Most of our experiences, as well as our intuition, are usually built on a linear understanding of systems and processes. Complex systems in general, and more specifically swarm robotics in this context, leverage non-linear effects to self-organise and to ensure that 'more is different'. In previous work, the non-linear and therefore counter-intuitive effect of 'less is more' was shown for a site-selection swarm scenario. Although it seems intuitive that being able to communicate over longer distances should be beneficial, swarms were found to sometimes profit from communication limitations. Here, we build on this work and show the same effect for the collective perception scenario in a dynamic environment. We also find an additional effect that we call 'slower is faster': in certain situations, swarms benefit from sampling their environment less frequently. Our findings are supported by an intensive empirical approach and a mean-field model. All our experimental work is based on simulations using the ARGoS simulator extended with a simulator of the smart environment for the Kilobot robot called Kilogrid. 4 The current geopolitical situation motivated our choice of tile colour .

The physiology of living organisms, such as living plants, is complex and particularly difficult to understand on a macroscopic, organism-holistic level. Among the many options to study plant physiology, electrical potential and tissue impedance are arguably simple measurement techniques to gather plant-level information. Despite the many possible uses, our research is exclusively driven by the idea of phytosensing, that is, interpreting living plants' signals to learn information about surrounding environmental conditions. As ready-to-use plant-level physiological models are not available, we consider the plant as a blackbox and apply statistics and machine learning to automatically interpret measured signals. In simple plant experiments, we expose Zamioculcas zamiifolia and Solanum lycopersicum (tomato) to four different stimuli: wind, heat, red and blue light. We measure electrical potential and tissue impedance signals. Given these signals, we evaluate a large variety of methods from statistical discriminant analysis and from deep learning for the classification problem of determining the correct stimulus to which the plant was exposed. We identify a set of methods that successfully classify stimuli with good accuracy without a clear winner. The statistical approach is competitive, partially depending on data availability for the machine learning approach. Our extensive results show the feasibility of the blackbox approach and can be used in future research to select appropriate classifier techniques for a given use case. In our own future research, we will exploit these methods to drive a phytosensing approach for air pollution monitoring in urban areas.

Scalability is a key feature of swarm robotics. Hence, measuring performance depending on swarm size is important to check the validity of the design. Performance diagrams have generic qualities across many different application scenarios. We summarize these findings and condense them in a practical performance analysis guide for swarm robotics. We introduce three general classes of performance: linear increase, saturation, and increase/decrease. As the performance diagrams may contain rich information about underlying processes, such as the degree of collaboration and chains of interference events in crowded situations, we discuss options for quickly devising hypotheses about the underlying robot behaviors. The validity of our performance analysis guide is then made plausible in a number of simple examples based on models and simulations.