Ali Emre Turgut

Self-organized flocking with a mobile robot swarm: a novel motion control method

In flocking, a swarm of robots moves cohesively in a common direction. Traditionally, flocking is realized using two main control rules: proximal control, which controls the cohesion of the swarm using local range-and bearing information about neighboring robots; and alignment control, which allows the robots to align in a common direction and uses more elaborate sensing mechanisms to obtain the orientation of neighboring robots. So far, limited attention has been given to motion control, used to translate the output of these two control rules into robot motion. In this paper, we propose a novel motion control method: magnitude-dependent motion control (MDMC). Through simulations and real robot experiments, we show that, with MDMC, flocking in a random direction is possible without the need for alignment control and for robots having a preferred direction of travel. MDMC has the advantage to be implementable on very simple robots that lack the capability to detect the orientation of their neighbors. In addition, we introduce a small proportion of robots informed about a desired direction of travel. We compare MDMC with a motion control method used in previous robotics literature, which we call magnitude-independent motion control (MIMC), and we show that the swarms can travel longer distances in the desired direction when using MDMC instead of MIMC. Finally, we systematically study flocking under various conditions: with or without alignment control, with or without informed robots, with MDMC or with MIMC.

Evolution of Self-Organized Task Specialization in Robot Swarms

Division of labor is ubiquitous in biological systems, as evidenced by various forms of complex task specialization observed in both animal societies and multicellular organisms. Although clearly adaptive, the way in which division of labor first evolved remains enigmatic, as it requires the simultaneous co-occurrence of several complex traits to achieve the required degree of coordination. Recently, evolutionary swarm robotics has emerged as an excellent test bed to study the evolution of coordinated group-level behavior. Here we use this framework for the first time to study the evolutionary origin of behavioral task specialization among groups of identical robots. The scenario we study involves an advanced form of division of labor, common in insect societies and known as “task partitioning”, whereby two sets of tasks have to be carried out in sequence by different individuals. Our results show that task partitioning is favored whenever the environment has features that, when exploited, reduce switching costs and increase the net efficiency of the group, and that an optimal mix of task specialists is achieved most readily when the behavioral repertoires aimed at carrying out the different subtasks are available as pre-adapted building blocks. Nevertheless, we also show for the first time that self-organized task specialization could be evolved entirely from scratch, starting only from basic, low-level behavioral primitives, using a nature-inspired evolutionary method known as Grammatical Evolution. Remarkably, division of labor was achieved merely by selecting on overall group performance, and without providing any prior information on how the global object retrieval task was best divided into smaller subtasks. We discuss the potential of our method for engineering adaptively behaving robot swarms and interpret our results in relation to the likely path that nature took to evolve complex sociality and task specialization.

Cue-based aggregation with a mobile robot swarm: a novel fuzzy-based method

Aggregation in swarm robotics is referred to as the gathering of spatially distributed robots into a single aggregate. Aggregation can be classified as cue-based or self-organized. In cue-based aggregation, there is a cue in the environment that points to the aggregation area, whereas in self-organized aggregation no cue is present. In this paper, we proposed a novel fuzzy-based method for cue-based aggregation based on the state-of-the-art BEECLUST algorithm. In particular, we proposed three different methods: naïve, that uses a deterministic decision-making mechanism; vector-averaging, using a vectorial summation of all perceived inputs; and fuzzy, that uses a fuzzy logic controller. We used different experiment settings: one-source and two-source environments with static and dynamic conditions to compare all the methods. We observed that the fuzzy method outperformed all the other methods and it is the most robust method against noise.

Flocking in stationary and non-stationary environments: a novel communication strategy for heading alignment

We propose a novel communication strategy inspired by explicit signaling mechanisms seen in vertebrates, in order to improve performance of self-organized flocking for a swarm of mobile robots. The communication strategy is used to make the robots match each others headings. The task of the robots is to coordinately move towards a common goal direction, which might stay fixed or change over time. We perform simulation-based experiments in which we evaluate the accuracy of flocking with respect to a given goal direction. In our settings, only some of the robots are informed about the goal direction. Experiments are conducted in stationary and non-stationary environments. In the stationary environment, the goal direction and the informed robots do not change during the experiment. In the non-stationary environment, the goal direction and the informed robots are changed over time. In both environments, the proposed strategy scales well with respect to the swarm size and is robust with respect to noise.

A self-adaptive communication strategy for flocking in stationary and non-stationary environments

We propose a self-adaptive communication strategy for controlling the heading direction of a swarm of mobile robots during flocking. We consider the problem where a small group of informed robots has to guide a large swarm along a desired direction. We consider three versions of this problem: one where the desired direction is fixed; one where the desired direction changes over time; one where a second group of informed robots has information about a second desired direction that conflicts with the first one, but has higher priority. The goal of the swarm is to follow, at all times, the desired direction that has the highest priority and, at the same time, to keep cohesion. The proposed strategy allows the informed robots to guide the swarm when only one desired direction is present. Additionally, a self-adaptation mechanism allows the robots to indirectly sense the second desired direction, and makes the swarm follow it. In experiments with both simulated and real robots, we evaluate how well the swarm tracks the desired direction and how well it maintains cohesion. We show that, using self-adaptive communication, the swarm is able to follow the desired direction with the highest priority at all times without splitting.

GESwarm: grammatical evolution for the automatic synthesis of collective behaviors in swarm robotics

In this paper we propose GESwarm, a novel tool that can automatically synthesize collective behaviors for swarms of autonomous robots through evolutionary robotics. Evolutionary robotics typically relies on artificial evolution for tuning the weights of an artificial neural network that is then used as individual behavior representation. The main caveat of neural networks is that they are very difficult to reverse engineer, meaning that once a suitable solution is found, it is very difficult to analyze, to modify, and to tease apart the inherent principles that lead to the desired collective behavior. In contrast, our representation is based on completely readable and analyzable individual-level rules that lead to a desired collective behavior. The core of our method is a grammar that can generate a rich variety of collective behaviors. We test GESwarm by evolving a foraging strategy using a realistic swarm robotics simulator. We then systematically compare the evolved collective behavior against an hand-coded one for performance, scalability and flexibility, showing that collective behaviors evolved with GESwarm can outperform the hand-coded one.

Investigation of cue-based aggregation in static and dynamic environments with a mobile robot swarm

Aggregation is one of the most fundamental behaviors and has been studied in swarm robotic researches for more than two decades. Studies in biology have revealed that the environment is a preeminent factor, especially in cue-based aggregation. This can be defined as aggregation at a particular location which is a heat or a light source acting as a cue indicating an optimal zone. In swarm robotics, studies on cue-based aggregation mainly focused on different methods of aggregation and different parameters such as population size. Although of utmost importance, environmental effects on aggregation performance have not been studied systematically. In this paper, we study the effects of different environmental factors: size, texture and number of cues in a static setting, and moving cues in a dynamic setting using real robots. We used the aggregation time and size of the aggregate as the two metrics with which to measure aggregation performance. We performed real robot experiments with different population sizes and evaluated the performance of aggregation using the defined metrics. We also proposed a probabilistic aggregation model and predicted the aggregation performance accurately in most of the settings. The results of the experiments show that environmental conditions affect the aggregation performance considerably and have to be studied in depth.

Fuzzy-based aggregation with a mobile robot swarm

Aggregation is a widely observed phenomenon in social insects and animals such as cockroaches, honeybees and birds. From swarm robotics perspective [3], aggregation can be defined as gathering randomly distributed robots to form an aggregate. Honeybee aggregation is an example of cue-based aggregation method that was studied in [4]. In that study, micro robots were deployed in a gradually lighted environment to mimic the behavior of honeybees which aggregate around a zone that has the optimal temperature (BEECLUST). In our previous study [2], two modifications on BEECLUST --- dynamic velocity and comparative waiting time --- were applied to increase the performance of aggregation.

Guiding a Robot Flock via Informed Robots

In this paper, we study how and to what extent a self-organized mobile robot flock can be guided to move in a desired direction by informing some of the individuals within the flock. Specifically, we extend a flocking behavior that was shown to maneuver a swarm of mobile robots as a cohesive group in free space avoiding obstacles in its path. In its original form, this behavior does not have a preferred direction and the flock would wander aimlessly in the environment. In this study, we extend the flocking behavior by “informing” some of the individuals about the desired direction that we wish the swarm to move. The informed robots do not signal that they are “informed” (a.k.a. unacknowledged leadership) and instead guide the rest of the swarm by their tendency to move in the desired direction. Through experimental results obtained from physical and simulated robots we show that the self-organized flocking of a swarm of robots can be effectively guided by a minority of informed robots within the flock. In our study, we use two metrics to measure the accuracy of the flock in following the desired direction, and the ability to stay cohesive meanwhile. Using these metrics, we show that the proposed behavior is scalable with respect to the flock’s size, and that the accuracy of guidance increases with 1) the “stubbornness” of the informed robots to align with the preferred direction, and 2) the ratio of the number of informed robots over the whole flock size.

Comparison of Different Cue-Based Swarm Aggregation Strategies

In this paper, we compare different aggregation strategies for cue-based aggregation with a mobile robot swarm. We used a sound source as the cue in the environment and performed real robot and simulation based experiments. We compared the performance of two proposed aggregation algorithms we called as the vector averaging and naive with the state-of-the-art cue-based aggregation strategy BEECLUST. We showed that the proposed strategies outperform BEECLUST method. We also illustrated the feasibility of the method in the presence of noise. The results showed that the vector averaging algorithm is more robust to noise when compared to the naive method.