In this paper, a distributed algorithm for adaptive task allocation and adaptable partitioning of a swarm into different work-groups is proposed and used in a swarm of underwater robots. The algorithm is based on local interactions of agents and is inspired by honeybee age-polyethism. It is adaptive to changes in the swarm size (workforce) and relative demands (workload) for different tasks and it limits the number of switchings of agents between different tasks enabling specialization of agents. The preliminary version of the algorithm was introduced previously. Here a fully decentralized version of the algorithm is proposed that improves the previous version by removing the need for global information. The algorithm is successfully implemented in swarms of physically-embodied underwater robots while the swarm size and the demands for the tasks change over the course of the experiments.

In this paper, a distributed adaptive partitioning algorithm inspired by division of labor in honeybees is investigated for its applicability in a swarm of underwater robots in one hand and is qualitatively compared with the behavior of honeybee colonies on the other hand. The algorithm, partitioning social inhibition (PSI), is based on local interactions and uses a simple logic inspired from age-polyethism and task allocation in honeybee colonies. The algorithm is analyzed in simulation and is successfully applied here to partition a swarm of underwater robots into groups demonstrating its adaptivity to changes and applicability in real world systems. In a turn towards the inspiration origins of the algorithm, three honeybee colonies are then studied for age-polyethism behaviors and the results are contrasted with a simulated swarm running the PSI algorithm. Similar effects are detected in both the biological and simulated swarms suggesting biological plausibility of the mechanisms employed by the artificial system.

Plant growth is a self-organized process incorporating distributed sensing, internal communication and morphology dynamics. We develop a distributed mechatronic system that autonomously interacts with natural climbing plants, steering their behaviours to grow user-defined shapes and patterns. Investigating this bio-hybrid system paves the way towards the development of living adaptive structures and grown building components. In this new application domain, challenges include sensing, actuation and the combination of engineering methods and natural plants in the experimental set-up. By triggering behavioural responses in the plants through light spectra stimuli, we use static mechatronic nodes to grow climbing plants in a user-defined pattern at a two-dimensional plane. The experiments show successful growth over periods up to eight weeks. Results of the stimuli-guided experiments are substantially different from the control experiments. Key limitations are the number of repetitions performed and the scale of the systems tested. Recommended future research would investigate the use of similar bio-hybrids to connect construction elements and grow shapes of larger size.

Morphology of an artificial structure can be designed beforehand or it can be developed over time via interactions between different parts of the structure. Since structures are supposed to sustain and act in their surrounding environments, a successful generative process needs to consider both the global and local effects of environment during morphogenesis. As in their biological counterparts, many morphogenesis models are distributed over the growing structure. In this paper, a novel distributed model, called Vascular Morphogenesis Controller (VMC), is introduced by being inspired from branching mechanisms in plants where every branch of a plant acts as an autonomous agent competing with the other agents for a larger share of the resources for growth. To the best of our knowledge, this is the first explicit use of distribution of limited resources in morphogenesis process of artificial structures. The model is implemented for growing a simulated modular robot that is designed based on a physical robot. The parameters of model are successfully evolved to direct the growth of robots in different environmental condition, i.e., in harsh and calm environments, in various light conditions, and in a layered environment. The results demonstrate usability of the model despite simplicity of its logic.