In large-scale systems composed of autonomous embodied agents (e.g., robots), unpredictability of... more In large-scale systems composed of autonomous embodied agents (e.g., robots), unpredictability of events, sensor noise and actuator imperfection pose significant challanges to the designers of control software. If such systems tend to selforganize, emergent phenomena prevent classical engineering approaches per se. In recent years, the Artificial Life Lab at the University of Graz has investigated a variety of methods to synthesize such control algorithms used in multi-modular robotics and in swarm robotics. These methods either translate mechanisms directly from biology to the engineering domain (bio-mimicry, bio-inspiration) or generates such controllers through artificial evolution from scratch. In this article I first discuss distributed control algorithms, which determine the collective behavior of autonomous robotic swarms. These algorithms are derived from collective behavior of honeybees and from slime mold aggregation. One of these algorithms is inspired by inter-adult food exchange in honeybees (’trophallaxis’) another one from chemical signaling in slime molds. In addition to the control of robot swarms, control paradigms for multi-modular robotic organisms are presented, which are again based on simulated fluid exchange (hormones) among compartments of robotic organisms. In both domains -swarms and organisms- the control system is self-organized and consists of many homeostatic sub-systems which adapt to each other on the individual (module) and on the collective level (organism, swarm). Additionally, I discuss the importance of distributed feedback networks, as well as the benefits and drawbacks of bio-inspiration and bio-mimicry in collective robotics.
The field of swarm robotics draws most of its inspiration from (eu)social animals, which leads to... more The field of swarm robotics draws most of its inspiration from (eu)social animals, which leads to the creation of bio-inspired algorithms. In this study, we show that counterintuitively - seemingly asocial behavior can also lead to successful problem solving performed by a swarm. We decided to test our new algorithm at first with real robots as a proof of concept, because this approach is of high conceptual novelty. We show that our social distancing algorithm (SocDist) performs similarly effective as a simple version of the well established bio-inspired social algorithm BEECLUST in a laboratory experiment, while avoiding some of its drawbacks. In additional agent-based computer simulation experiments we show that such an ‘asocial’ component within a swarm robotic algorithm can lead to a significant performance increase. Beside its effectiveness, the SocDist approach also leads to specific spatial distributions of the swarm robots, which may be useful for practical applications.
Swarm robotics is mainly inspired by the collective behaviour of social animals in nature. Among ... more Swarm robotics is mainly inspired by the collective behaviour of social animals in nature. Among different behaviours such as foraging and flocking performed by social animals; aggregation behaviour is often considered as the most basic and fundamental one. Aggregation behaviour has been studied in different domains for over a decade. In most of these studies, the settings are over-simplified that are quite far from reality. In this paper, we investigate cue-based aggregation behaviour using BEECLUST in a complex environment having two cues –one being the local optimum and the other being the global optimum– with an obstacle between the two cues. The robotic validation of the BEECLUST strategy in a complex environment is the main motivation of this paper. We measured the aggregation size on both cues with and without the obstacle varying the number of robots. The simulations were performed on a custom open-source simulation platform, Bee-Ground, using MONA robots. The results showed that the aggregation behaviour with BEECLUST strategy was able to overcome a certain degree of environmental complexities revealing the robustness of the method. We also verified these results using our stock-flow model.
Fundamentalism and extremism are behavioral traits prominently observed today, ranging from relig... more Fundamentalism and extremism are behavioral traits prominently observed today, ranging from religion and ideology to product choices and even dietary preferences. This study develops a multi-agent model depicting the exemplary case of preventing to eat meat (vegetarianism) or animal-derived products (veganism) as a set of “memes” that can evolve within a society. Behavioral traits can develop over time by means of (local) individual-based adaptation to an ultimate societal phenomenon by cultural (global) learning. The multi-agent model presented here predicts that fundamentalist strategies emerge in significant amounts only if certain extreme starting conditions of the environment (market) or of the initial population are met. When starting from non-extreme conditions, the population develops towards moderate behavioral traits and the market adapts to comfort these society-level adaptations. The underlying social network structure has a significant effect on those processes, as fundamentalism occurs only in fragmented subpopulations as longer-lasting phenomena. A very simple mechanism of social interaction is capable to capture general principles of the emergence of fundamentalist traits in societies.
It is a challenging task to develop morphologies of structures in response to dynamic environment... more It is a challenging task to develop morphologies of structures in response to dynamic environmental factors and constraints. In the context of the EU-funded project flora robotica [1] we are interested in developing selforganized methods that combine local considerations and global requirements and drive the development of structures. Embryogenetic development of biological organisms and cell differentiation are studied for a long time in evolutionary developmental biology (EvoDevo) [2], [3]. Some of the mechanisms from that field are already applied to pattern formation [4] and development of body morphologies [5], [6] and controllers [7] in evolutionary robotics [8] and modular robotics [9]. In this work, vascular system and branching dynamics of plants are used as the source of inspiration for designing a novel algorithm called "Vascular Morphogenesis Controller" (VMC) that is applied to morphological development of modular structures. Plant vessels develop in the stems and roots. They transport water and minerals from the roots to the leaves, and sugars and photosynthates from the leaves to other parts of the plant [10]. There are evidences [11], [12] suggesting that there is a competition between different branches over the vascular growth. The branches that are in better situations (e.g., get more light) produce more photosynthates that flow back from the leaves. The higher flow rate leads to more vascular tissues in the branch and therefore more water and minerals from the roots reach the branch. More water and minerals facilitate the growth of the branch and the branch may end up in an even better situation which in turn reinforces the growth. Different branches with their different local conditions compete over production of new vessels. On the other hand, global resources (i.e., water) are limited and the vessels are subject to degradation as well. Based on the positive and negative feedback loops established by this competition and limitation, a dynamic system of vessels shape the growth of the plants.
In large-scale systems composed of autonomous embodied agents (e.g., robots), unpredictability of... more In large-scale systems composed of autonomous embodied agents (e.g., robots), unpredictability of events, sensor noise and actuator imperfection pose significant challanges to the designers of control software. If such systems tend to selforganize, emergent phenomena prevent classical engineering approaches per se. In recent years, the Artificial Life Lab at the University of Graz has investigated a variety of methods to synthesize such control algorithms used in multi-modular robotics and in swarm robotics. These methods either translate mechanisms directly from biology to the engineering domain (bio-mimicry, bio-inspiration) or generates such controllers through artificial evolution from scratch. In this article I first discuss distributed control algorithms, which determine the collective behavior of autonomous robotic swarms. These algorithms are derived from collective behavior of honeybees and from slime mold aggregation. One of these algorithms is inspired by inter-adult food exchange in honeybees (’trophallaxis’) another one from chemical signaling in slime molds. In addition to the control of robot swarms, control paradigms for multi-modular robotic organisms are presented, which are again based on simulated fluid exchange (hormones) among compartments of robotic organisms. In both domains -swarms and organisms- the control system is self-organized and consists of many homeostatic sub-systems which adapt to each other on the individual (module) and on the collective level (organism, swarm). Additionally, I discuss the importance of distributed feedback networks, as well as the benefits and drawbacks of bio-inspiration and bio-mimicry in collective robotics.
The field of swarm robotics draws most of its inspiration from (eu)social animals, which leads to... more The field of swarm robotics draws most of its inspiration from (eu)social animals, which leads to the creation of bio-inspired algorithms. In this study, we show that counterintuitively - seemingly asocial behavior can also lead to successful problem solving performed by a swarm. We decided to test our new algorithm at first with real robots as a proof of concept, because this approach is of high conceptual novelty. We show that our social distancing algorithm (SocDist) performs similarly effective as a simple version of the well established bio-inspired social algorithm BEECLUST in a laboratory experiment, while avoiding some of its drawbacks. In additional agent-based computer simulation experiments we show that such an ‘asocial’ component within a swarm robotic algorithm can lead to a significant performance increase. Beside its effectiveness, the SocDist approach also leads to specific spatial distributions of the swarm robots, which may be useful for practical applications.
Swarm robotics is mainly inspired by the collective behaviour of social animals in nature. Among ... more Swarm robotics is mainly inspired by the collective behaviour of social animals in nature. Among different behaviours such as foraging and flocking performed by social animals; aggregation behaviour is often considered as the most basic and fundamental one. Aggregation behaviour has been studied in different domains for over a decade. In most of these studies, the settings are over-simplified that are quite far from reality. In this paper, we investigate cue-based aggregation behaviour using BEECLUST in a complex environment having two cues –one being the local optimum and the other being the global optimum– with an obstacle between the two cues. The robotic validation of the BEECLUST strategy in a complex environment is the main motivation of this paper. We measured the aggregation size on both cues with and without the obstacle varying the number of robots. The simulations were performed on a custom open-source simulation platform, Bee-Ground, using MONA robots. The results showed that the aggregation behaviour with BEECLUST strategy was able to overcome a certain degree of environmental complexities revealing the robustness of the method. We also verified these results using our stock-flow model.
Fundamentalism and extremism are behavioral traits prominently observed today, ranging from relig... more Fundamentalism and extremism are behavioral traits prominently observed today, ranging from religion and ideology to product choices and even dietary preferences. This study develops a multi-agent model depicting the exemplary case of preventing to eat meat (vegetarianism) or animal-derived products (veganism) as a set of “memes” that can evolve within a society. Behavioral traits can develop over time by means of (local) individual-based adaptation to an ultimate societal phenomenon by cultural (global) learning. The multi-agent model presented here predicts that fundamentalist strategies emerge in significant amounts only if certain extreme starting conditions of the environment (market) or of the initial population are met. When starting from non-extreme conditions, the population develops towards moderate behavioral traits and the market adapts to comfort these society-level adaptations. The underlying social network structure has a significant effect on those processes, as fundamentalism occurs only in fragmented subpopulations as longer-lasting phenomena. A very simple mechanism of social interaction is capable to capture general principles of the emergence of fundamentalist traits in societies.
It is a challenging task to develop morphologies of structures in response to dynamic environment... more It is a challenging task to develop morphologies of structures in response to dynamic environmental factors and constraints. In the context of the EU-funded project flora robotica [1] we are interested in developing selforganized methods that combine local considerations and global requirements and drive the development of structures. Embryogenetic development of biological organisms and cell differentiation are studied for a long time in evolutionary developmental biology (EvoDevo) [2], [3]. Some of the mechanisms from that field are already applied to pattern formation [4] and development of body morphologies [5], [6] and controllers [7] in evolutionary robotics [8] and modular robotics [9]. In this work, vascular system and branching dynamics of plants are used as the source of inspiration for designing a novel algorithm called "Vascular Morphogenesis Controller" (VMC) that is applied to morphological development of modular structures. Plant vessels develop in the stems and roots. They transport water and minerals from the roots to the leaves, and sugars and photosynthates from the leaves to other parts of the plant [10]. There are evidences [11], [12] suggesting that there is a competition between different branches over the vascular growth. The branches that are in better situations (e.g., get more light) produce more photosynthates that flow back from the leaves. The higher flow rate leads to more vascular tissues in the branch and therefore more water and minerals from the roots reach the branch. More water and minerals facilitate the growth of the branch and the branch may end up in an even better situation which in turn reinforces the growth. Different branches with their different local conditions compete over production of new vessels. On the other hand, global resources (i.e., water) are limited and the vessels are subject to degradation as well. Based on the positive and negative feedback loops established by this competition and limitation, a dynamic system of vessels shape the growth of the plants.
Uploads
Papers by Thomas Schmickl