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1. Steering a Swarm of Particles Using Global Inputs and Swarm Statistics

   https://doi.org/10.1109/TRO.2017.2769094  

   Shahrokhi, Shiva; Lin, Lillian; Ertel, Chris; Wan, Mable; Becker, Aaron T. (January 2017, IEEE Transactions on Robotics)

   Microrobotics has the potential to revolutionize many applications including targeted material delivery, assembly, and surgery. The same properties that promise breakthrough solutions—small size and large populations—present unique challenges for controlling motion. Robotic manipulation usually assumes intelligent agents, not particle systems manipulated by a global signal. To identify the key parameters for particle manipulation, we used a collection of online games in which players steer swarms of up to 500 particles to complete manipulation challenges. We recorded statistics from more than 10 000 players. Inspired by techniques in which human operators performed well, we investigate controllers that use only the mean and variance of the swarm. We prove that mean position is controllable and provide conditions under which variance is controllable. We next derive automatic controllers for these and a hysteresis-based switching control to regulate the first two moments of the particle distribution. Finally, we employ these controllers as primitives for an object manipulation task and implement all controllers on 100 kilobots controlled by the direction of a global light source. 

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2. Coordinating Development of the SWARM-EX Formation Flying CubeSat Swarm Across Multiple Institutions

   Agarwal, R. (August 2021, 35th Annual Small Satellite Conference)

   The Space Weather Atmospheric Reconfigurable Multiscale Experiment (SWARM-EX) is a National Science Foundation (NSF) sponsored CubeSat mission distributed across six colleges and universities in the United States. The project has three primary goals: (1) contributing to aeronomy and space weather knowledge, (2) demonstrating novel engineering technology, and (3) advancing higher education. The scientific focus of SWARM-EX is to study the spatial and temporal variability of ion-neutral interactions in the equatorial Ionosphere-Thermosphere (I-T) region. Since the mission consists of three spacecraft operating in a swarm, SWARM-EX will take in-situ measurements of the neutral and ion composition on timescales of less than an orbital period to study the persistence and correlation between different phenomena in the I-T region. The engineering objectives of SWARM-EX are focused on advancing the state of the art in spacecraft formation flying. In addition to being the first passively safe, autonomous formation of more than two spacecraft, SWARM-EX will demonstrate several other key innovations. These include a novel hybrid propulsive/differential drag control scheme and the realization of a distributed aeronomy sensor. As a project selected by the NSF for its broader impacts as well as its intellectual merit, SWARM-EX aims to use CubeSat development as a vehicle for education. The six collaborating institutions have varying levels of CubeSat experience and involve students who range from first-year undergraduates to Ph.D. candidates. These differences in knowledge, as well as the distributed nature of the program, present a tremendous educational opportunity, but also raise challenges such as cross-institutional communication and coordination, document sharing and file management, and hardware development. By detailing its procedures for overcoming these challenges, the SWARM-EX team believes that it may serve as a case study for the coordination of a successful CubeSat program distributed across multiple institutions. 

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3. Measuring Group Personality with Swarm AI

   Rosenberg, Louis; Willcox, Gregg; Askay, David; Metcalf, Lynn; Kwong, Bryon; Liu, Richard (September 2019, TransAI 2019)

   The aggregation of individual personality tests to predict team performance is widely accepted in management theory but has significant limitations: the isolated nature of individual personality surveys fails to capture much of the team dynamics that drive real-world team performance. Artificial Swarm Intelligence (ASI), a technology that enables networked teams to think together in real-time and answer questions as a unified system, promises a solution to these limitations by enabling teams to take personality tests together, whereby the team uses ASI to converge upon answers that best represent the group’s disposition. In the present study, the group personality of 94 small teams was assessed by having teams take a standard Big Five Inventory (BFI) test both as individuals, and as a real-time system enabled by an ASI technology known as Swarm AI. The predictive accuracy of each personality assessment method was assessed by correlating the BFI personality traits to a range of real-world performance metrics. The results showed that assessments of personality generated using Swarm AI were far more predictive of team performance than the traditional survey-based method, showing a significant improvement in correlation with at least 25% of performance metrics, and in no case showing a significant decrease in predictive performance. This suggests that Swarm AI technology may be used as a highly effective team personality assessment tool that more accurately predicts future team performance than traditional survey approaches. 

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4. Beamforming-Constrained Swarm UAS Networking Routing

   https://doi.org/10.1109/TNSE.2020.3040311  

   Wang, Jian; Liu, Yongxin; Niu, Shuteng; Song, Houbing (November 2020, IEEE Transactions on Network Science and Engineering)

   null (Ed.)
5. Swarm Model Checking on the {GPU}

   https://doi.org/10.1007/978-3-030-30923-7_6  

   DeFrancisco, R.; Cho, S.; Ferdman, M.; Smolka, S. A. (October 2020, International journal on software tools for technology transfer)

   We present Grapple, a new and powerful framework for explicit-state model checking on GPUs. Grapple is based on swarm verification (SV), a model-checking technique wherein a collection or swarm of small, memory- and time-bounded verification tests (VTs) are run in parallel to perform state-space exploration. SV achieves high state-space coverage via diversification of the search strategies used by constituent VTs. Grapple represents a swarm implementation for the GPU. In particular, it runs a parallel swarm of internally-parallel VTs, which are implemented in a manner that specifically targets the GPU architecture and the SIMD parallelism its computing cores offer. Grapple also makes effective use of the GPU shared memory, eliminating costly inter-block communication overhead. We conducted a comprehensive performance analysis of Grapple focused on the various design parameters, including the size of the queue structure, implementation of guard statements, and nondeterministic exploration order. Tests are run with multiple hardware configurations, including on the Amazon cloud. Our results show that Grapple performs favorably compared to the SPIN swarm and a prior non-swarm GPU implementation. Although a recently debuted FPGA swarm is faster, the deployment process to the FPGA is much more complex than Grapple's. 

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