Design innovation of mesoscale robotic swarms: applications to cooperative urban sensing and mapping

Abstract

Development of mesoscale robots is gaining interest in security and surveillance domains due to their stealth and portable nature in achieving tasks. Their design and development require a host of hardware, controls, and behavioral innovations to yield fast, energy-efficient, distributed, adaptive, robust, and scalable systems. We extensively describe one such design and development process by: (1) the genealogy of our embedded platforms;(2) the key system architecture and functional layout; (3) the developed and implemented design principles for mesoscale robotic systems; (4) the various key algorithms developed for effective collective operations of mesoscale robotic swarms, with applications to urban sensing and mapping. This study includes our perception of the embedded hardware requirements for reliable operations of mesoscale robotic swarms and our description of the key innovations made in magnetic sensing, indoor localization, central pattern generator control, and distributed autonomy. Although some elements of the design process of such a complex robotic system are inevitably ad-hoc, we focus on the system-of-systems design process and the component design integration. This system-of-systems process provides a basis for developing future systems in the field, and the designs represent the state-of-the-art development that may be benchmarked against and adapted to other applications.

Beyond Bio-Inspired Robotics: How Multi-Robot Systems Can Support Research on Collective Animal Behavior

“…Currently, observations made by biologists often serve as the inspiration for the strategies developed by roboticists ( Dharmawan et al, 2019 ), while the reverse flow of ideas is significantly smaller. This one-way flow of ideas still has the potential to be extended to smaller scales—in relation with self-organization at the micro- or nano-scales by active matter, cells and bacteria—owing to recent progress in miniaturization.…”

“…The increased fidelity of robotic methods compared to that of simulations comes at the cost of longer iteration times when conducting collective behavior experiments with multi-robot systems, for which establishing quantitative and reproducible results can be challenging ( Dharmawan et al, 2019 ; Kit et al, 2019 ). In addition, experiments involving such multi-robot systems tend to happen on a smaller scale, with physical experiments utilizing a smaller number of agents compared to their virtual counterparts.…”