We present the Kilogrid, an open-source virtualization environment and data logging manager for the Kilobot robot, Kilobot for short. The Kilogrid has been designed to extend the sensory-motor abilities of the Kilobot, to simplify the task of collecting data during experiments, and to provide researchers with a tool to fine-control the experimental setup and its parameters. Based on the design of the Kilobot and compatible with existing hardware, the Kilogrid is a modular system composed of a grid of computing nodes, or modules, that provides a bidirectional communication channel between the Kilobots and a remote workstation. In this paper, we describe the hardware and software architecture of the Kilogrid system as well as its functioning to accompany its release as a new open-hardware tool for the swarm robotics community. We demonstrate the capabilities of the Kilogrid using a 200-module Kilogrid, swarms of up to 100 Kilobots, and four different case studies: exploration and obstacle avoidance, site selection based on multiple gradients, plant watering, and pheromone-based foraging. Through this set of case studies, we

0000−0001−5550−8467] , Gabriele Valentini 2[0000−0002−8961−3211] , and Marco Dorigo 1[0000−0002−3971−0507]Abstract. The design and control of swarm robotics systems generally relies on either a fully self-organizing approach or a completely centralized one. Self-organization is leveraged to obtain systems that are scalable, flexible and fault-tolerant at the cost of reduced controllability and performance. Centralized systems, instead, are easier to design and generally perform better than self-organizing ones but come with the risks associated with a single point of failure. We investigate a hybrid approach to the control of robot swarms in which a part of the swarm acts as a control entity, estimating global information, to influence the remaining robots in the swarm and increase performance. We investigate this concept by implementing a consensus achievement system tasked with choosing the best of two resource locations. We show (i) how estimating and leveraging global information impacts the decision-making process and (ii) how the proposed hybrid approach improves performance over a fully self-organizing approach.The final authenticated publication is available online at https://doi.

The problem of gas detection is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. Using mobile robots for gas detection has several advantages and can reduce danger for humans. In our work, we address the problem of planning a path for a mobile robotic platform equipped with a remote gas sensor, which minimizes the time to detect all gas sources in a given environment. We cast this problem as a coverage planning problem by defining a basic sensing operation -a scan with the remote gas sensor -as the field of "view" of the sensor. Given the computing effort required by previously proposed offline approaches, in this paper we suggest a online coverage algorithm, called Next-Best-Smell, adapted from the Next-Best-View class of exploration algorithms. Our algorithm evaluates candidate locations with a global utility function, which combines utility values for travel distance, information gain, and sensing time, using Multi-Criteria Decision Making. In our experiments, conducted both in simulation and with a real robot, we found the performance of the Next-Best-Smell approach to be comparable with that of the state-of-the-art offline algorithm, at much lower computational cost.