## 2 Background  
Virtual hormones and hormone-inspired systems have previously been used to directly control the motor functions of a single robot. In [8] the authors presented a method that modelled a robot as two cells controlling the left and right motor of a puck robot, each motor was driven by their own hormones \(H_{r}\) and \(H_{l}\) with wheel speed changing proportionately with the magnitude of hormone value. Figure 1: Behavioural hierarchy for those behaviours investigated within this paper. The hormones for each cell were stimulated by a proximity sensor and were capable of diffusing between cells, acting as an inhibitor to the opposing hormone when present in the neighbouring cell. With the hormone values corresponding to the wheel speeds on the respective sides of the robot, this produced an effective hormone controlled method for obstacle avoidance. The study found that this system could be successfully implemented in hardware and could be well studied with an exhaustive parameter sweep for'reasonable computational cost'. Similarly, work by [PERSON] et al [9] produced a system which allowed hormones to regulate the movement of individual robots in a similar manner to [8]. This work added additional function to the virtual hormone, using the same hormone to regulate an additional behaviour state. In this new behaviour state the robots conjoined to produce a larger, specialised morphology. The hormone in this state was re-purposed to create a hormone gradient, regulating the size of the newly formed conjoined organism. This showed that, while explicit control over a robot is attainable with a virtual hormone system, virtual hormones can also be used to effectively arbitrating behaviour states. Following this work additional hormone-inspired controllers have been successfully implemented to adapt swarm morphology, identifying context to environments via stimuli and then constructing appropriate formations [4; 10]. These studies show that hormone-inspired systems can be engineered to provide an effective, computationally inexpensive method for robot control. The need for mid-task adaptation for the energy efficient use of robot swarms has been highlighted in works such as [11]. In which the energy consumption of several bio-inspired robotic coordination procedures were investigated. This investigation found that energy consumption typically increased in line with parameters (e.g., swarm size, arena size, number of tasks). It is, therefore, important that such parameters are understood and controlled for before engaging in a task. This finding strengthens the demand for self allocating systems such as [5; 7; 12] that modulate the number of active robots performing a task. These self regulating systems reduce the need for a centralised decision on swarm size and means that swarms can instead perform multiple different tasks in series or explore different environments sequentially, without needing to return and redeploy. In the previous implementations of hormone arbitration systems found in [5; 7] the adaptive properties of hormone equations have been utilised for both task arbitration and robotic preference. By using hormone equations that provide a value that decays over time and increases as specified stimuli are encountered, environmental features can be extrapolated based on the current value provided by the equation. Through the comparison of hormone values receiving different stimuli or the comparison of hormone values present in different robots, hormone equations provide a powerful tool at a low computational cost for respectively regulating tasks or ranking the performance of robots within a swarm. Using virtual hormones as a method for behavioural control, while providing a strong method for adaptation, does take an element of control away from the user. In traditional behavioural control where user defined thresholds or specific actions are used for behaviour transition, systems can be produced that behave consistently and repetitively in a manner that virtual hormones cannot.