Foraging robots involved in a search and retrieval task may create paths to navigate faster in their environment. In this context, a swarm of robots that has found several resources and created different paths may benefit strongly from path selection. Path selection enhances the foraging behavior by allowing the swarm to focus on the most profitable resource with the possibility for unused robots to stop participating in the path maintenance and to switch to another task. In order to achieve path selection, we implement virtual ants that lay artificial pheromone inside a network of robots. Virtual ants are local messages transmitted by robots; they travel along chains of robots and deposit artificial pheromone on the robots that are literally forming the chain and indicating the path. The concentration of artificial pheromone on the robots allows them to decide whether they are part of a selected path. We parameterize the mechanism with a mathematical model and provide an experimental validation using a swarm of 20 real robots. We show that our mechanism favors the selection of the closest resource is able to select a new path if a selected resource becomes unavailable and selects a newly detected and better resource when possible. As robots use very simple messages and behaviors, the system would be particularly well suited for swarms of microrobots with minimal abilities.

In this paper, we propose a swarm intelligence localization strategy in which robots have to locate different resource areas in a bounded arena and forage between them. The robots have no knowledge of the arena dimensions and of the number of resource areas. The strategy is based on peer-to-peer local communication without the need for any central unit. Social Odometry leads to a self-organized path selection. We show how collective decisions lead the robots to choose the closest resource site from a central place. Results are presented with simulated and real robots.

When selecting a resource to exploit, an insect colony must take into account at least two constraints: the resource must be abundant enough to sustain the whole group, but not too large to limit exploitation costs, and risks of conflicts with other colonies. Following recent results on cockroaches and ants, we introduce here a behavioral mechanism that satisfies these two constraints. Individuals simply modulate their probability to switch to another resource as a function of the local density of conspecifics locally detected. As a result, the individuals gather at the smallest resource that can host the whole group, hence reducing competition and exploitation costs while fulfilling the overall group's needs. Our analysis reveals that the group becomes better at discriminating between similar resources as it grows in size. Also, the discrimination mechanism is flexible and the group readily switches to a better suited resource as it appears in the environment. The collective decision emerges through the self-organization of individuals, that is, in absence of any centralized control. It also requires a minimal individual cognitive investment, making the proposed mechanism likely to occur in other social species and suitable for the development of distributed decision making tools.

Water is a major preoccupation for our generation since it is crucial in keeping a healthy ecosystem and supporting biodiversity. The state of aquatic systems and water bodies needs to be continiously monitored to make informed decisions and trigger sanitation when necessary. However, observing and tracking the evolution of many water bodies without disturbing and polluting the biotopes is expensive, not scalable, and thus infeasible. This paper presents a way to make sustainable measurements using a new low-cost, open-source, and autonomous monitoring system deployable in a broad network. The smart buoy is deployed and controlled by a central unit that uses lab-graded sensors to measure ambient factors. The custom electronic board offers sustainable electronics integration emphasizing power path and network connectivity. The smart buoy showed an average power consumption of 1.8mA and a cost of 932 euros per device. Currently, five spots have been monitored, which allowed the understanding of why biological events, such as a massive fish dead, occurred. The system is easily expandable and can be used in various applications to increase the knowledge of the underwater ecosystem.

Multi-robot exploration and navigation is a challenging task, especially within the swarm robotics domain, in which the individual robots have limited capabilities and have access to local information only. An interesting approach to exploration and navigation in swarm robotics is social odometry, that is, a cooperative strategy in which robots exploit odometry for individual navigation, and share their own position estimation through peer-to-peer local communication to collectively reduce the estimation error. In this paper, the robots have to localize both a home and a goal location and navigate back and forth between them. The way in which navigational information is aggregated influences both the efficiency in navigation between the two areas, and the self-organized selection of better paths. We propose three new parameter-free mechanisms for information aggregation and we provide an extensive study to ascertain their properties in terms of navigation efficiency and collective decisio

Remembering information is a fundamental aspect of cognition present in numerous natural systems. It allows adaptation of the behavior as a function of previously encountered situations. For instance, many living organisms use memory to recall if a given situation incurred a penalty or a reward and rely on that information to avoid or reproduce that situation. In groups, memory is commonly studied in the case where individual members are themselves capable of learning and a few of them hold pieces of information that can be later retrieved for the benefits of the group. Here, we investigate how a group may display memory when the individual members have reactive behaviors and can not learn any information. The well known conditioning experiments of Pavlov illustrate how single animals can memorize stimuli associated with a reward and later trigger a related behavioral response even in the absence of reward. To study and demonstrate collective memory in artificial systems, we get inspiration from the Pavlov experiments and propose a setup tailored for testing our robotic swarm. We devised a novel behavior based on the fundamental process of aggregation with which robots exhibit collective memory. We show that the group is capable of encoding, storing, and retrieving information that is not present at the level of the individuals.