Initially, swarm robotic research, such as Holland and Melhuish (1999), focused on the development of swarms of identical robots. More recently, there has been an increased interest in the deployment of different types of robot. In their taxonomy of multi-agent systems, Dudek, Jenkin and Milios (2002) include group composition (homogeneous or heterogeneous) as one of the axes that can be used to discriminate between collectives. It should, however, be pointed out that even a group of seemingly identical robots will become heterogeneous, as differences in sensor tuning, calibration, robot drift, and wear and tear amplify initially negligible differences (in the Case Study section, it is found that certain Predator robots, are much more effective at catching Prey than others, even though their software and hardware are intended to be identical).
Nonetheless, while acknowledging that homogeneity-heterogeneity is best viewed as a continuum rather than a discrete classification, it remains the case that some studies are explicitly concerned with a group of robots that can at least initially be considered to be identical (homogenous groups), while others are concerned with groups that are clearly not identical since they differ in their mechanics, their sensing, their role within the group, or their controllers, or underlying basic behaviours.
The development and use of heterogeneous groups of robots can be justified both in terms of biological inspiration and practical applications. We can briefly consider the biological justification first. The social organisation of ants, social bees and wasps, and termites all depends on polymorphism, defined as the co-existence of two or more functionally-different castes within the same sex. Three basic female castes, for instance, can be found in ants: the worker, the soldier, and the queen. Termites also have a soldier caste specialised for colony defence, and a worker caste. The existence of castes provides a mechanism for the division of labour that depends both on morphological differences and on the age of the insects, since there is usually a temporal division of
labour in a sequence that leads from nest-work, to brood care, to foraging. Even a brief consideration of the organisation of social insects makes it clear that current investiga-tions in swarm robotics, while biologically inspired, do not even come close to reflecting their complexity and effectiveness.
Nonetheless, there are clearly advantages to the use of heterogeneous groups of robots for real-world applications. Often an application demands capabilities that cannot be easily built into a single robot: a robot cannot be both big and small at the same time;
similarly, a single robot may not be able to carry all the sensors needed for a particular task. For example, Grabowski, Navarro-Serment, Panedis, and Khosla, (2002) describe a heterogeneous team they developed for mapping and exploration: a team that consists of four types of robot (large All Terrain Vehicles, medium sized Tank robots, Pioneer robots, and centimetre scale Millibots). The All Terrain Vehicles can transport the smaller robots to distant places of interest; the Pioneers are designed to facilitate exchange of information between team members; the Tank robots are autonomous and can undertake individual missions or coordinate the Millibots; and the Millibots are so small that they can manoeuvre into small spaces. Their team is hierarchically organised, and applied in the task domain of exploration and mapping.
Another example of a heterogeneous group is the marsupial robots employed by Robin Murphy and her colleagues. Murphy (2002) argues for the benefits of transporter or marsupial robots, enabling the transportation of small task-specific robots to the target area, without loss of battery power. The domain she is particularly interested in is that of search and rescue, and in her marsupial robot teams, mother robots can transport daughter robots over rubble to the target site, and can also offer backup and protection, for instance, recharging facilities, collection and processing of sensor data, and shelter from environmental conditions (such as planetary nights).
It can be seen then that there are a number of practical advantages to forming a collection of robots of varied abilities. Also, from the biological point of view, as Birk and Belpaeme (1998) point out, ecosystems with only one species are not biologically plausible. The many issues to be investigated in this area include: the measurement and representation of the degree of heterogeneity; task allocation between members of a heterogeneous team; physical cooperation and coordination between members; and communication.
Issues about the measurement and representation of heterogeneity have been explored by a number of researchers. The most obvious way in which robots in a heterogeneous team might differ is in terms of their physical form. For example, a group of robots might differ in its method of locomotion, which of course affects their mobility.
For instance, in the marsupial robots investigated by Murphy, the mothers and daughters are physically different in that the mothers have the ability to transport and protect the daughters. Grabowski et al. (2002) also consider the effect of different forms of propulsion in their heterogeneous groups. Kephera robots, for example, are wheeled, with small wheels housed in the centre of the robot (http://www.k-team.com/robots/). This is good for flat surfaces, but not for inclines. The millibots they use, on the other hand, can be equipped with thick rubber treads, allowing them to climb inclines. A heterogeneous team could also differ in their sensorial capabilities, and in behavioural capabilities. Other researchers have explored heterogeneity in groups of robots that exists only in software;
the heterogeneous group of robots studied by Ijspeert, Martinoli, Billard, and Gambardella, (2001) differ only in the length of time they grip the sticks, in a stick pulling experiment.
An important issue in heterogeneous and in homogenous robot teams is the way in which task allocation is carried out. One way in which this can be accomplished is through the adoption of specialised roles. For example, in the research described by Goldberg and Mataric (2002), the number of collisions between robots is reduced by using a dominance hierarchy; dominant robots are given priority. An alternative method is to use individual activation thresholds for task allocation. Although it used to be assumed that task allocation within insect societies was a rigid process (Gordon, 1996), more recent research has focused on behavioural flexibility and stressed the importance of external and decentralised factors such as pheromones or individual encounters (for example, Bourke & Franks, 1995). In an activation-threshold model, individuals react to stimuli intrinsically bound to the task in question. For example, neglected brood, or the corpses of dead ants, diffuse an odour of increasing strength. When this stimulus reaches threshold value, an individual reacts by performing the relevant activity (such as grooming the brood, or carrying a corpse out of the nest). If individuals do not have the same threshold values, recruitment is gradual, and team size is thereby regulated.
Krieger and Billeter (2000), using teams of up to 12 real robots, implemented a simple and decentralised task allocation mechanism based on individual activation thresholds.
Their results show that this mechanism resulted in efficient and dynamical task alloca-tion.
Another related issue is that of finding a method of measuring the degree of heterogeneity present in a group. Parker (1994), in her PhD thesis, introduced the concept of task coverage, which measures the ability of a given team member to achieve a specific task, a measure which decreases as groups become more heterogeneous. Balch (2002) introduces a method for measuring robot group diversity, based on social entropy, and argues for the importance of a quantitative metric. He concentrates on evaluating diversity in teams of mechanically-similar agents that use reinforcement learning to develop behavioural policies.
In summary, the issue of group composition, and the move from collections of seemingly identical robots to the development of heterogeneous groups, is one that is increasingly coming to the fore in collective robotics. Interestingly, it is not something that seems to be considered in swarm intelligence research, despite its biological justification. However, in swarm robotics there seems to be a gradual movement towards increasing task differentiation, and heterogeneity as more complex applications are considered and attempted.
APPLICATIONS
The task domains for which collections of simple autonomous robots seem most appropriate are those that occur in areas that are inaccessible or hazardous to humans, and that are likely to benefit from having a number of small, light, expendable, and cheap robots. These include surveillance, monitoring, de-mining (detecting and removing mines), toxic waste disposal, exploration, and search. However, in the current state of research, such tasks are usually not actually carried out, but rather analogous tasks, or tasks that involve components of these, are investigated. Foraging, for instance, involves many subcomponents of the task associated with toxic waste clean up, while
being closely related to the foraging behaviour of biological agents such as ants. Kube and Bonabeau (2000) in a brief review of tasks to which a swarm intelligence approach has been taken, write:
As the reader will perhaps be disappointed by the simplicity of the tasks performed by state-of-the-art swarm-based robotic systems .... let us remind her or him.. (that) .. it seems urgent to work at the fundamental level of what algorithms should be put into these robots: understanding the nature of coordination in groups of simple agents is a first step towards implementing useful multirobot systems.
In the following sub-sections, we will look at a number of task domains in which swarm robotic, or collective robotic, solutions have been sought, although as explained, these often explore only components of an application. Where real-world applications have been developed (for instance, in Urban Search and Rescue), these often involve a compromise in which swarm intelligent methods are combined with global control and communication, or even remote control and teleoperation.