In collective systems, the available agents are a limited resource that must be allocated among tasks to maximize collective performance. Computing the optimal allocation of several agents to numerous tasks through a brute-force approach can be infeasible, especially when each task’s performance scales differently with the increase of agents. For example, difficult tasks may require more agents to achieve similar performances compared to simpler tasks, but performance may saturate nonlinearly as the number of allocated agents increases. We propose a computationally efficient algorithm, based on marginal performance gains, for optimally allocating agents to tasks with concave scalability functions—including linear, saturating, and retrograde scaling—to achieve maximum collective performance. We test the algorithm by allocating a simulated robot swarm among collective decision-making tasks, where embodied agents sample their environment and exchange information to reach a consensus on spatially distributed environmental features. We vary task difficulties by different geometrical arrangements of environmental features in space (patchiness). In this scenario, decision performance in each task scales either as a saturating curve (following the Condorcet’s Jury Theorem (CJT) in an interference-free setup) or as a retrograde curve (when physical interference among robots restricts their movement). Using simple robot simulations, we show that our algorithm can be useful in allocating robots among tasks. Our approach aims to advance the deployment of future real-world multirobot systems.

With increasing numbers of mobile robots arriving in real-world applications, more robots coexist in the same space, interact, and possibly collaborate. Methods to provide such systems with system size scalability are known, for example, from swarm robotics. Example strategies are self-organizing behavior, a strict decentralized approach, and limiting the robot-robot communication. Despite applying such strategies, any multi-robot system breaks above a certain critical system size (i.e., number of robots) as too many robots share a resource (e.g., space, communication channel). We provide additional evidence based on simulations, that at these critical system sizes, the system performance separates into two phases: nearly optimal and minimal performance. We speculate that in real-world applications that are configured for optimal system size, the supposedly high-performing system may actually live on borrowed time as it is on a transient to breakdown. We provide two modeling options (based on queueing theory and a population model) that may help to support this reasoning.

In human-robot interaction (HRI), we study how humans interact with robots, but also the effects of robot behavior on human perception and well-being. Especially, the influence on humans by tandem robots with one human-controlled and one autonomous robot or even semi-autonomous multi-robot systems is not yet fully understood. Here, we focus on a leader-follower scenario and study how emotionally expressive motion patterns of a small, mobile follower robot affect the perception of a human operator controlling the leading robot. We examined three distinct emotional behaviors for the follower compared to a neutral condition: angry, happy, and sad. We asked participants to maneuver the leader robot along a set path while experiencing each follower behavior in a randomized order. We identified a significant shift in subjective attention toward the follower with emotionally expressive behaviors compared to the neutral condition. For example, the angry behavior significantly heightened participant stress levels and was considered the least preferred behavior. The happy behavior was the most preferred and associated with increased excitement by the participants. Integrating the proposed behaviors in robots can profoundly influence the human operator’s perceived attention, emotional state, and overall experience. These insights are valuable for future HRI tandem robot designs.

In robotics, understanding human interaction with autonomous systems is crucial for enhancing collaborative technologies. We focus on human-swarm interaction (HSI), exploring how active robot groups of varying sizes affect operators’ cognitive and perceptual reactions over distinct durations. We analyze the impact of different numbers of active robots within a 15-robot swarm on operators’ time perception, emotional state, flow experience, and task difficulty perception. Our findings indicate that managing multiple active robots when compared to one active robot significantly alters time perception and flow experience, leading to a faster passage of time and increased flow. More active robots and extended durations cause increased emotional arousal and perceived task difficulty, highlighting the interaction between the number of active robots and human cognitive processes. These insights inform the creation of intuitive human-swarm interfaces and aid in developing swarm robotic systems aligned with human cognitive structures, enhancing human-robot collaboration.

In high-pressure environments where human individuals must simultaneously monitor multiple entities, communicate effectively, and maintain intense focus, the perception of time becomes a critical factor influencing performance and well-being. One indicator of well-being can be the person's subjective time perception. In our project ChronoPilot, we aim to develop a device that modulates human subjective time perception. In this study, we present a method to automatically assess the subjective time perception of air traffic controllers, a group often faced with demanding conditions, using their physiological data and eleven state-of-the-art machine learning classifiers. The physiological data consist of photoplethysmo-gram, electrodermal activity, and temperature data. We find that the support vector classifier works best with an accuracy of 79 % and electrodermal activity provides the most descriptive biomarker. These findings are an important step towards closing the feedback loop of our ChronoPilot-device to automatically modulate the user's subjective time perception. This technological advancement may promise improvements in task management, stress reduction, and overall productivity in high-stakes professions.

Autonomous robot swarms must be able to make fast and accurate collective decisions, but speed and accuracy are known to be conflicting goals. While collective decision-making is widely studied in swarm robotics research, only few works on using methods of evolutionary computation to generate collective decision-making mechanisms exist. These works use task-specific fitness functions rewarding the accomplishment of the respective collective decision-making task. But task-independent rewards, such as for prediction error minimization, may promote the emer-gence of diverse and innovative solutions. We evolve collective decision-making mechanisms using a task-specific fitness function rewarding correct robot opinions, a task-independent reward for prediction accuracy, and a hybrid fitness function combining the two previous. In our simulations, we use the collective perception scenario, that is, robots must collectively determine which of two environmental features is more frequent. We show that evolution successfully optimizes fitness in all three scenarios, but that only the task-specific fitness function and the hybrid fitness function lead to the emergence of collective decision-making behaviors. In benchmark experiments, we show the competitiveness of the evolved decision-making mechanisms to the voter model and the majority rule and analyze the scalability of the decision-making mechanisms with problem difficulty.

Many large-scale multi-robot systems require human input during operation in different applications. To still minimize the human effort, interaction is intermittent or restricted to a subset of robots. Despite this reduced demand for human interaction, the mental load and stress can be challenging for the human operator. A specific effect of human-swarm interaction may be a hypothesized change of subjective time perception in the human operator. In a series of simple human-swarm interaction experiments with robot swarms of up to 15 physical robots, we study whether human operators have altered time perception due to the number of controlled robots or robot speeds. Using data gathered by questionnaires, we found that increased swarm size shrinks perceived time and decreased robot speeds expand the perceived time. We introduce the concept of subjective time perception to human-swarm interaction. Future research will enable swarm systems to autonomously modulate subjective timing to ease the job of human operators.

Collective decision-making is an essential capability of large-scale multi-robot systems to establish autonomy on the swarm level. A large portion of literature on collective decision-making in swarm robotics focuses on discrete decisions selecting from a limited number of options. Here we assign a decentralized robot system with the task of exploring an unbounded environment, finding consensus on the mean of a measurable environmental feature, and aggregating at areas where that value is measured (e.g., a contour line). A unique quality of this task is a causal loop between the robots' dynamic network topology and their decision-making. For example, the network's mean node degree influences time to convergence while the currently agreed-on mean value influences the swarm's aggregation location, hence, also the network structure as well as the precision error. We propose a control algorithm and study it in real-world robot swarm experiments in different environments. We show that our approach is effective and achieves higher precision than a control experiment. We anticipate applications, for example, in containing pollution with surface vehicles.

Solving complex problems collectively with simple entities is a challenging task for swarm robotics. For the task of collective decision-making, robots decide based on local observations on the microscopic level to achieve consensus on the macroscopic level. We study this problem for a common benchmark of classifying distributed features in a binary dynamic environment. Our special focus is on environmental features that are dynamic as they change during the experiment. We present a control algorithm that uses sophisticated statistical change detection in combination with Bayesian robots to classify dynamic environments. The main profit is to reduce false positives allowing for improved speed and accuracy in decision-making. Supported by results from various simulated experiments, we introduce three feedback loops to balance speed and accuracy. In our benchmarks, we show the superiority of our new approach over previous works on Bayesian robots. Our approach of using change detection shows a more reliable detection of environmental changes. This enables the swarm to successfully classify even difficult environments (i.e., hard to detect differences between the binary features), while achieving faster and more accurate results in simpler environments.

Developing reusable software for mobile robots is still challenging. Even more so for swarm robots, despite the desired simplicity of the robot controllers. Prototyping and experimenting are difficult due to the multi-robot setting and often require robot-robot communication. Also, the diversity of swarm robot hardware platforms increases the need for hardware-independent software concepts. The main advantages of the commonly used robot software architecture ROS 2 are modularity and platform independence. We propose a new ROS 2 package, ROS2sWARM, for applications of swarm robotics that provides a library of ready-to-use swarm behavioral primitives. We show the successful application of our approach on three different platforms, the TurtleBot3 Burger, the TurtleBot3 Waffle Pi, and the Jackal UGV, and with a set of different behavioral primitives, such as aggregation, dispersion, and collective decision-making. The proposed approach is easy to maintain, extendable, and has good potential for simplifying swarm robotics experiments in future applications.

Collective decision-making enables self-organizing robot swarms to act autonomously on a swarm level and is essential to coordinate their actions as a whole. When robots only share and communicate information locally a distributed and decentralized approach is required. In a previous paper [4], an efficient method based on a distributed Bayesian algorithm was created to distinguish a binary environment. We extended it to have the capability of dealing with dynamic environments. Therefore, it must avoid global lock-in states. In many realistic applications the robot swarm needs to adapt to (collectively) measurable changes at runtime by revising previous collective decisions. The trade-off between decision-making speed and readiness to revise previous decisions is a seemingly unavoidable challenge. We present our extension of the former approach and study how this trade-off can efficiently be balanced.

Efficient engineered systems require scalability. A scalable system has increasing performance with increasing system size. In an ideal situation, the increase in performance (e.g., speedup) corresponds to the number of units (e.g., processors, robots, users) that are added to the system (e.g., three times the number of processors in a computer would lead to three times faster computations). However, if multiple units work on the same task, then coordination among these units is required. This coordination can introduce overheads with an impact on system performance. The coordination costs can lead to sublinear improvement or even diminishing performance with increasing system size. However, there are also systems that implement efficient coordination and exploit collaboration of units to attain superlinear improvement. Modeling the scalability dynamics is key to understanding and engineering efficient systems. Known laws of scalability, such as Amdahl’s law, Gustafson’s law, and Gunther’s Universal Scalability Law, are minimalistic phenomenological models that explain a rich variety of system behaviors through concise equations. While useful to gain general insights, the phenomenological nature of these models may limit the understanding of the underlying dynamics, as they are detached from first principles that could explain coordination overheads or synergies among units. Through a decentralized system approach, we propose a general model based on generic interactions between units that is able to describe, as specific cases, any general pattern of scalability included by previously reported laws. The proposed general model of scalability has the advantage of being built on first principles, or at least on a microscopic description of interaction between units, and therefore has the potential to contribute to a better understanding of system behavior and scalability. We show that this generic model can be applied to a diverse set of systems, such as parallel supercomputers, robot swarms, or wireless sensor networks, therefore creating a unified view on interdisciplinary design for scalability.

Applications of large-scale mobile multirobot systems can be beneficial over monolithic robots because of higher potential for robustness and scalability. Developing controllers for multirobot systems is challenging because the multitude of interactions is hard to anticipate and difficult to model. Automatic design using machine learning or evolutionary robotics seem to be options to avoid that challenge, but bring the challenge of designing reward or fitness functions. Generic reward and fitness functions seem unlikely to exist and task-specific rewards often have undesired side effects. Approaches of so-called innate motivation try to avoid the specific formulation of rewards and work instead with different drivers, such as curiosity. Our approach to innate motivation is to minimize surprise, which we implement by maximizing the accuracy of the swarm robot’s sensor predictions using neuroevolution. A unique advantage of the swarm robot case is that swarm members populate the robot’s environment and can trigger more active behaviors in a self-referential loop. In this article, we summarize our previous simulation-based results concerning behavioral diversity, robustness, scalability, and engineered self-organization, and put them into context. In several new studies, we analyze the influence of the optimizer’s hyperparameters, the scalability of evolved behaviors, and the impact of realistic robot simulations. Finally, we present results using real robots that show how the reality gap can be bridged.

We propose a 2D path planning algorithm in a non-convex workspace defined as a sequence of connected convex polytopes. The reference path is parameterized as a B-spline curve, which is guaranteed to entirely remain within the workspace by exploiting the local convexity property and by formulating linear constraints on the control points of the B-spline. The novelties of the paper lie in the use of the equivalent Bézier representation of the B-spline curve, which significantly reduces the conservatism in the local convexity bound and in the integration of these constraints into a convex quadratic optimization problem, which minimizes the curve length. The algorithm is successfully validated in both simulations and experiments, by providing obstacle-free reference paths on real occupancy grid maps obtained from the laser scan data of a mobile robot platform.

In software engineering, the imprecise requirements of a user are transformed to a formal requirements specification during the requirements elicitation process. This process is usually guided by requirements engineers interviewing the user. We want to partially automate this first step of the software engineering process in order to enable users to specify a desired software system on their own. With our approach, users are only asked to provide exemplary behavioral descriptions. The problem of synthesizing a requirements specification from examples can partially be reduced to the problem of grammatical inference, to which we apply an active coevolutionary learning approach. However, this approach would usually require many feedback queries to be sent to the user. In this work, we extend and generalize our active learning approach to receive knowledge from multiple oracles, also known as proactive learning. The ``user oracle'' represents input received from the user and the "knowledge oracle" represents available, formalized domain knowledge. We call our two-oracle approach the "first apply knowledge then query" (FAKT/Q) algorithm. We compare FAKT/Q to the active learning approach and provide an extensive benchmark evaluation. As result we find that the number of required user queries is reduced and the inference process is sped up significantly. Finally, with so-called On-The-Fly Markets, we present a motivation and an application of our approach where such knowledge is available.

Using multi-robot systems for autonomous construction allows for parallelization and scalability. Swarm construction furthermore exploits robot interactions and collaboration, such that the robot swarm collectively constructs artifacts beyond what a single comparable robot could achieve. Here we present an alternative concept of swarm construction that is distinct because it uses continuous building material. Our approach is unique in its use of braiding techniques for construction. We deploy fibres that potentially allow for structures that are not possible with building blocks. To achieve maximal scalability we restrict ourselves to a decentralized approach. The main challenges are the local coordination of the robot teams, self-organized task allocation, and the dynamic reconfiguration of the braiding scheme at runtime. We successfully validate our approach in multi-robot experiments that show both braiding and branching of the braid. In addition, we show options for implementing an open system-that is robots can join and leave the braiding process on the fly.

Swarm robots operate as autonomous agents and a swarm as a whole gets autonomous by its capability of collective decision-making. Despite intensive research on models of collective decision-making, the implementation in multi-robot systems is still challenging. Here, we advance the state of the art by introducing more plasticity to the decision-making process and by increasing the scenario difficulty. Most studies on large-scale multi-robot decision-making are limited to one instance of an iterated exploration-dissemination phase followed by successful and permanent convergence. We investigate a dynamic environment that requires constant collective monitoring of option qualities. Once a significant change in qualities is detected by the swarm, it has to collectively reconsider its previous decision accordingly. This is only possible by preventing lock-ins, a global consensus state of no return (i.e., a dominant majority of robots prevents the swarm from switching to another, possibly better option). In addition, we introduce a scenario of increased difficulty as the robots must locate themselves to assess the quality of an option. Using local communication, swarm robots propagate hop-count information throughout the swarm to form a global reference frame. We successfully validate our implementation in many swarm robot experiments concerning robustness to disruptions of the reference frame, scalability, and adaptivity to a dynamic environment.

Bio-hybrid systems-close couplings of natural organisms with technology-are high potential and still underexplored. In existing work, robots have mostly influenced group behaviors of animals. We explore the possibilities of mixing robots with natural plants, merging useful attributes. Significant synergies arise by combining the plants' ability to efficiently produce shaped material and the robots' ability to extend sensing and decision-making behaviors. However, programming robots to control plant motion and shape requires good knowledge of complex plant behaviors. Therefore, we use machine learning to create a holistic plant model and evolve robot controllers. As a benchmark task we choose obstacle avoidance. We use computer vision to construct a model of plant stem stiffening and motion dynamics by training an LSTM network. The LSTM network acts as a forward model predicting change in the plant, driving the evolution of neural network robot controllers. The evolved controllers augment the plants' natural light-finding and tissue-stiffening behaviors to avoid obstacles and grow desired shapes. We successfully verify the robot controllers and bio-hybrid behavior in reality, with a physical setup and actual plants.

Self-assembly is the aggregation of simple parts into complex patterns as frequently observed in nature. Following this inspiration, creating programmable systems of self-assembly that achieve similar complexity and robustness with robots is challenging. As a role model we pick the growth of natural plants that adapts to environmental conditions and is robust enough to withstand disturbances such as changes due to dynamic environments and cut parts. We program a robot swarm to self-assemble into tree-like shapes and to adapt efficiently to the environment. Our approach is inspired by the vascular morphogenesis of plants, the patterned formation of vascular tissue to transport fluids and nutrients internally. The aggregated robots establish an internal network of resource sharing, allowing them to make rational decisions collectively about where to add and where to remove robots. As a result, the growth is adaptive to an environmental feature (here, light) and robust to changes in a dynamic environment. The robot swarm is able to self-repair by regrowing lost parts. We successfully validate and benchmark our approach in a number of robot swarm experiments showing adaptivity, robustness, and self-repair.

Within software engineering, requirements engineering starts from imprecise and vague user requirements descriptions and infers precise, formalized specifications. Techniques, such as interviewing by requirements engineers, are typically applied to identify the user's needs. We want to partially automate even this first step of requirements elicitation by methods of evolutionary computation. The idea is to enable users to specify their desired software by listing examples of behavioral descriptions. Users initially specify two lists of operation sequences, one with desired behaviors and one with forbidden behaviors. Then, we search for the appropriate formal software specification in the form of a deterministic finite automaton. We solve this problem known as grammatical inference with an active coevolutionary approach following Bongard and Lipson [2]. The coevolutionary process alternates between two phases: (A) additional training data is actively proposed by an evolutionary process and the user is interactively asked to label it; (B) appropriate automata are then evolved to solve this extended grammatical inference problem. Our approach leverages multi-objective evolution in both phases and outperforms the state-of-the-art technique [2] for input alphabet sizes of three and more, which are relevant to our problem domain of requirements specification.

Swarm-based braiding of structures represents a novel research direction in the domain of building architecture. The idea is that autonomous agents, for instance robots that unroll threads or plants that grow, are programmed or influenced to braid. It is an aspect of biohybrid systems where organisms and robots join forces. In order to harness this idea, we have developed a swarm-based model that allows architects to explore the resulting design spaces in virtual reality. In this paper, we present (1) the model of our swarm-based simulation that aims at growing braided structures, (2) the design elements to guide the otherwise self-organising virtual agents, and (3) the user interface that allows the user to configure, place and grow the swarms of braiding agents. We also present results of a first user study with students and faculty from architecture, in which we tried to capture the usability of our first prototype based on a survey and an analysis of the built results.

Mixing societies of natural and artificial systems can provide interesting and potentially fruitful research targets. Here we mix robotic setups and natural plants in order to steer the motion behavior of plants while growing. The robotic setup uses a camera to observe the plant and uses a pair of light sources to trigger phototropic response, steering the plant to user-defined targets. An evolutionary robotic approach is used to design a controller for the setup. Initially, preliminary experiments are performed with a simple predetermined controller and a growing bean plant. The plant behavior in response to the simple controller is captured by image processing, and a model of the plant tip dynamics is developed. The model is used in simulation to evolve a robot controller that steers the plant tip such that it follows a number of randomly generated target points. Finally, we test the simulation-evolved controller in the real setup controlling a natural bean plant. The results demonstrate a successful crossing of the reality gap in the setup. The success of the approach allows for future extensions to more complex tasks including control of the shape of plants and pattern formation in multiple plant setups.

Open-ended evolution is still an unachieved goal in evolutionary computation. Evolution guided by objective functions can easily be trapped on local optima. Our approach is inspired by evolutionary paths along stepping stones, as observed in user behaviors of Picbreeder. We propose a general framework, inspiration-triggered search, which tries to roughly mimic the creative design process of a human being. Instead of using a fixed objective function, the search algorithm itself is free to switch between objectives within certain constraints but inspired by features of the currently evolved artifacts. The overall optimization task is to generate complex artifacts that cannot be generated by a direct optimization approach. In contrast to other approaches that make extensive use of external knowledge (e.g., Innovation Engines), we try to approach the ambitious goal of virtually bootstrapping a creative process from scratch. The proposed method is tested in the domain of images, that is to find complex and aesthetically pleasant images, and is compared to novelty search.

Autonomous self-assembly allows to create structures and scaffolds on demand and automatically. The desired structure may be predetermined or alternatively it is the result of an artificial growth process that adapts to environmental features and to the intermediate structure itself. In a self-organizing and decentralized control approach the robots interact only locally and form the structure collectively. Designing a complete approach that allows the robot group to collectively decide on where to start the self-assembly, that adapts at runtime to environmental conditions, and that guarantees the structural stability, is challenging and does not yet exist. We present an approach to self-assembly inspired by diffusion-limited aggregation that generates an adaptive structure reacting to environmental conditions in an artificial growth process. During a preparatory stage the robots collectively decide where to start the self-assembly also depending on environmental conditions. In the actual self-assembly stage, the robots create tree-like structures that grow towards light. We report the results of robot self-assembly experiments with 50 Kilobots. Our results demonstrate how an adaptive growth process can be implemented in robots. We briefly describe our future work of how to extend the approach to a 3-d growth process and how robot self-assembly as an open-ended adaptive growth process opens up a multiplicity of future opportunities.

Robot Gardens are an augmented reality concept allowing a human user to design a biohybrid, plant-robot system. Plants growing from deliberately placed seeds are directed by robotic units that the user can position, configure and activate. For example, the robotic units may serve as physical shields or frames but they may also guide the plants' growth through emission of light. The biohybrid system evolves over time to redefine architectural spaces. This gives rise to the particular challenge of designing a biohybrid system before its actual implementation and potentially long before its developmental processes unfold. Here, an augmented reality interface featuring according simulation models of plants and robotic units allows one to explore the design space a priori. In this work, we present our first functional augmented reality prototype to design biohybrid systems. We provide details about its workings and elaborate on first empirical studies on its usability.

We study a self-organized collective decision-making strategy to solve a site-selection problem using a swarm of simple robots. Robots can only move forward or turn in place; sense the intensity of the ambient light; and exchange 3-byte messages with peers in a limited range. The goal of the swarm is to collectively decide which of the sites available in the environment is the best candidate site. We define a distributed and iterative decision-making strategy: robots explore the available options, determine the options' qualities, decide autonomously which option to take, and communicate their decision to neighboring robots. We study the effectiveness and robustness of the proposed strategy using a swarm of 100 Kilobots and we focus on the impact of the neighborhood size over the dynamics of the system.

Fitness function design is known to be a critical feature of the evolutionary-robotics approach. Potentially, the complexity of evolving a successful controller for a given task can be reduced by integrating a priori knowledge into the fitness function which complicates the comparability of studies in evolutionary robotics. Still, there are only few publications that study the actual effects of different fitness functions on the robot's performance. In this paper, we follow the fitness function classification of Nelson et al. (2009) and investigate a selection of four classes of fitness functions that require different degrees of a priori knowledge. The robot controllers are evolved in simulation using NEAT and we investigate different tasks including obstacle avoidance and (periodic) goal homing. The best evolved controllers were then post-evaluated by examining their potential for adaptation, determining their convergence rates, and using cross-comparisons based on the different fitness function classes. The results confirm that the integration of more a priori knowledge can simplify a task and show that more attention should be paid to fitness function classes when comparing different studies.

Recent approaches in evolutionary robotics (ER) propose to generate behavioral diversity in order to evolve desired behaviors more easily. These approaches require the definition of a behavioral distance, which often includes task-specific features and hence a priori knowledge. Alternative methods, which do not explicitly force selective pressure towards diversity (SPTD) but still generate it, are known from the field of artificial life, such as in artificial ecologies (AEs). In this study, we investigate how SPTD is generated without task-specific behavioral features or other forms of a priori knowledge and detect how methods of generating SPTD can be transferred from the domain of AE to ER. A promising finding is that in both types of systems, in systems from ER that generate behavioral diversity and also in the investigated speciation model, selective pressure is generated towards unpopulated regions of search space. In a simple case study we investigate the practical implications of these findings and point to options for transferring the idea of self-organizing SPTD in AEs to the domain of ER.

Similarly to evolving controllers for single robots also controllers for groups of robots can be generated by applying evolutionary algorithms. Usually a fitness function rewards desired behavioral features. Here we investigate an alternative method that generates collective behaviors al-most only as a by-product. We roughly follow the idea of Helmholtz that perception is a process based on probabilistic inference and evolve an in-ternal model that is supposed to predict the agent’s future perceptions. Separated from this prediction model the agent also evolves a regular controller. Direct selective pressure, however, is only effective on the pre-diction model by minimizing prediction error (surprise). Our results show that a number of basic collective behaviors emerge by this approach, such as dispersion, aggregation, and flocking. The probability that a certain behavior emerges and also the difficulty of making correct predictions de-pends on the swarm density. The reported method has potential to be another simple approach to open-ended evolution analogical to the search for novelty. 1

A grand challenge in the field of artificial life is to find a general theory of emergent self-organizing systems. In swarm systems most of the observed complexity is based on motion of simple entities. Similarly, statistical mechanics focuses on collective properties induced by the motion of many interacting particles. In this article we apply methods from statistical mechanics to swarm systems. We try to explain the emergent behavior of a simulated swarm by applying methods based on the fluctuation theorem. Empirical results indicate that swarms are able to produce negative entropy within an isolated subsystem due to frozen accidents. Individuals of a swarm are able to locally detect fluctuations of the global entropy measure and store them, if they are negative entropy productions. By accumulating these stored fluctuations over time the swarm as a whole is producing negative entropy and the system ends up in an ordered state. We claim that this indicates the existence of an inverted fluctuation theorem for emergent self-organizing dissipative systems. This approach bears the potential of general applicability.

This paper investigates the properties required to evolve Artificial Neural Networks for distributed control in modular robotics, which typically involves non-linear dynamics and complex interactions in the sensori-motor space. We investigate the relation between macro-scale properties (such as modularity and regularity) and micro-scale properties in Neural Network controllers. We show how neurons capable of multiplicative-like arithmetic operations may increase the performance of controllers in several ways whenever challenging control problems with non-linear dynamics are involved. This paper provides evidence that performance and robustness of evolved controllers can be improved by a combination of carefully chosen micro- and macro-scale neural network properties.

One of the main challenges in automatic controller synthesis is to develop methods that can successfully be applied for complex tasks. The difficulty is increased even more in the case of settings with multiple interacting agents. We apply the artificial homeostatic hormone system (AHHS) approach, which is inspired by the signaling network of unicellular organisms, to control a system of several independently acting agents decentrally. The approach is designed for evaluation-minimal, artificial evolution in order to be applicable to complex modular robotics scenarios. The performance of AHHS controllers is compared with neuroevolution of augmenting topologies (NEAT) in the coupled inverted pendulums benchmark. AHHS controllers are found to be better for multimodular settings. We analyze the evolved controllers with regard to the usage of sensory inputs and the emerging oscillations, and we give a nonlinear dynamics interpretation. The generalization of evolved controllers to initial conditions far from the original conditions is investigated and found to be good. Similarly, the performance of controllers scales well even with module numbers different from the original domain the controller was evolved for. Two reference implementations of a similar controller approach are reported and shown to have shortcomings. We discuss the related work and conclude by summarizing the main contributions of our work.

The challenging scientific field of self-reconfiguring modular robotics (i.e., decentrally controlled 'super-robots' based on autonomous, interacting robot modules with variable morphologies) calls for novel paradigms of designing robot controllers. One option is the approach of evolutionary robotics. In this approach, the challenge is to achieve high evaluation numbers with the available resources which may even affect the feasibility of this approach. Simulations are usually applied at least in a preliminary stage of research to support controller design. However, even simulations are computationally expensive which gets even more burdensome once comprehensive studies and comparisons between different controller designs and approaches have to be done. Hence, a benchmark with low computational cost is needed that still contains the typical challenges of decentral control, is comparable, and easily manageable. We propose such a benchmark and report an empirical study of its characteristics including the transition from the single-robot setting to the multi-robot setting, typical local optima, and properties of adaptive walks through the fitness landscape.

The current definitions of emergence have no effects in the context of artificial life that could convincingly be called `constructive'. They are rather descriptive labels or tests. In order to get towards recipes of generating emergence we need to know systemic characteristics that help during the design phase of artificial life systems and worlds. In this paper, we develop and discuss five hypotheses that are not meant to be irrevocable but rather thought-provoking. We introduce two modeling approaches for Langton's ant to clarify these hypotheses. Then we discuss general properties of systems, such as (ir-)reversibility, dependence on initial states, computation, discreetness, and undecidable properties of system states.

For any embodied, mobile, autonomous agent it is essential to control its actuators appropriately for the faced task. This holds for natural organisms as well as for robots. If several such agents have to cooperate, the coordination of actions becomes important. We present an artificial homeostatic hormone system which is a bio-inspired control paradigm. It allows to control both, a single robot as well a set of cooperating modules in multi-modular reconfigurable robotics. Our approach is inspired by chemical signal-processing and hormone control in animals. Evolutionary computation is used to adapt controllers for two distinct morphological robot configurations (uni-and multi-modular), different environmental conditions, and tasks. This approach is compared to artificial neural networks. Our results indicate, that the proposed control paradigm is well adaptable to different robot morphologies and to different environmental situations. It is able to generate behaviors for several robotic tasks and outperforms neural networks in terms of evolvability in the tested multi-modular robotic setting tested.

One of the prominent challenges in mobile robotics is to develop control methodologies that allow the adaptation to dynamic and unforeseen environments. The classic approach of hand-coded controllers is very efficient for well-defined tasks and specific environments but poor in adapting to changing environmental conditions. One alternative approach is the application of evolutionary algorithms which need, in turn, easily evolvable representations of controllers. In this paper, we investigate one promising approach of an artificial hormone system as a control paradigm which is believed to be easily optimized by evolutionary processes. In a first step of this research, we focus on the simple task of collision avoidance. We present a brief mathematical analysis of this controller approach and an implementation of the controller on a mobile robot to check the feasibility in principle of our approach. The task is successfully accomplished and we conclude with a discussion of the hormone dynamics in the robot.

We present a comparative study of two spatially resolved macroscopic models of an autonomous robotic swarm. In previous experiments, the collective behavior of 15 autonomous swarm robots, driven by a simple bio-inspired control algorithm, was investigated: in two different environmental conditions, the ability of the robots to aggregate below a light source was tested. Distinct approaches to predict the dynamics of the spatial distribution were made by two different modeling approaches: one model was constructed in a compartmental manner (ODEs). In parallel, a space-continuous model (PDEs) was constructed. Both models show a high degree of similarity concerning the modeling of concrete environmental factors (light), but due to their different basic approaches, show also significant differences in their implementation. However, the predictions of both models compare well to the observed behavior of the robotic swarm, thus both models can be used to develop further extensions of the algorithm as well as different experimental setups without the need to run extensive real robotic preliminary experiments.

Two control algorithms for a swarm robot are presented that enable it to orientate itself by using information from the geometry of trail bifurcations within a trail network. The development of these algorithms was inspired by the behavior of Pharaoh's ants as reported by Jackson et al., 2004. The performance of the robot is analyzed in a large number of embodied experiments with different bifurcation angles. The reactive behavior implemented by simple rules is sufficient to accomplish this task using a robot of limited capabilities. The frequency of correct reorientations is maximized when the trail bifurcation angle is 60 degrees, as found in natural networks