Helen McCreery, Georgina Gemayal, Ana Pais, Simon Garnier, and Radhika Nagpal. 2022. “Hysteresis stabilizes dynamic control of self-assembled army ant constructions.” Nature Communications, 13, 1660. Open Access LinkAbstract
Biological systems must adjust to changing external conditions, and their resilience depends on their control mechanisms. How is dynamic control implemented in noisy, decentralized systems? Army ants’ self-assembled bridges are built on unstable features, like leaves, which frequently move. Using field experiments and simulations, we characterize the bridges’ response as the gaps they span change in size, identify the control mechanism, and explore how this emerges from individuals’ decisions. For a given gap size, bridges were larger after the gap increased rather than decreased. This hysteresis was best explained by an accumulator model, in which individual decisions to join or leave a bridge depend on the difference between its current and equilibrium state.This produces robust collective structures that adjust to lasting perturbations while ignoring small, momentary shifts. Our field data support separate joining and leaving cues; joining is prompted by high bridge performance and leaving by an excess of ants. This leads to stabilizing hysteresis, an important feature of many biological and engineered systems.
Bahar Haghighat, Johannes Boghaert, Zev Minsky-Primus, Julia Ebert, Fanghzheng Liu, Martin Nisser13th International Conference Swarm Intelligence (Swarm on robotics and swarm, Ariel Ekblaw, and Radhika Nagpal. 2022. “An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots.” In Intl. Conf. on Swarm Intelligence (ANTS).
Julia Ebert. 2022. “Distributed Decision-making Algorithms for Inspection by Autonomous Robot Collectives.” PhD Thesis, School of Engineering and Applied Sciences (CS), Harvard University.
Florian Berlinger, Melvin Gauci, and Radhika Nagpal. 2021. “Implicit coordination for 3D underwater collective behaviors in a fish-inspired robot swarm.” Science Robotics, 6, 50.Abstract
Many fish species gather by the thousands and swim in harmony with seemingly no effort. Large schools display a range of impressive collective behaviors, from simple shoaling to collective migration and from basic predator evasion to dynamic maneuvers such as bait balls and flash expansion. A wealth of experimental and theoretical work has shown that these complex three-dimensional (3D) behaviors can arise from visual observations of nearby neighbors, without explicit communication. By contrast, most underwater robot collectives rely on centralized, above-water, explicit communication and, as a result, exhibit limited coordination complexity. Here, we demonstrate 3D collective behaviors with a swarm of fish-inspired miniature underwater robots that use only implicit communication mediated through the production and sensing of blue light. We show that complex and dynamic 3D collective behaviors—synchrony, dispersion/aggregation, dynamic circle formation, and search-capture—can be achieved by sensing minimal, noisy impressions of neighbors, without any centralized intervention. Our results provide insights into the power of implicit coordination and are of interest for future underwater robots that display collective capabilities on par with fish schools for applications such as environmental monitoring and search in coral reefs and coastal environments.
Cover Article -- Movies -- Focus and Science news.
Florian Berlinger, Paula Wulkop, and Radhika Nagpal. 2021. “Self-Organized Evasive Fountain Maneuvers with a Bio-inspired Underwater Robot Collective.” In Intl. Conference on Automation and Robotics (ICRA).Abstract

Several animal species self-organize into large groups to leverage vital behaviors such as foraging, construc- tion, or predator evasion. With the advancement of robotics and automation, engineered multi-agent systems have been inspired to achieve similarly high degrees of scalable, robust, and adaptable autonomy through decentralized and dynamic coordination. So far however, they have been most successfully demonstrated above ground or with partial assistance from central controllers and external tracking. Here we demonstrate an underwater robot collective that realizes full spatiotempo- ral coordination. Using the example of fish-inspired evasive maneuvers, our robots display alignment, formation control, and coordinated escape, enabled by real-time on-board multi- robot tracking and local decision making. Accompanied by a custom simulator, this robotic platform advances the physically- validated development of algorithms for collective behaviors and future applications including collective exploration, track- ing and capture, or environmental sampling.

(finalist for best paper award)
Florian Berlinger. 2021. “Blueswarm: 3D Self-organization in a Fish-inspired Robot Swarm.” PhD Thesis, School of Engineering and Applied Sciences (CS), Harvard University. phdthesis2021berlinger.pdf
Florian Berlinger, Mehdi Saadat, Hossein Haj-Hariri, George V Lauder, and Radhika Nagpal. 2021. “Fish-like three-dimensional swimming with an autonomous, multi-fin, and biomimetic robot.” Bioinspiration & Biomimetics, 16, 2. Publishers VersionAbstract
Fish migrate across considerable distances and exhibit remarkable agility to avoid predators and feed. Fish swimming performance and maneuverability remain unparalleled when compared to robotic systems, partly because previous work has focused on robots and flapping foil systems that are either big and complex, or tethered to external actuators and power sources. By contrast, we present a robot – the Finbot – that combines high degrees of autonomy, maneuverability, and biomimicry with miniature size (160 cm3). Thus, it is well-suited for controlled three-dimensional experiments on fish swimming in confined laboratory test beds. Finbot uses four independently controllable fins and sensory feedback for precise closed-loop underwater locomotion. Different caudal fins can be attached magnetically to reconfigure Finbot for swimming at top speed (122 mm/s ≡ 1 BL/s) or minimal cost of transport (CoT = 8.2) at Strouhal numbers as low as 0.53. We conducted more than 150 experiments with 12 different caudal fins to measure three key characteristics of swimming fish: (i) linear speed-frequency relationships, (ii) U-shaped costs of transport, and (iii) reverse Kármán wakes (visualized with particle image velocimetry). More fish-like wakes appeared where the cost of transport was low. By replicating autonomous multi-fin fish-like swimming, Finbot narrows the gap between fish and fish-like robots and can address open questions in aquatic locomotion, such as optimized propulsion for new fish robots, or the hydrodynamic principles governing the energy savings in fish schools.
Mehdi Saadat, Florian Berlinger, Artan Sheshmani, Radhika Nagpal, George V Lauder, and Hossein Haj-Hariri. 2021. “Hydrodynamic advantages of in-line schooling.” Bioinspiration & Biomimetics, 16, 4. Publisher's VersionAbstract

Fish benefit energetically when swimming in groups, which is reflected in lower tail-beat frequencies for maintaining a given speed. Recent studies further show that fish save the most energy when swimming behind their neighbor such that both the leader and the follower benefit. However, the mechanisms underlying such hydrodynamic advantages have thus far not been established conclusively. The long-standing drafting hypothesis—reduction of drag forces by judicious positioning in regions of reduced oncoming flow–fails to explain advantages of in-line schooling described in this work. We present an alternate hypothesis for the hydrodynamic benefits of in-line swimming based on enhancement of propulsive thrust. Specifically, we show that an idealized school consisting of in-line pitching foils gains hydrodynamic benefits via two mechanisms that are rooted in the undulatory jet leaving the leading foil and impinging on the trailing foil: (i) leading-edge suction on the trailer foil, and (ii) added-mass push on the leader foil. Our results demonstrate that the savings in power can reach as high as 70% for a school swimming in a compact arrangement. Informed by these findings, we designed a modification of the tail propulsor that yielded power savings of up to 56% in a self-propelled autonomous swimming robot. Our findings provide insights into hydrodynamic advantages of fish schooling, and also enable bioinspired designs for significantly more efficient propulsion systems that can harvest some of their energy left in the flow.

Nicole Carey, Paul Bardunias, Radhika Nagpal, and Justin Werfel. 2021. “Validating a termite-inspired construction coordination mechanism using an autonomous robot.” Frontiers in Robotics and AI.
Julia Ebert, Melvin Gauci, Frederick Mallmann-Trenn, and Radhika Nagpal. 2020. “Bayes Bots: Collective Bayesian Decision-Making in Decentralized Robot Swarms.” Intl. Conference on Robotics and Automation (ICRA). icra2020-ebert.pdf
Melinda Malley, Bahar Haghighat, Lucie Houel, and Radhika Nagpal. 2020. “Eciton robotica: Design and Algorithms for an Adaptive Self-Assembling Soft Robot Collective.” Intl. Conferenc on Robotics and Automation (ICRA). icra2020-malley.pdf
Paul Bardunias, Daniel Calovi, Nicole Carey, Rupert Soar, J. Scott Turner, Radhika Nagpal, and Justin Werfel. 2020. “The extension of internal humidity levels beyond the soil surface facilitates mound expansion in Macrotermes.” Proceedings of the Royal Society B: Biological Science, 287, 1930.Abstract
Termites in the genus Macrotermes construct large-scale soil mounds above their nests. The classic explanation for how termites coordinate their labour to build the mound, based on a putative cement pheromone, has recently been called into question. Here, we present evidence for an alternate interpretation based on sensing humidity. The high humidity characteristic of the mound's internal environment extends a short distance into the low-humidity external world, in a ‘bubble’ that can be disrupted by external factors like wind. Termites transport more soil mass into on-mound reservoirs when shielded from water loss through evaporation, and into experimental arenas when relative humidity is held at a high value. These results suggest that the interface between internal and external conditions may serve as a template for mound expansion, with workers moving freely within a zone of high humidity and depositing soil at its edge. Such deposition of additional moist soil will increase local humidity, in a feedback loop allowing the ‘interior’ zone to progress further outward and lead to mound expansion.
Mihai Duduta, Florian Berlinger, Radhika Nagpal, David Clarke, Rob Wood, and Zeynep Temel. 2020. “Tunable Multi-Modal Locomotion in Soft Dielectric Elastomer Robots.” IEEE Robotics and Automation Letters (RAL). ral2020_duduta.pdf
Melinda Malley. 2020. “Army Ant Inspired Adaptive Self-Assembly with Soft Climbing Robots.” PhD Thesis, School of Engineering and Apllied Sciences (Mech. Eng), Harvard University. phdthesis2020malley.pdf
Daniel Calovi, Paul Bardunias, Nicole Carey, J. Scott Turner, Radhika Nagpal, and Justin Werfel. 2019. “Surface curvature guides early construction activity in mound-building termites.” Philosophical Transactions of the Royal Society , 374, 1774. Publisher's VersionAbstract

Termite colonies construct towering, complex mounds, in a classic example of distributed agents coordinating their activity via interaction with a shared environment. The traditional explanation for how this coordination occurs focuses on the idea of a ‘cement pheromone’, a chemical signal left with deposited soil that triggers further deposition. Recent research has called this idea into question, pointing to a more complicated behavioural response to cues perceived with multiple senses. In this work, we explored the role of topological cues in affecting early construction activity in Macrotermes. We created artificial surfaces with a known range of curvatures, coated them with nest soil, placed groups of major workers on them and evaluated soil displacement as a function of location at the end of 1 h. Each point on the surface has a given curvature, inclination and absolute height; to disambiguate these factors, we conducted experiments with the surface in different orientations. Soil displacement activity is consistently correlated with surface curvature, and not with inclination nor height. Early exploration activity is also correlated with curvature, to a lesser degree. Topographical cues provide a long-term physical memory of building activity in a manner that ephemeral pheromone labelling cannot. Elucidating the roles of these and other cues for group coordination may help provide organizing principles for swarm robotics and other artificial systems.

This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.

Helen McCreery, Jenna Bilek, Radhika Nagpal, and Michael Breed. 2019. “Effects of load mass and size on cooperative transport in ants over multiple transport challenges.” Journal of Experimental Biology, doi: 10.1242/jeb.206821. Publisher's Version (open access)Abstract
Some ant species cooperatively transport a wide range of extremely large, heavy food objects of various shapes and materials. While previous studies have examined how object mass and size affect the recruitment of additional workers, less is understood about how these attributes affect the rest of the transport process. Using artificial baits with independently varying mass and size, we reveal their effects on cooperative transport in Paratrechina longicornis across two transport challenges: movement initiation and obstacle navigation. As expected, object mass was tightly correlated with number of porters as workers adjust group size to the task. Mass affected performance similarly across the two challenges, with groups carrying heavy objects having lower performance. Yet, object size had differing effects depending on the challenge. While larger objects led to reduced performance during movement initiation – groups took longer to start moving these objects and had lower velocities – there was no evidence for this during obstacle navigation, and the opposite pattern was weakly supported. If a group struggles to start moving an object, it does not necessarily predict difficulty navigating around obstacles; groups should persist in trying to move ‘difficult’ objects, which may be easier to transport later in the process. Additionally, groups hitting obstacles were not substantially disrupted, and started moving again sooner than at the start, despite the nest direction being blocked. Paratrechina longicornis transport groups never failed, performing well at both challenges while carrying widely varying objects, and even transported a bait weighing 1900 times the mass of an individual.
Mihai Duduta, Florian Berlinger, Radhika Nagpal, David Clarke, Robert Wood, and Zeynep Temel. 2019. “Electrically-latched compliant jumping mechanism based on a dielectric elastomer actuator.” Smart Materials and Structures , 28, 09LT01. duduta_sms_letter_2019.pdf
Lucie Houel. 2019. “Self-assembly of soft-robots in simulation inspired by army ant bridge behavior.” EPFL Master's Thesis.
Katherine Binney. 2019. “Teach a Fish to Swim: Evaluating the Ability of Turing Learning to Infer Schooling Behavior.” Senior Thesis, Harvard University.Abstract

Turing Learning is a promising evolutionary design method for swarm robotics that uses ob- servation of natural or artificial systems to infer controllers for agents in a swarm. However, Tur- ing Learning has thus far only been used to infer very simple swarm behaviors. In this work, we expand Turing Learning to infer dispersion, a much more complex swarm behavior, by a simulated school of robotic fish. Turing Learning depends on the co-evolution of replicas and classifiers. Replicas mimic ideal behavior and classifiers distinguish between data samples from replica and ideal agents. We model replicas and classifiers with neural networks and investigate the architecture of each component independently in order to determine needed modifications to Turing Learning for it to infer fish schooling. We find that previously formulated data samples led to the inference of behaviors that locally mimicked the agent trajectories in dispersion, yet poorly mimicked dispersion of an entire swarm. We present three alternative data samples that consider the spatial arrangement of agents in a swarm. We also introduce three new classifier fitness func- tions that accelerate evolution of high-accuracy classifiers. We find in a preliminary trial that using one of our data samples (metrics) and classifier fitness functions (foutputs) enables the successful inference of dispersion via Turing Learning.

Katherina Soltan, Jamie O'Brien, Florian Berlinger, Radhika Nagpal, and Jeff Dusek. 2018. “A Biomimetic Actuation Method for a Miniature, Low-Cost Multi-jointed Robotic Fish.” In IEEE OCEANS conference. oceans2018soltan.pdf