The foraging task is one of the canonical testbeds for cooperative robotics, in which a collection of coordinated robots have to find and transport one or more objects to one or more specific storage points. Swarm robotics has been widely considered in such situations, due to its strengths such as robustness, simplicity and scalability. Typical multi-robot foraging systems currently consider te...

The recruiting system in foraging ant colonies is a typical example of swarm intelligence. The system is underpinned by the use of volatile pheromones which form a trail connecting from nest to food. We have incorporated this property into the behavior of the swarm of real robots. Because the trail is narrow, avoiding overcrowding on the trail, as well as in the environment, is a critical issue...

Ants balance the use of remembered private information and communicated public information to maximally exploit resources. This work determines how the strategy that best balances these two sources of information, and the performance of that best strategy, depend on the information in the distribution that is available to be exploited, and the number of ants in the colony. We answer this questi...

This paper presents a simple adaptation mechanism to automatically adjust the ratio of foragers to resters (division of labour) in a swarm of foraging robots and hence maximise the net energy income to the swarm. Three adaptation rules are introduced based on local sensing and communications. Individual robots use internal cues (successful food retrieval), environmental cues (collisions with te...

In spite of its significance for the adaptability of autonomous robotic swarms, the dynamic allocation and re-distribution of robots to tasks (i.e., role-allocation and role-switching behaviour) is still a design challenge in swarm robotics. This study investigates a simulated foraging scenario in which the variability of the environmental conditions requires that robots switch between two role...

The foraging scenario is important in robotics, because it has many different applications and demands several fundamental skills from a group of robots, such as collective exploration, shortest path finding, and efficient task allocation. Particularly for large groups of robots emergent behaviors are desired that are decentralized and based on local information only. But the design of such beh...

Foraging is a benchmark problem in robotics especially for distributed autonomous robotic systems. The systematic study of robot foraging is important for several reasons: firstly, because foraging is a metaphor for the broad class of problems integrating robotic exploration, navigation and object identification, manipulation and transport; secondly, in multi-robot systems foraging is a canonic...

We present a new social animal inspired emotional swarm intelligence technique. This technique is used to solve a variant of the popular collective robots problem called foraging. We show with a simulation study how simple interaction rules based on sensations like hunger and loneliness can lead to globally coherent emergent behavior which allows sensor constrained robots to solve the given pro...

A key challenge in designing robot teams is determining how to allocate team members to specific roles according to their abilities and the demands of the environment. In this paper we explore this issue in the context of multi-robot foraging, and we show that optimal foraging theory can be used to evaluate our work in learned multi-robot foraging tasks. We present a means by which members of a...

In this demo, we show the emergence of Swarm Intelligence in physical robots. We transferred an optimization algorithm which is based on bee-foraging behavior to a robotic swarm with the advantage that this algorithm, and so the actual robots, do not require input of environmental parameters (e.g., pheromones).