We propose a network-based approach to mission planning for a swarm of robots. We model a search and rescue mission with a fleet of aquatic robots, coordinated with a computing central on a rowing boat, to minimize the environmental impact and avoid noticeable waves. We develop a theoretical yet implementable model for the mission management, taking into account energy and multi-robot communication issues. The considered mathematical tools come from network theory and multi-layered graphs. The chosen mission is aquatic cleaning, and in particular trash detection in canals of Venice. We schematize a specific canal as an example, the "Rio di Ca’ Foscari." Computing centers are located in gondolas, and each of them is coordinated with a swarm of aquatic robots. In this context, we consider a newly-designed floating robot, RoboWood, equipped with GPS sensors, distance sensors, and an underwater camera to observe the ground of the canal and store images, to be analyzed onboard and offline. Finally, we discuss a sonification strategy to obtain human-friendly, auditory feedback from the robotic movements.

Movements of robots in a swarm can be mapped to sounds, highlighting the group behavior through the coordinated and simultaneous variations of musical parameters across time. The vice versa is also possible: Sound parameters can be mapped to robotic motion parameters, giving instructions through sound. In this article, we first develop a theoretical framework to relate musical parameters such as pitch, timbre, loudness, and articulation (for each time) with robotic parameters such as position, identity, motor status, and sensor status. We propose a definition of musical spaces as Hilbert spaces and musical paths between parameters as elements of bigroupoids, generalizing existing conceptions of musical spaces. The use of Hilbert spaces allows us to build up quantum representations of musical states, inheriting quantum computing resources, already used for robotic swarms. We present the theoretical framework and then some case studies as toy examples. In particular, we discuss a 2D video and matrix simulation with two robo-caterpillars; a 2D simulation of 10 robo-ants with Webots; a 3D simulation of three robo-fish in an underwater search-and-rescue mission.

What could the Venice of the future look like? A project for an ideal city can take off from an allegory: arts and sciences inside a seed of Lodoicea maldivica, whose bipartition reminds us of a human brain. From the seeds left by the past, we derive the vision of the future. The ideal city certainly needs brains able to conceptualize images and develop ideas, and bridges to strengthen connections and interactions. A well-working brain needs “bridges” as connections between ideas and techniques. My vision for a future city contains a livable and stimulating space enhancing at one time creativity, enthusiasm, and scientific development. To this aim, I use the image and the metaphor of cerebral hemispheres, specialized in activities of different typologies yet interconnected. The image of the brain is one of the symbols of cognition. The right hemisphere deals with artistic expression, while the left hemisphere refers to logic thinking, mathematics, scientific attitude toward the world. Different cerebral areas, which contribute to the complex and rich life of an individual, are a metaphor for different places in a city, contributing to the completeness of a community life. Separation between hemispheres, also as a homage to Venice, is seen as a sort of Canal Grande, and connections are represented by bridges. The whole brain is seen as a giant seed of Lodoicea maldivica: the city of the future needs to develop from seeds, that is, knowledge inherited from the past and new ideas from our minds and thoughts. Thoughts that, in turn, are fed upon the Science of Complexity.

Data sonification enhance and enrich information understanding with an additional sensory dimension. Sonification also opens the way to more creative applications, joining arts and sciences. In this study, we present sequences of chords obtained as auditory feedback from the trajectories of a robotic swarm. The swarm behavior is an emerging effect from simple local interactions and autonomous decisions of each robot. The swarm effect can be identified through sonification outcomes in terms of voice leading patterns. Thus, chord patterns represent behavior patterns. The convergence to the target is represented by the convergence to a specific pitch. The swarm decision process is based upon quantum computing. We first present logic gates and their implementations as quantum circuits, describing examples with 2- and 3-dimensional motion of a 3-robot toy swarm. The considered scenarios are ant foraging (2D) and underwater search and rescue (3D). Then, we provide and discuss some examples of the harmonic sequences that can be obtained as feedback from robotic motion.