Swarm intelligence and behavior of swarm robots

Currently, robotics is a rapidly developing knowledge field with great potential in all spheres of modern society. The most relevant recent developments in this field are group robotics projects. These studies are closely related to the effect of system behavior, which can be observed in various social insects called swarm intelligence. Such a feature is that clear and simple rules of conduct of each group member create complex organized behavior of the whole group. The intended group behavior is formed by robots interacting with one another and their surroundings.

Swarm robotics

Group, or swarm robotics, is a field that explores and finds new approaches to coordinating systems consisting of many robots, predominantly of a simple design. In such scenarios, the predictive behavior of the collective is the result of the interaction of robot units among themselves and with the environment. The results of biological studies of insects, namely ants and bees, as well as the results of studies in other areas of nature where swarming behavior takes place, were adapted in the direction of artificial swarm intelligence. The activity of the whole swarm should condition each action of a robot unit in such a system. Interaction and interconnection between rank-and-file robots in the system are ordered, and each member has rules and tasks. It is the interaction between group members that creates constant feedback. Accordingly, the complex organized behavior of the whole swarm is embodied through simple rules of individual conduct. The notion of central control recedes into the background, and swarm intelligence, and even group intelligence within one large swarm emerges instead. The system will be controlled based on the overall task of the group, as well as the location of each robot at a certain point in time, predicting the behavior of surrounding participants. Creating complex systems consisting of simple components is connected with solving several specific problems, typical for robots working together. Among them are noted such as:

  • The unpredictable constant change of the external environment up to and including conscious counteraction,
  • incomplete data on the environment and group members
  • a great variety of vectors of ways to achieve the goal, structures of the team, distribution of roles, etc.,
  • distributed and dynamic character of planning of actions of a collective,
  • problems caused by the fact that swarming systems are a set of physical objects functioning in a complex environment (reliable communication issues, distribution of the team in space, etc.),
  • other technical problems (network architecture, protocols, operating tools, etc.).

Characteristics of a swarm of robots

The advantages and characteristics of a swarm of robots can be traced by comparing such a system with a single robot. A robot swarm's features are similar to those of insect swarms in nature. An individual robot usually has a complex structure and various control modules, as a result of which the design, development, and maintenance costs are pretty high. Such a robot is quite vulnerable. Damage to even small parts can lead to the failure of the entire system. A swarm of robots performs the assigned tasks through intergroup interaction. Such systems have the advantage of multiple uses of simple robots, as well as low cost and low maintenance costs. Swarm robots are particularly suited to large, scalable tasks. Swarm systems are scalable, allowing robot units to enter or exit the interaction anytime without interrupting the task. A swarm adapts to changes in the number of robots entering it using only local communication. Such a system might be created using radio frequency or infrared wireless data transmission technologies. It means that such systems are flexible enough not to require changes in both design and software. Therefore, swarm robots are well applicable to real-world conditions. It is also impossible not to note such a property of such systems as parallelism. The number of participants in a swarm of robots increases quite quickly, giving large systems the ability to focus on multiple goals within a single task. It indicates that such a swarm can perform tasks involving multiple targets distributed over various environments. This way, we reduce the time it takes to complete a task. The following distinguishing characteristic is stability. Based on scalability, a swarm of robots is highly reliable, even when some robot units have become inoperable due to various factors. Damage to one or more robots in the group generally does not disrupt the operation. Reducing the number of robots in a swarm leads to the degradation of such a system, thereby reducing the efficiency of the multitude. Still, the remaining part tends to perform the task at hand. Such a feature is essential for functions in extreme conditions. The loss of functionality of individual units in a single robot can disrupt the work it performs, and attempts to duplicate the most basic functional units of a robot lead to an increase in weight, size, and cost of the robot but does not increase efficiency (even reduce it, given the large size and mass). The cost-effectiveness of such systems cannot be overlooked. It follows from the above that the cost of maintenance, development, and production of robot swarms is significantly lower than that of a complex of single individual robots, even if the number of hives is hundreds or thousands. Mass production of swarms of robots is possible in contrast to serial high-precision production of personal robots. Regarding energy costs, a single robot in a swarm has a simple design and is smaller than an individual robot; therefore, energy costs and battery capacity are not as high. It means that the life cycle of a swarm of robots can be increased. In an environment with no fuel or power reserves, a robot swarm is more suitable than a traditional individual robot.

Possible management strategies for the swarm system:

  • centralized - remote control with a dedicated base station, the swarm leader is assigned from a central node,
  • Decentralized - the swarm leader is determined based on some algorithm and does not depend on the central control station,
  • Mixed - combines the advantages of centralized and decentralized strategies by allocating the swarm leader based on one of the algorithms with the transfer of control rights to the operator, if necessary. Depending on the nature of information exchange in the swarm, two scenarios are possible:
  1. A robot that has detected a target reports its coordinates to neighboring robots, which pass this information down the chain to their neighbors until it is known to all robots in the group; they then change their trajectory toward the target.
  2. A robot that has detected the target cannot tell its coordinates to other robots in the swarm; in doing so, it changes its trajectory towards the target; other robots related to it by the rules of admissible distances follow it, i.e., it becomes the "leading" robot.

Applications of robot swarms

The number of possible applications of robot swarms is quite large. Among them:

  • Extraction of raw materials. This application area implies excellent opportunities but also requires many skills from a robot swarm, such as collective exploration, finding the shortest path, efficient distribution, and task management. It also includes the task of collectively transporting an object.
  • Working in extreme situations, carrying out search and rescue operations at the sites of natural and man-made disasters and in combat zones. For example, a swarm of robots can solve the problem of mine clearance faster and cheaper than an individual robot—executing technical operations, including those in hazardous and hazardous industries. Robots of small size and weight can move freely in cramped aisles, remaining unnoticed by enemy radar stations.
  • Monitoring, surveying, and studying the planet Earth and other solar system planets. It may also include the task of surveillance of territories and waters in conditions of organized counteraction of the enemy, the job of the search for victims in the rubble after natural or man-made disasters, and the task of tracking and neutralization explosive devices in anti-terrorist operations in dense urban areas.
  • Cleaning surface, sea and ocean water areas, and cosmic space from dangerous chemical and radioactive substances.
  • Performance of some surgical operations, such as noninvasive removal of malignant tumors. Advances in robot design tend to miniaturize and make construction cheaper. Swarm robots can perform reconnaissance functions, inspecting various structures, reducing the time required to complete this task. Robots in a swarm have limited sentience capabilities, but the collective perception of the hive can be channeled to implement global studies (terrain mapping). Such tasks as space exploration using nanorobots in human veins and arteries for medical purposes (to fight diseases) can be imagined shortly. The fundamental factors in swarming robotic systems are cost and miniaturization. These are the two main challenges in developing large groups of robots. Based on the above, the most justified approach is to implement swarm intelligence to achieve meaningful behavior at the group level rather than at the individual level. Swarm robotics opens up the possibility of creating swarms of robots in the future that can collectively solve many tasks while informationally and physically uniting into a single entity based on the principle of self-organization. It is crucial to understand that the functionality of the robot swarm as a whole is not greatly affected by the failure of a single robot. In the process of creating various robots, the task of modeling different algorithms for implementing robot movement, cooperation with other robot neighbors, and interaction with the outside world arise. Thanks to software development, it is possible to test the desired algorithms on a virtual model, study their strengths and weaknesses, and eliminate deficiencies without having to build a real robot.

Current shortcomings

The use of swarm robotic systems is currently still rare. Swarm size often depends on the number of robots available to companies or research agencies and is not always chosen according to the desired swarm behavior. Although research has been going on for decades, there has not yet been a breakthrough in swarm robotics, especially for industrial applications. It is because there are still several open questions. First of all, the reliability of swarm robots is a concern. Natural swarms operate with the assumption that individual swarm members can fail. High reliability and availability are required in engineered swarms to keep the system running. Failure of individual swarm members can increase operational costs and lead to security problems. Swarm behavior with its emergent characteristics, performed by autonomous robots relying on distributed information, cannot provide the required security guarantees. Therefore, many industrial projects still rely on centralized control, such as agriculture and warehouses. In these projects, the term "swarm" is used exclusively to refer to a large number of agents. The implementations ignore the basic idea of swarm robotics, which is distributed decision-making that leads to self-organizing behavior. Although robots can detect their environment, collect data locally, and transmit that data to the rest of the swarm, they rely on a central unit. This central unit either predetermines the behavior of each robot or, in more dynamic scenarios, processes the information received from the robot to control its behavior. In addition to technological limitations, security is an essential issue in communication. It is of particular interest for military applications. First, the information exchanged by the robots can be sensitive and should not be disclosed to those parties. Second, swarm behavior can be influenced based on the information swarm members receive. It means that swarm behavior can be affected by changing the messages exchanged by the robots or by introducing false information into the swarm. Therefore, communication in the multitude must be encrypted. It is crucial when a central station sends commands to control the swarm.

Prospects for further improvements and applications

Swarming algorithms are based on self-organizing swarm behavior, such as observed in teeming natural systems such as insect colonies or flocks of birds, which can operate under highly diverse and dynamic conditions. The same is true of robot swarms. They are designed to work in the physical world, which typically faces constant dynamic changes and must cope with events and external conditions that are difficult to predict or model. In addition to the tremendous potential for applications in fields such as logistics, agriculture, and inspection, one suitable working environment for swarms is placed unsuitable for humans, including those that are hard to reach, dangerous, or dirty. Robot systems in these environments can help better observe, understand and take advantage of swarming behavior: adaptability, reliability, and scalability. Compared to a single central robot, the advantages of robotic group systems are the wide scalability with a single local communication, fault tolerance, and the ability to self-organize and self-regulate. The field of application of such techniques is continuously increasing. It ranges from autonomous search and rescue operations to the deployment of decentralized autonomous systems for protection. However, at present, the unpredictability and rapid dynamics of the external environment determine the number of problems associated with incomplete and contradictory data about the state of the outside world, as well as information about other team members, with a variety of options to achieve the goal, team structures, and others. Solving these problems will qualitatively improve both hardware and software of robots included in the groups, increase the system's flexibility, and increase the reliability and power of a group of robots.