**1. Introduction**

In nature, hormones provide an adaptation technique that cues behavioural change through chemical processing. As stimuli reach cells or organs hormone chemicals are produced and diffused throughout the body. The build up and gradual decay of these hormones as they are metabolised gives an organism contextual information based on how frequently stimuli are received. The balance and concentration of various hormones can then influence behaviour of the organism. These hormone induced changes to behaviour have been observed in a variety of natural examples [1–3].

In the context of robotics, previous work has shown that virtual hormones can be engineered to control, arbitrate and adapt swarms of robots amongst a small set of behaviours in a similar manner to the examples seen in nature [4–6]. However, it is yet to be shown how hormone systems could be used when a large array of behaviours and task types are available to a swarm. Evidence of virtual hormones being used to control such systems in simulation would provide evidence of their viability in non-abstracted tasks and support virtual hormone implementation in physical systems. This paper identifies for the first time, the viability of combining multiple hormone systems at once, each regulating a separate function or feature of the swarm. The primary goal of this amalgamation of hormone systems will be to ensure that the benefits of each system can provide improvements to the energy efficiency of a foraging swarm when combined, without disrupting the performance of other systems.

Having already explored several applications for hormone inspired systems in previous work [5,7] in which virtual hormone systems have effectively regulated behaviours and preference, respectively selecting appropriate states in a dynamic environment and allocating robots to environments based on their performance across different terrains. The work in this paper combines these applications to create an energy efficient foraging swarm regulated by numerous, simultaneously functioning hormones.

The hormones comprising the amalgamation operate at different levels of a behavioural hierarchy (illustrated in Figure 1), controlling preference, behavioural control and actuator control. Combining systems acting at these different levels of behaviour allows for the swarm to be controlled by hormones at every stage of operation, truly testing the combined systems capabilities and compatibility. This, alongside the fact that more than three times the number of individual hormone types previously studied have been used in these experiments means that the number of hormones used in this amalgamation can be considered numerous.

**Figure 1.** Behavioural hierarchy for those behaviours investigated within this paper.

Section 3 investigates virtual hormone driven motor control as a method to improve energy efficiency in the foraging swarm. This will focus on the need for adaptive motor speeds and their implementation.

Section 4 explores the compatibility between this new system and one governing sleep [5]. The potential energy efficiency benefits of combining a sleep system and a virtual hormone framework are examined.

Section 5, the swarm will be diversified, using the heterogeneous wheel types designed in [7], and a system capable of self analysis for task reallocation is combined with the previously established hormone speed and sleep regulation. Thus creating a system with 6 or more simultaneously acting virtual hormones in each member of the swarm, depending on the number of environments available to the swarm.

The implementation of this complex virtual hormone system will be effective for live adaptation and produce significant improvements to energy efficiency in foraging examples over individual hormone systems.

Finally, Section 7 gives a number of conclusions for the presented work and suggests future areas of investigation.

#### **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 *Hr* and *Hl* with wheel speed changing proportionately with the magnitude of hormone value. 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 Kernbach 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. However, while this may be appropriate for individual robots whose performance and interactions can be predicted, in the context of swarms of robots exploring dynamic and volatile environments the level of on-line adaptation a virtual hormone system provides to the swarm will typically produce a better performance.

While the advantages of behavioural and preference control have be previously studied, there is very little literature on energy efficient speed control systems capable of adapting to demand. There are some examples of research investigating optimal speeds for energy efficiency [13,14]. However, this research only relates to rail vehicles, providing little relevance to puck robot vehicles. For this reason, the next section begins by obtaining data from real robots to obtain information that can be used to produce the motor driving hormone system before it is added to the other, previously developed hormone systems.

#### **3. HIBAS Implementation for Control of a Foraging System with Deviating Motor Speeds**

Hormone Inspired behavioural arbitration systems (HIBAS) have been studied using energy efficiency as the target output [5,7]. However, the speed at which robots move and the efficiency of their movement, vital to energy efficiency, have not been investigated. When simulating the energy consumption of robots it is typically assumed that robots in the swarm are either moving at a specific speed, stationary or consuming a fixed quantity of energy in a given behaviour state [5,12,15,16]. This section investigates the viability of virtual hormone implementation to directly control and adapt wheel speeds to achieve improved energy efficiency when foraging. A 'demand' concept will be present in the task that allows the user to specify, prior to or during use, the number of items to be gathered in a given time period. The purpose of this is to add an additional complexity for the swarm to overcome through adaptation.
