooperate in order to approach a light source while avoiding collisions. The robots are morphologically different: Two of them are equipped with infrared sensors, one with light sensors. Thus, the two morphologically identical robots should take care of obstacle avoidance; the other one should take care of phototaxis. Since all of the agents can emit and perceive sound, the group’s coordination of actions is based on acoustic communication. The results of this study are a proof of concept: They show that dynamic artificial neural networks can be successfully synthesized by artificial evolution to design the neural mechanisms required to underpin the behavioral strategies and adaptive communication capabilities demanded by this task. Postevaluation analyses unveil operational aspects of the best evolved behavior. Our results suggest that the building blocks and the evolutionary machinery detailed in the article should be considered in future research work dealing with the design of homogeneous controllers for groups of heterogeneous cooperating and communicating robots. Keywords Collective robotics, evolutionary robotics, dynamic neural networks, social behavior, coordinated motion, signaling 1

Abstract In certain multi-robot systems, the physical limitations of the individual robots can be overcome using self-assembly—the autonomous creation of physical connections between individual robots to form a larger composite robotic entity. However, existing robotic systems capable of self-assembly have little or no control over the morphology of the self-assembled entities. This restricts the adaptability of such systems, since robots can carry out certain tasks more efficiently if their morphology is specialized to the task. In this paper, we extend the distributed mechanism presented in (Christensen et al. in IEEE Robot. Autom. Mag. 14(4):18–25, 2007) that allows autonomous mobile robots to self-assemble into specific morphologies. We present a simple language, SWARMORPH-script, that allows for concise descriptions of the rules that govern the distributed morphology growth process. Local visual communication allows physically connected robots to send and receive strings. A string can be a rule identifier that triggers execution of predefined logic for extending a morphology. Alternatively, whole scripts can be communicated and subsequently executed on the receiving robot. On real self-propelled robots capable of self-assembly, we demonstrate how specific morphologies can be constructed, how the size of a morphology can be regulated, and how multiple morphologies can be assembled. We also show how the transmission of entire scripts gives the robots the capacity to participate in the formation of morphologies of which they had no a priori knowledge. Electronic supplementary material The online version of this article

This paper analyses the strengths and weaknesses of self-organising approaches, such as evolutionary robotics, and direct design approaches, such as behaviour-based controllers, for the production of autonomous robots ’ controllers, and shows how the two approaches can be usefully combined. In particular, the paper proposes a method for encoding evolved neural-network based behaviours into motor schemabased controllers and then shows how these controllers can be modified and combined to produce robots capable of solving new tasks. The method has been validated in the context of a collective robotics scenario in which a group of physically assembled simulated autonomous robots are requested to produce different forms of coordinated behaviours (e.g., coordinated motion, walled-arena exiting, and light pursuing).