Radhika Nagpal

Living architectures, such as beehives and ant bridges, adapt continuously to their environments through self-organization of swarming agents. In contrast, most human-made architecture remains static, unable to respond to changing climates or occupant needs. Despite advances in biomimicry within architecture, architectural systems still lack the self-organizing dynamics found in natural swarms. In this work, we introduce the concept of architectural swarms: systems that integrate swarm intelligence and robotics into modular architectural façades to enable responsiveness to environmental conditions and human preferences. We present the Swarm Garden, a proof of concept composed of robotic modules called SGbots. Each SGbot features buckling-sheet actuation, sensing, computation, and wireless communication. SGbots can be networked into reconfigurable spatial systems that exhibit collective behavior, forming a testbed for exploring architectural swarm applications. We demonstrate two application case studies. The first explores adaptive shading using self-organization, where SGbots respond to sunlight using a swarm controller based on opinion dynamics. In a 16-SGbot deployment on an office window, the system adapted effectively to sunlight, showing robustness to sensor failures and different climates. Simulations demonstrated scalability and tunability in larger spaces. The second study explores creative expression in interior design, with 36 SGbots responding to human interaction during a public exhibition, including a live dance performance mediated by a wearable device. Results show that the system was engaging and visually compelling, with 96% positive attendee sentiments. The Swarm Garden exemplifies how architectural swarms can transform the built environment, enabling "living-like" architecture for functional and creative applications.

Living architectures, such as beehives and ant bridges, adapt continuously to their environments through self-organization of swarming agents. In contrast, most human-made architecture remains static, unable to respond to changing climates or occupant needs. Despite advances in biomimicry within architecture, architectural systems still lack the self-organizing dynamics found in natural swarms. In this work, we introduce the concept of architectural swarms; systems that integrate swarm intelligence and robotics into modular architectural façades to enable responsiveness to environmental conditions and human preferences. We present the Swarm Garden, a proof-of-concept composed of robotic modules called SGbots. Each SGbot features a buckling-sheet actuation, sensing, computation, and wireless communication. SGbots can be networked into reconfigurable spatial systems that exhibit collective behavior, forming a testbed for exploring architectural swarm applications. We demonstrate two application case studies. The first explores adaptive shading using self-organization, where SGbots respond to sunlight using a swarm controller based on opinion dynamics. In a 16-SGbot deployment on an office window, the system adapted effectively to sunlight, showing robustness to sensor failures and different climates. Simulations demonstrated scalability and tunability in larger spaces. The second study explores creative expression in interior design, with 36 SGbots responding to human interaction during a public exhibition, including a live dance performance mediated by a wearable device. Results show the system was engaging and visually compelling, with 96% positive attendee sentiments. The Swarm Garden exemplifies how architectural swarms can transform the built environment, enabling "living-like" architecture for functional and creative applications.

Living architectures, such as beehives and ant bridges, adapt continuously to their environments through self-organization of swarming agents. In contrast, most human-made architecture remains static, unable to respond to changing climates or occupant needs. Despite advances in biomimicry within architecture, architectural systems still lack the self-organizing dynamics found in natural swarms. In this work, we introduce the concept of architectural swarms; systems that integrate swarm intelligence and robotics into modular architectural façades to enable responsiveness to environmental conditions and human preferences. We present the Swarm Garden, a proof-of-concept composed of robotic modules called SGbots. Each SGbot features a buckling-sheet actuation, sensing, computation, and wireless communication. SGbots can be networked into reconfigurable spatial systems that exhibit collective behavior, forming a testbed for exploring architectural swarm applications. We demonstrate two application case studies. The first explores adaptive shading using self-organization, where SGbots respond to sunlight using a swarm controller based on opinion dynamics. In a 16-SGbot deployment on an office window, the system adapted effectively to sunlight, showing robustness to sensor failures and different climates. Simulations demonstrated scalability and tunability in larger spaces. The second study explores creative expression in interior design, with 36 SGbots responding to human interaction during a public exhibition, including a live dance performance mediated by a wearable device. Results show the system was engaging and visually compelling, with 96% positive attendee sentiments. The Swarm Garden exemplifies how architectural swarms can transform the built environment, enabling "living-like" architecture for functional and creative applications.

Living architectures, such as beehives and ant bridges, adapt continuously to their environments through self-organization of swarming agents. In contrast, most human-made architecture remains static, unable to respond to changing climates or occupant needs. Despite advances in biomimicry within architecture, architectural systems still lack the self-organizing dynamics found in natural swarms. In this work, we introduce the concept of architectural swarms; systems that integrate swarm intelligence and robotics into modular architectural façades to enable responsiveness to environmental conditions and human preferences. We present the Swarm Garden, a proof-of-concept composed of robotic modules called SGbots. Each SGbot features a buckling-sheet actuation, sensing, computation, and wireless communication. SGbots can be networked into reconfigurable spatial systems that exhibit collective behavior, forming a testbed for exploring architectural swarm applications. We demonstrate two application case studies. The first explores adaptive shading using self-organization, where SGbots respond to sunlight using a swarm controller based on opinion dynamics. In a 16-SGbot deployment on an office window, the system adapted effectively to sunlight, showing robustness to sensor failures and different climates. Simulations demonstrated scalability and tunability in larger spaces. The second study explores creative expression in interior design, with 36 SGbots responding to human interaction during a public exhibition, including a live dance performance mediated by a wearable device. Results show the system was engaging and visually compelling, with 96% positive attendee sentiments. The Swarm Garden exemplifies how architectural swarms can transform the built environment, enabling "living-like" architecture for functional and creative applications.

As marine ecosystems face rapid declines, field observations have become essential for better understanding our oceans. Fish-inspired robots are a promising solution, as they are less disruptive than propeller-based approaches in sensitive environments. However, in both fish and fish-inspired robots, there is a trade-off between speed (that favors rigid bodies) and maneuverability (that favors flexible bodies). In this work, we present BlueKoi, an untethered, fish-inspired robotic platform that leverages both a stiff tuna-inspired tail for efficient swimming and a koi-inspired rotating head for maneuvering, reaching speeds of 1.84 body lengths per second and a turn radius of 1.93 body lengths. We experimentally quantify the robot’s turn radius under varying conditions and develop a reduced-order model to both understand the turning behavior and inform future design decisions, without needing explicit measurements of hydrodynamic coefficients. Furthermore, we show that our model is not only accurate but also capable of extending simulations to account for future design modifications. By decoupling propulsion and maneuver-ability, BlueKoi is a scalable and modular platform that enables adaptability for diverse sensing and navigation needs.

Aquatic ecosystems vital to biodiversity and climate change-such as coral reefs, kelp forests, and mangrove forests-are often cluttered with natural obstacles. To navigate these complex habitats, fish have evolved relatively small body sizes and outstanding maneuverability. In contrast, most unmanned underwater vehicles currently deployed for ocean monitoring are bulky and slow, limiting their ability to access these environments. Developing small and agile underwater robots that mimic native fish species provides a unique opportunity for automated sampling of dynamic aquatic ecosystems. In this paper, we present BlueGuppy, a miniature, low-cost, and untethered fish-like robot (9.5×2.4×3.0cm, 33.1 g) capable of maneuvering with a single actuator. It achieves swimming speeds of up to 2.8 body lengths per second and can execute tight turns with small circles 1.4 body lengths in radius. BlueGuppy can generate a net thrust even in the presence of an incoming flow, but the flow field around BlueGuppy only mirrors that of biological organisms when it is free-swimming, underscoring the importance of untethered robots for biomimetic research. We explored the maneuverability of BlueGuppy by tuning its kinematics. By varying its flapping frequencies and temporal bias, BlueGuppy can access a wide range of speeds and turning curvatures. The combination of speed, maneuverability, and simplicity establishes BlueGuppy as a unique platform in the literature with tremendous potential for both uncovering the biomechanics of schooling fish and advancing the state-of-the-art in autonomous ocean sampling.

The coordinated movement of fish schools has long captivated researchers studying animal collective behavior. Classical literature from Weihs and Lighthill suggests that fish schools should favor planar diamond formations to increase hydrodynamic efficiency, inspiring a large body of work ranging from fluid simulations to hydrofoil experiments. However, whether fish schools actually adopt and maintain this idealized formation remains debated and unresolved. When fish schools are free to self-organize in three dimensions, what formations do they prefer? By tracking polarized schools of giant danios (Devario aequipinnatus) swimming continuously for ten hours, we demonstrate that fish rarely stay in a horizontal plane, and even more rarely, in the classical diamond formation. Of all fish pairs within four body-lengths from each other, only 25.2% were in the same plane. Of these, 54.6% were inline, 30.0% were staggered, and 15.4% were side-by-side. The diamond formation was observed in less than 0.1% of all frames. Notably, a vertical "ladder formation" emerged as the most probable formation for schooling giant danios, appearing in 79% of all fish pairs, and it elongated at higher swimming speeds. These findings highlight the dynamic and three-dimensional nature of fish schools and suggest that hydrodynamic benefits may be obtained without maintaining fixed formations. This research provides a foundation for future studies that examine the hydrodynamics and control of underwater collectives in 3D formations.

Robot swarms offer significant potential for inspecting diverse infrastructure, ranging from bridges to space stations. However, effective inspection requires accurate robot localization, which demands substantial computational resources and limits productivity. Inspired by biological systems, we introduce a novel cooperative localization mechanism that minimizes collective computation expenditure through self-organized sacrifice. Here, a few agents bear the computational burden of localization; through local interactions, they improve the inspection productivity of the swarm. Our approach adaptively maximizes inspection productivity for unconstrained trajectories in dynamic interaction and environmental settings. We demonstrate the optimality and robustness using mean-field analytical models, multi-agent simulations, and hardware experiments with metal climbing robots inspecting a 3D cylinder.

The schooling behavior of fish is hypothesized to confer many survival benefits, including foraging success, safety from predators, and energy savings through hydrodynamic interactions when swimming in formation. Underwater robot collectives may be able to achieve similar benefits in future applications, e.g. using formation control to achieve efficient spatial sampling for environmental monitoring. Although many theoretical algorithms exist for multi-robot formation control, they have not been tested in the underwater domain due to the fundamental challenges in underwater communication. Here we introduce a leader-follower strategy for underwater formation control that allows us to realize complex 3D formations, using purely vision-based perception and a reactive control algorithm that is low computation. We use a physical platform, BlueSwarm, to demonstrate for the first time an experimental realization of inline, side-by-side, and staggered swimming 3D formations. More complex formations are studied in a physics-based simulator, providing new insights into the convergence and stability of formations given underwater inertial/drag conditions. Our findings lay the groundwork for future applications of underwater robot swarms in aquatic environments with minimal communication.

Abstract The coordinated movement of fish schools has long captivated researchers studying animal collective behavior. Classical literature from Weihs and Lighthill suggests that fish schools should favor planar diamond formations to increase hydrodynamic efficiency, inspiring a large body of work ranging from fluid simulations to hydrofoil experiments. However, whether fish schools actually adopt and maintain this idealized formation remains debated and unresolved. When fish schools are free to self-organize in three dimensions, what formations do they prefer? By tracking polarized schools of giant danios ( Devario aequipinnatus ) swimming continuously for ten hours, we demonstrate that fish rarely stay in a horizontal plane, and even more rarely, in the classical diamond formation. Of all fish pairs within four body-lengths from each other, only 25.2% are in the same plane. Of these, 54.6% are inline, 30.0% are staggered, and 15.4% are side-by-side. The diamond formation was observed in less than 0.1% of all frames. Notably, a “ladder formation” emerged as the most probable formation for schooling giant danios, appearing in 79% of all fish pairs and fish schools elongate at higher swimming speeds. These findings highlight the dynamic and three-dimensional nature of fish schools and suggest that hydrodynamic benefits may be obtained without requiring fixed positions. This research provides a foundation for future studies that examine the hydrodynamics and control of underwater collectives in 3D formations.

Abstract In this work computational models of Bluebots, bio-inspired swimming robots that demonstrate 3D maneuverability and collective behaviors, are developed. Flexibility is prescribed to the caudal fins (CF) using a virtual skeleton. The hydrodynamic interactions occurring within in-line arrangements of these Bluebots is investigated by altering the flexion angle of the leader Bluebot caudal fin and a balance between optimizing leader Bluebot (LB) performance and follower Bluebot (FB) wake interaction is identified. Compared to the rigid CF baseline, optimal CF flexion for the thrust of LB leads to higher negative pressure within generated vortex structures and narrowing of the thrust jet which impinges along the entire body of the FB. Further increase of the LB flexion creates even stronger negative pressure regions while widening the thrust jet behind the leader. These flow conditions are more favorable for the FB as the accelerated flow only interacts with the anterior of the robot body and the stronger negative pressure supplies stronger anterior suction. The ability of the FB to sense these flow changes is also important, and the pressure sensor data on the FB exhibits differences between the cases. Near the anterior surface, the sensor pressure data provides insight to the varying vortex ring strengths for higher LB CF flexion, meanwhile, such differences are not as obvious when examining probe data further downstream on the FB.

Robot swarms can be tasked with a variety of automated sensing and inspection applications in aerial, aquatic, and surface environments. In this paper, we study a simplified two-outcome surface inspection task. We task a group of robots to inspect and collectively classify a 2D surface section based on a binary pattern projected on the surface. We use a decentralized Bayesian decision-making algorithm and deploy a swarm of 3-cm sized wheeled robots to inspect a randomized black and white tiled surface section of size 1m×1m in simulation. We first describe the model parameters that characterize our simulated environment, the robot swarm, and the inspection algorithm. We then employ a noise-resistant heuristic optimization scheme based on the Particle Swarm Optimization (PSO) using a fitness evaluation that combines the swarm’s classification decision accuracy and decision time. We use our fitness measure definition to asses the optimized parameters through 100 randomized simulations that vary surface pattern and initial robot poses. The optimized algorithm parameters show up to 55% improvement in median of fitness evaluations against an empirically chosen parameter set.

Robot swarms can effectively serve a variety of sensing and inspection applications. Certain inspection tasks require a binary classification decision. This work presents an experimental setup for a surface inspection task based on vibration sensing and studies a Bayesian two-outcome decision-making algorithm in a swarm of miniaturized wheeled robots. The robots are tasked with individually inspecting and collectively classifying a 1mx1m tiled surface consisting of vibrating and non-vibrating tiles based on the majority type of tiles. The robots sense vibrations using onboard IMUs and perform collision avoidance using a set of IR sensors. We develop a simulation and optimization framework leveraging the Webots robotic simulator and a Particle Swarm Optimization (PSO) method. We consider two existing information sharing strategies and propose a new one that allows the swarm to rapidly reach accurate classification decisions. We first find optimal parameters that allow efficient sampling in simulation and then evaluate our proposed strategy against the two existing ones using 100 randomized simulation and 10 real experiments. We find that our proposed method compels the swarm to make decisions at an accelerated rate, with an improvement of up to 20.52% in mean decision time at only 0.78% loss in accuracy.

Robot swarms can effectively serve a variety of sensing and inspection applications. Certain inspection tasks require a binary classification decision. This work presents an experimental setup for a surface inspection task based on vibration sensing and studies a Bayesian two-outcome decision-making algorithm in a swarm of miniaturized wheeled robots. The robots are tasked with individually inspecting and collectively classifying a 1mx1m tiled surface consisting of vibrating and non-vibrating tiles based on the majority type of tiles. The robots sense vibrations using onboard IMUs and perform collision avoidance using a set of IR sensors. We develop a simulation and optimization framework leveraging the Webots robotic simulator and a Particle Swarm Optimization (PSO) method. We consider two existing information sharing strategies and propose a new one that allows the swarm to rapidly reach accurate classification decisions. We first find optimal parameters that allow efficient sampling in simulation and then evaluate our proposed strategy against the two existing ones using 100 randomized simulation and 10 real experiments. We find that our proposed method compels the swarm to make decisions at an accelerated rate, with an improvement of up to 20.52% in mean decision time at only 0.78% loss in accuracy.

Robot swarms can be tasked with a variety of automated sensing and inspection applications in aerial, aquatic, and surface environments. In this paper, we study a simplified two-outcome surface inspection task. We task a group of robots to inspect and collectively classify a 2D surface section based on a binary pattern projected on the surface. We use a decentralized Bayesian decision-making algorithm and deploy a swarm of miniature 3-cm sized wheeled robots to inspect randomized black and white tiles of $1m\times 1m$. We first describe the model parameters that characterize our simulated environment, the robot swarm, and the inspection algorithm. We then employ a noise-resistant heuristic optimization scheme based on the Particle Swarm Optimization (PSO) using a fitness evaluation that combines decision accuracy and decision time. We use our fitness measure definition to asses the optimized parameters through 100 randomized simulations that vary surface pattern and initial robot poses. The optimized algorithm parameters show up to a 55% improvement in median of fitness evaluations against an empirically chosen parameter set.

Collective behaviour defines the lives of many animal species on the Earth. Underwater swarms span several orders of magnitude in size, from coral larvae and krill to tunas and dolphins. Agent-based algorithms have modelled collective movements of animal groups by use of social forces , which approximate the behaviour of individual animals. But details of how swarming individuals interact with the fluid environment are often under-examined. How do fluid forces shape aquatic swarms? How do fish use their flow-sensing capabilities to coordinate with their schooling mates? We propose viewing underwater collective behaviour from the framework of fluid stigmergy , which considers both physical interactions and information transfer in fluid environments. Understanding the role of hydrodynamics in aquatic collectives requires multi-disciplinary efforts across fluid mechanics, biology and biomimetic robotics. To facilitate future collaborations, we synthesize key studies in these fields.