**1. Introduction**

Swarm robotics is an approach that applies the smart swarm behaviour [1] observed in flocks of birds, schools of fish and swarms of social insects to engineering problems [2–4]. This study focuses on the excellent functions of swarms of ants, which are social insects. Ants sustain large colonies through caste systems, with the queen at the top, which assign different roles to each caste member. The perception functions and action rules of ants are limited, and communication between them can only be conducted through different pheromones. The queen ant cannot monitor everything that happens in a colony and cannot give instructions to each ant directly. Nevertheless, ants are successfully assigned different roles, such as colony protection, food exploration and foraging, without any centralised management system [5]. The autonomous role assignment mechanism of ants may be useful for transport automation by using several autonomous mobile robots in large warehouses and for search-and-rescue operations using several autonomous drones in disaster relief.

As one of the autonomous role assignments in termites, a worker ant specialises as a soldier ant [6]. However, an appropriate role assignment system is required because an excessively increasing number of soldier ants reduce the amount of collected feeds. Therefore, the specialisation of a worker ant to a soldier ant is impeded by a soldier pheromone, the concentration of which rises with the number of soldier ants. In addition, a colony's autonomous role assignment allows it to adapt to changing circumstances. For example, when food becomes scarce, a colony needs to increase the number of ants exploring new food sources as well as the number of ants foraging for food once a new food source has been discovered. Rather than assigning roles in a top-down manner, ants assign roles appropriately through local communication using pheromones.

Bonabeau et al. [7–9] modelled this role assignment using a response threshold model. The response threshold model is an equation that describes the sensitivity of ants to pheromones. There are two types of ants [10]: one with high sensitivity to pheromones and the other with low sensitivity. These different sensitivities are thought to contribute to an autonomous role assignment. However, the conventional response threshold model uses the ratio of workers in an ant colony as an external stimulus, ignoring the crucial factor that social insects can assign roles through local communication.

In contrast, Gordon et al. [11–13] revealed that an ant's tendency to perform midden work1 or foraging work is related to the recent history of its contact with an ant engaged in those works based on the observation of red harvester ants. Our research group has proposed an autonomous role assignment and task allocation method with local interactions in scalable swarm robotic systems [14, 15]. The method used a response threshold model using the ratio of encountered foraging ants in the short term as an external stimulus and mimicking the action rules of real ants. We applied the proposed method to ant foraging problems in a dynamic environment with a varying number of ants [15]. Through simulation results, we confirmed that, during internal environment fluctuations, the proposed method using local interactions outperformed the conventional method using global information.

In this study, we propose a simple autonomous role assignment method using contact stimuli with foraging ants, rather than the ratio of encountered foraging ants in the short term. To evaluate the proposed method's effectiveness, we apply the method to ant foraging problems in a dynamic environment with fluctuating amounts and distributions of feeds. Through simulation results, we demonstrate that, during external environment fluctuations, the proposed method using local interaction outperforms the conventional method using global information. In addition, we demonstrate that the method can successfully perform role assignment in an ant colony by switching between exploring and foraging behaviours through contact stimuli with foraging ants.

The rest of this chapter is organised as follows. Section 2 explains how to model an ant foraging problem. Section 3 shows the new response threshold model. Section 4 demonstrates the proposed method's effectiveness through simulations. Conclusions and future work are discussed in Section 5.
