Ryusuke Fujisawa

Communication using pheromone field for multiple robots

In this paper, we consider a issue that the reliable and the inexpensive communication method in swarm robotics. The ants forage for preys by using pheromone trails. They lay down the pheromone trails between preys and a nest. By detecting the trail pheromone, they can find the preys. Though they do not have excellent intelligence, they can communicate with each other and cooperate by adding information to the environment, like a pheromone. This communication method has a merit that an agent does not need to memorize the place of the preys. We consider to answer the issue that ldquoHow do the swarm robots communicate using pheromone trail?rdquo. We construct a swarm behavior simulator and develop swarm robots that communicate using the pheromone trail. We demonstrate the effectiveness of the communication using the pheromone trail by computer simulations and experiments using swarm robots. To realize this purpose, we design a swarm behavior algorithm, based on 4 perceptual signs (stimuli) and 3 effector signs (actions). In the simulations, an experimental field is discretized by computational grids, and evaporation and diffusion are phenomena of the pheromone modeled by discretized equations. The proposed algorithm is demonstrated by the simulation. Simulation result shows that proposed algorithms act effectively. Based on the simulation results, we set three robots, one nest and one prey in the flat experimental field. We observe three robotspsila behavior and the state of the environment for 20 minutes. The robots laid down the pheromone trail between the nest and the prey, and reinforced the pheromone trail many times. This fact means that swarm robots can realize the function of the chemical, indirect, plastic and local communication like ants by using the pheromone trail.

Dependency by Concentration of Pheromone Trail for Multiple Robots

In this paper, we discuss the concentration dependency of pheromone communication in swarm robotics. Instead of a pheromone trail and the insect antenna, we used ethanol and an alcohol sensor. This experimental system has a trade-off problem; high concentrations of the pheromone yield high signal strength but the signal duration is short, while low pheromone concentrations yield low signal strength but a long signal duration. We examined the optimal pheromone concentration for a swarm of robots. For this purpose, we developed a swarm behaviour algorithm and swarm robots that communicate using a pheromone trail. In addition, we discuss the effects of the pheromone concentration.

Designing pheromone communication in swarm robotics: Group foraging behavior mediated by chemical substance

In swarm robotics, communication among the robots is essential. Inspired by biological swarms using pheromones, we propose the use of chemical compounds to realize group foraging behavior in robot swarms. We designed a fully autonomous robot, and then created a swarm using ethanol as the trail pheromone allowing the robots to communicate with one another indirectly via pheromone trails. Our group recruitment and cooperative transport algorithms provide the robots with the required swarm behavior. We conducted both simulations and experiments with real robot swarms, and analyzed the data statistically to investigate any changes caused by pheromone communication in the performance of the swarm in solving foraging recruitment and cooperative transport tasks. The results show that the robots can communicate using pheromone trails, and that the improvement due to pheromone communication may be non-linear, depending on the size of the robot swarm.

Cooperative Transportation by Swarm Robots Using Pheromone Communication

Ants communicate with each other using pheromones, and their society is highly sophisticated. When foraging, they transport cooperatively with interplay of forces. The swarm is robust against changes in internal state, and shows flexibility in dealing with external problems. In this brief paper, we focus on the robot swarm that achieves cooperative transportation making use of ethanol as a substantial artificial pheromone.We also propose a swarm system with a newly developed algorithm that enables cooperative transportation of real robots. They will transport food to the nest analogous to the behaviour of a swarm of ants. Emphasis will be placed on the systematic task solution process.We present a number of experiments demonstrating the robustness and flexibility of the system and also confirming the effectiveness of the algorithm.

Effectiveness of tuning of pheromone trail lifetime in attraction of robot swarm

We focus on swarm behavior of ants. They communicate with each other using pheromone. They forage food using pheromone trail to attract many ants to foods. They also realize adaptive foraging by adjusting the property of the pheromone trail according to changes in the environment. It is effective to apply the swarm behavioral mechanism to the robotics. We propose adjustment method of the trail duration time based on the food quantity. We obtained simulation and experimental results that the proposed adjustment of the trail duration time is effective.

Anomalous diffusion on the servosphere: A potential tool for detecting inherent organismal movement patterns

Tracking animal movements such as walking is an essential task for understanding how and why animals move in an environment and respond to external stimuli. Different methods that implemented image analysis and a data logger such as GPS have been used in laboratory experiments and in field studies, respectively. Recently, animal movement patterns without stimuli have attracted an increasing attention in search for common innate characteristics underlying all of their movements. However, it is difficult to track the movements in a vast and homogeneous environment without stimuli because of space constraints in laboratories or environmental heterogeneity in the field, hindering our understanding of inherent movement patterns. Here, we applied an omnidirectional treadmill mechanism, or a servosphere, as a tool for tracking two-dimensional movements of small animals that can provide both a homogenous environment and a virtual infinite space for walking. To validate the use of our tracking system for assessment of the free-walking behavior, we compared walking patterns of individual pillbugs (Armadillidium vulgare) on the servosphere with that in two types of experimental flat arenas. Our results revealed that the walking patterns on the servosphere showed similar diffusive characteristics to those observed in the large arena simulating an open space, and we demonstrated that our mechanism provides more robust measurements of diffusive properties compared to a small arena with enclosure. Moreover, we showed that anomalous diffusion properties, including Lévy walk, can be detected from the free-walking behavior on our tracking system. Thus, our novel tracking system is useful to measure inherent movement patterns, which will contribute to the studies of movement ecology, ethology, and behavioral sciences.

Lévy walk enhances efficiency of group foraging in pheromone-communicating swarm robots

This paper describes an implementation of Lévy walk (or Lévy flight) to pheromone communicating swarm robots. Lévy flight is a special class of random walk in which the step length distribution is given by power law distribution. Lévy flight is known to maximize the efficiency of resource searches in uncertain environments. Using computer simulations, we show that the Lévy walk-like searching strategy can maximize the group foraging efficiency of the swarm robots using pheromone trails (mimicking ant group foraging), as well as maximize individual searching area. The Lévy walk was achieved by adjusting the probability per unit time with which an individual robot moves forward (otherwise it turns to right, to left, and reverse). We discuss the effect of swarming on optimal parameter values of Lévy walk. Optimization of individual searching strategies should be studied further, both in swarm robots and real organisms.

Increasing individual density reduces extra-variance in swarm intelligence

Social organisms form a swarm and forage preys, collectively and effectively [1]. The swarm has to inhibit a variance of foraging frequency for survival, which ensures stable and predictable income. In the previous study, we focused on ”trail pheromone” system which enables robots to communicate one another [2]. In the present study, we analysed statistically the variance of the foraging behaviour of the robot swarm, using repeated (48 trials) computer simulation.We set the individual density as the parameter. The density in Figures, X axis means the number of individuals on the unit field(180 × 180 [cm]). In the simulation, we set 360 × 360[cm] as the field size. When the individual density is low, the variance of the foraging behaviour is larger than expected from the random behaviour(i.e., binomial distribution; Fig. 1). Horizontal lines in Fig. 1 mean expected one from the binomial distribution. As the density goes high, the variance is reduced toward the expected value.

Designing an algorithm for swarm behavior using the concept of Umwelt

In this study, we propose a methodology for designing a swarm behavior. The difficulty in designing the swarm behavior is a gap between the object of evaluation and that of design. The former is the performance of a group, but the latter is the action of each individual. We utilize the concept “Umwelt” in ethology for bridging the gap. The advantage of this concept is that all actions necessary for the swarm behavior can he derived from the purpose of each individual. Using this concept, the swarm behavior can he built into the action algorithm of the individuals. In order to evaluate the proposed method, we construct the swarm algorithm for a search and collection task. Using a computer simulation, we confirmed that the swarm successfully achieved the task with flexibility and parallelism, and also robustness in part. These results support the effectiveness of the proposed methodology.

Regulatory mechanism predates the evolution of ant-like swarm intelligence in simulated robots

The evolution of complexity is one of the prime features of life on Earth. Although well accepted as the product of adaptation, the dynamics underlying the evolutionary build-up of complex adaptive systems remains poorly resolved. Using simulated robot swarms that exhibit ant-like group foraging with trail pheromones, we show that their swarm intelligence paradoxically involves regulatory behavior that arises in advance. We focused on a “traffic rule” on their foraging trail as a regulatory trait. We allowed the simulated robot swarms to evolve pheromone responsiveness and behaviors simultaneously. In most cases, the traffic rule, initially arising as selectively neutral component behaviors, assisted the group foraging system to bypass a fitness valley caused by overcrowding on the trail. Our study reveals a hitherto underappreciated role of regulatory mechanisms in the origin of swarm intelligence, as well as highlights the importance of embodiment in the study of their evolution.