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Abstract In recent years, three-dimensional (3D) construction printing has emerged as a viable alternative to conventional construction methods. Particularly promising for large scale construction are collective printing systems consisting of multiple mobile 3D printers. However, the design of these systems typically relies on the assumption of continuous communication between the printers, which is unrealistic in dynamically changing construction environments. As a first step toward decentralized collective 3D printing, we explore an active sensing framework allowing individual agents to reconstruct the shape of the structure, toward assessing other agents' progress in the absence of direct communication. In this vein, the shape of the structure is discretized as a 2D lattice embodying its topology, such that the problem is equivalent to the inference of a network. We leverage environmental modifications introduced by each agent through the printing of new layers to track the structure evolution. We demonstrate the validity of a sequential approach based on system identification through numerical simulations. Our work paves the way to decentralized collective 3D construction printing, as well as other applications in collective behavior that rely on the physical medium to transfer information among agents. 

Zebrafish is a model organism that is receiving considerable attention in preclinical research. Particularly important is the use of zebrafish in behavioral pharmacology, where a number of high-throughput experimental paradigms have been proposed to quantify the effect of psychoactive substances consequences on individual and social behavior. In an effort to assist experimental research and improve animal welfare, we propose a mathematical model for the social behavior of groups of zebrafish swimming in a shallow water tank in response to the administration of psychoactive compounds to select individuals. We specialize the mathematical model to caffeine, a popular anxiogenic compound. Each fish is assigned to a Markov chain that describes transitions between freezing and swimming. When swimming, zebrafish locomotion is modeled as a pair of coupled stochastic differential equations, describing the time evolution of the turn-rate and speed in response to caffeine administration. Comparison with experimental results demonstrates the accuracy of the model and its potential use in the design of in-silico experiments.