SIN: A Programmable Platform for Swarm Robotics

Ali Foroutannia Milad Shoryabi Amirali Alizadeh Anaraki Intelligent Systems & Robotics Lab Intelligent Systems & Robotics Lab Intelligent Systems & Robotics Lab Department of Electrical Engineering Department of Electrical Engineering Department of Electrical Engineering University of Neyshabur University of Neyshabur University of Neyshabur Neyshabur, Iran Neyshabur, Iran Neyshabur, Iran [email protected] [email protected] [email protected]

Alireza Rowhanimanesh Intelligent Systems & Robotics Lab Department of Electrical Engineering University of Neyshabur Neyshabur, Iran [email protected]

Abstract— Swarm robotics is an inspiration from nature visual display. The latest version of this platform has been and incorporates swarm intelligence to help collective robotics. widely employed in various educational and research This recent technology is usually characterized by a swarm of purposes [32, 33]. AMiR designed at Putra Malaysia simple, low-cost, and small robots instead of a complicated and University, 6.5 cm in size, is a low-cost but robust and expensive robot. Designing optimal and reliable swarm mobile platform for swarm robotics [34]. Kilobot was intelligence algorithms require real-world test environments. developed at Harvard University, is another applied As a practical solution, physical platforms can efficiently platform with capabilities such as grouping and group address this issue. In this paper, a programmable physical planning. Due to its simplicity and low energy consumption, platform, called SIN, is introduced for swarm robotics. Kilobot can work up to 24 hours without any need for Different design parameters such as communication range, battery recharging [35, 36]. ARGoS is another platform signaling pattern, types of sensors and actuators, cooperation rules, and degree of uncertainty and noise can be simply designed and simulated by Marco Dorigo's research team adjusted by user. The building blocks of each agent has been [37-40]. Also, Elisa-III is suitable for realizing distributed developed in a modular form to improve the hardware algorithms [41]. Bristle-bots is an efficient swarm micro- flexibility. To illustrate the efficiency of the proposed platform, robot with 8-bit microprocessors for motorized motion and a cooperative multi-robot target tracking problem is data transmission [42]. implemented on this platform as a case study, where the robots One of the well-known applications of swarm robotics is interacts by artificial attraction-repulsion forces based on nanorobotics, in which each robot is a straightforward short-range and noisy optical communication. The results demonstrate how the details of swarm behaviors such as nanoscale agent with limited capabilities. Hence, effective decentralized aggregation and collective target tracking can be cooperation among these agents through swarm intelligence successfully implemented on the proposed platform. is a vital need. Previous research on the application of swarm intelligence in nanorobotics has focused significantly Keywords— Swarm Robotics, Programmable Platform, on nanomedicine [43-48]. Designing optimal and reliable Swarm Aggregation, Cooperative Target Tracking. swarm intelligence algorithms require testing of the performance of the algorithm on a real swarm. Testing at I. INTRODUCTION microscopic scales is challenging and expensive. Also, Swarm Robotics is a synergic combination of two applied simulating real-world noise and uncertainty in computer research areas, swarm intelligence and robotics [1-5]. There software is hard. As a practical approach, a macroscopic exist some emerging behaviors that are not seen in individual physical platform can efficiently facilitate the optimal robots while they are observed in the presence of collective design. intelligence among robots. Flexibility, simplicity, In this paper, a programmable physical platform, called parallelism, scalability, sustainability, cost-effectiveness, SIN, is introduced for swarm robotics. Since all components energy efficiency, and robustness are crucial features of of agents are modular and programmable and can be easily swarm robotics [2, 6-14]. Swarm intelligence, inspired from activated or deactivated by user, different swarm nature, enables swarm robotics to deal with complex tasks intelligence algorithms and patterns of communication and and makes it attractive for different applications [11, 15-24]. cooperation in the swarm can be implemented on this Some of the previous works on this promising approach are flexible open-source platform. To illustrate the efficiency of reviewed [25-27]. the proposed platform, a cooperative multi-robot target A team at EPFL University designed Alice, 2.2-cm-wide tracking problem is implemented on this platform as a case swarm robots that have been used in many research study [45], where the robots interacts by artificial attraction- applications [28]. Another group at the University of repulsion forces based on short-range and noisy optical Stuttgart developed swarm robots called Jasmine, with a communication. size of 3 cm as an open-source swarm robotic platform This paper is organized as follows. In Section 2, a [29]. Another project was done at Lincoln University, mathematical model is presented for the case study of this entitled Colias swarm robots [30, 31]. Mona swarm robots paper. Then, this model is used for computer simulation in designed at Manchester University, 6.5 cm in size, is an Section 3. The hardware structure of each robot is explained open-source modular platform which allows the use of in Section 4. The details of real-world implementation and additional plugins such as wireless communications or a test results are demonstrated in Section 5. Finally, Section 6

XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE concludes the paper. be used [55].

II. MATHEMATICAL MODELING In this section, a mathematical model is presented for the case study of this paper [45]. The goal of the swarm is decentralized aggregation and cooperative target tracking in the presence of noise and uncertainty. The locomotion of robots is based on simple attraction/repulsion forces among agents and targets. The individual and collective behavior of agents is inspired by the Brownian motion of real microscopic biological agents. The following equations describe the governing dynamics of a swarm of N individuals:

(1)

Fig. 2. The velocity of each agent is a function of the distance-dependent In this equation, and are forces applied to it from the target and other agents. the position and velocity of robot 푖 in the n-dimensional III. COMPUTER SIMULATION space, xt is the position of the target that can move In this section, the performance of swarm formation and independently in the space, dt,i is the Euclidian distance between target and robot i, f (.):RR is a distance- target tracking is demonstrated through computer simulation t,i in MATLAB. Without loss of generality, simulation is dependent attraction force applied from target to robot i, dj,i is the Euclidian distance between robot j and robot i, performed in a two-dimensional space in the presence of f (.):RR is a distance-dependent attraction-repulsion force noise and uncertainty. The shape and structure of robots are j,i identical. The robots (blue circles) and target (red circle) are applied from robot j to robot i, and fn is a bio-inspired white noise. According to this equation and as shown in Fig.2, the initially positioned randomly. Fig.3 shows the temporal and velocity of each agent is a function of the forces applied to it spatial profiles of the swarm dynamics in six time-points. In from the target and other agents, and these forces are very Fig.4, the simulation is repeated from four distinguishing dependent on the distances between them. random initial points. As expected from the mathematical model of Section 2, in all cases, the robots could robustly The patterns of these forces play a crucial role in ensuring converge to the target. The simulation environment utilizes swarm stability, swarm aggregation, and target tracking [49- a user-friendly graphical user interface, depicted in Fig.5, 52]. In this paper, the following patterns are considered: such that the user can easily adjust all simulation parameters. (2)

(3) where a, b, c, A, B are positive constant parameters, and b > a. As shown in Fig.1, the value of fj,i is zero in the equilibrium distance. Also, it is repulsive if the distance between the two robots is less than the equilibrium distance. Conversely, if this distance is more than the equilibrium distance, the force will be attractive [53].

Fig. 3. The spatial and temporal profiles of the swarm at 6 time-points in computer simulation.

Fig. 1. The pattern of a noisy distance-dependent attraction-repulsion force used in this paper [54] It should be noted that a parameter has been defined as a neighborhood threshold in the simulator environment. It means that the robots have a short communication range, inspired from nature. If the distance between some robots becomes more than the threshold, no communication will be possible among them. In order to ensure fast swarm formation and target tracking, higher number of robots can Fig. 4. The final spatial profiles of the swarm in 4 different random runs Fig.7 shows the details of the hardware structure of each agent in the proposed swarm platform, which is a simple robot with a 20 cm diameter. All embedded sensors, actuators, processing units, and communication modules are represented in this figure. The mechanical structure of robots has been designed in SolidWorks. Inspired from nature, the default type of sensors and communication modules are assumed to be optical to be simple, short-range, and noisy. Fig. 8 depicts a real sample signal recorded from the optical communication between agents during swarm aggregation and target tracking. Due to the high flexibility of the proposed platform, other types of components can be Fig. 5. Graphical User Interface. easily activated by user.

IV. HARDWARE STRUCTURE Fig. 6 illustrates the functional architecture of each agent. All components, including sensors, actuators, and communication modules, have been designed in a modular manner. Thus, they can be independently activated or deactivated by user. Various types of transceivers have been embedded in the robots to realize different types of communication in the swarm, including ultrasonic and optical. Each robot has a Wi-Fi transceiver that enables it to communicate with server for monitoring and data logging. Furthermore, users can employ this interface to reprogram the swarm and upload specific swarm intelligence algorithms to the processing unit of robots.

Fig. 7. The Hardware Structure of Each Agent.

Fig. 6. The Functional Architecture of Each Agent. Fig. 8. Optical Communication among Agents.

Fig. 9. The spatial and temporal profiles of the swarm at 8-time points in practical tests. V. IMPLEMENTATION [6] C. Blum and X. Li, "Swarm intelligence in optimization," in Swarm Intelligence: Springer, 2008, pp. 43-85. Fig.9 illustrates a real-world implementation of the case [7] M. Dorigo and M. Birattari, "Swarm intelligence," Scholarpedia, vol. study of this paper on the proposed swarm robotic platform 2, no. 9, p. 1462, 2007. in 1.8m × 1.8m environment. This platform has been [8] S. Duan, J. Mao, J. Li, and L. Fu, "Design implementation and designed in Intelligent Systems and Robotics Laboratory of application of swarm intelligence algorithm optimization function University of Neyshabur from 2015 to 2018. Also, this simulation platform," in Software Engineering and Information project was selected and awarded in many national festivals Technology: Proceedings of the 2015 International Conference on Software Engineering and Information Technology (SEIT2015), 2016: [45]. 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