For the experimental validation, we have used a differential-drive robot developed in-house as a low-cost tool for robotics research (36). The platform is equipped with six infrared rangefinders, an inertial measurement unit (IMU), two wheel encoders, and two light sensors.

The robots are granted collective autonomy and distributed communication by attaching a “swarm-enabling” unit (14) composed of a single-board computer (SBC) and an XBee module (see Fig. 5) (37). This unit interfaces with the robot via Bluetooth and is responsible for implementing the cooperative control algorithms, communicating with other robots, and controlling the motion of the robot.

The SBC and XBee module attached to the robot provides autonomy and distributed communications to the unit, making it able to swarm and perform decentralized collective motion such as heading consensus. (Photo credit: David Mateo, Singapore University of Technology and Design.)

The units communicate via radio signals sent over the distributed, dynamic mesh established by the XBee modules. Because physical limitations impose a maximum range at which these signals can be sent, the swarm has a natural “metric” interaction model, meaning that each agent is able to communicate with any other agents within a given distance. Field experiments with aquatic autonomous surface vehicles using a similar setup for communication (15) have shown that, when the number of agents is large, the interaction model is also weakly density-dependent, deviating from a pure metric model. However, for the current experimental setup, there is no practical spatial limitation on communication between the agents, and instead, the different interaction networks are explored by tuning the cooperative control rule.

To control the robot, the SBC processes the data from the IMU and encoders through a Kálmán filter to have an accurate estimation of the platform’s location. Then, a proportional integral derivative (PID) controller allows it to adjust the trajectory of the robot. The PID coefficients are tuned using the Ziegler-Nichols frequency response method (33).

The behavior of the robot, defined by the cooperative control algorithm, is implemented in the swarm-enabling unit using the in-house marabunta package (14). The software is designed prioritizing platform-agnostic development of cooperative control rules, portability, and a simple workflow to facilitate fast prototyping. In the study of collective motion, these behavior rules typically take the form of simple, local, iterative algorithms, where the velocity of an agent is defined in terms of its own state, the local environment, and the state of the agents in a certain neighborhood.

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