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.

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 have the potential to revolutionize areas ranging from warehouse management and agriculture to underwater and space exploration. However, there remains a substantial gap between theory and robot implementation. While algorithms might assume reliable communication, perfect sensing, and instantaneous cognition, most robots have lossy or even no communication, imperfect sensing, and limited cognition speed. In our previous work on implicit vision-based coordination, we demonstrated autonomous three-dimensional behaviors underwater by removing the need for radio communication between robots. Here we explore impressionist algorithms, capable of working with even more minimal information where traditional algorithms are prone to fail. Our case study focuses on classic flocking behaviors, where a robot swarm must coordinate group motion. We demonstrate that reliable alignment, dispersion, and milling can be achieved with only infrequent and imperfect sensory impressions. In simulation studies and theoretical analyses, we investigate the effect of systematically reducing spatial and temporal fidelity of individual information on the success metrics for the group; we also demonstrate physical experiments with Blueswarm robots using simple color detection. Our results show the potential of impressionist algorithms that operate on simpler neighborhood-awareness metrics and still achieve desired global goals.

Groups of robots can be tasked with identifying a location in an environment where a feature cue is past a threshold, then disseminating this information throughout the group – such as identifying a high-enough elevation location to place a communications tower. This is a continuous-cue target search, where multi-robot search algorithms like particle swarm optimization (PSO) can improve search time through parallelization. However, many robots lack global communication in large spaces, and PSO-based algorithms often fail to consider how robots disseminate target knowledge after a single robot locates it. We present a two-stage hybrid algorithm to solve this task: (1) locating a target with a variation of PSO, and (2) moving to maximize target knowledge across the group. We conducted parameter sweep simulations of up to 32 robots in a grid-based grayscale environment. Pre-decision, we find that PSO with a variable velocity update interval improves target localization. In the post-decision phase, we show that dispersion is the fastest strategy to communicate with all other robots. Our algorithm is also competitive with a coverage sweep benchmark, while requiring significantly less inter-individual coordination.

Several animal species self-organize into large groups to leverage vital behaviors such as foraging, construction, 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 spatiotemporal 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, tracking and capture, or environmental sampling.

We present a distributed Bayesian algorithm for robot swarms to classify a spatially distributed feature of an environment. This type of "go/no-go" decision appears in applications where a group of robots must collectively choose whether to take action, such as determining if a farm field should be treated for pests. Previous bio-inspired approaches to decentralized decision-making in robotics lack a statistical foundation, while decentralized Bayesian algorithms typically require a strongly connected network of robots. In contrast, our algorithm allows simple, sparsely distributed robots to quickly reach accurate decisions about a binary feature of their environment. We investigate the speed vs. accuracy tradeoff in decision-making by varying the algorithm's parameters. We show that making fewer, less-correlated observations can improve decision-making accuracy, and that a well-chosen combination of prior and decision threshold allows for fast decisions with a small accuracy cost. Both speed and accuracy also improved with the addition of bio-inspired positive feedback. This algorithm is also adaptable to the difficulty of the environment. Compared to a fixed-time benchmark algorithm with accuracy guarantees, our Bayesian approach resulted in equally accurate decisions, while adapting its decision time to the difficulty of the environment.

Social insects successfully create bridges, rafts, nests and other structures out of their own bodies and do so with no centralized control system, simply by following local rules. For example, while traversing rough terrain, army ants (genus Eciton) build bridges which grow and dissolve in response to local traffic. Because these self-assembled structures incorporate smart, flexible materials (i.e. ant bodies) and emerge from local behavior, the bridges are adaptive and dynamic. With the goal of realizing robotic collectives with similar features, we designed a hardware system, Eciton robotica, consisting of flexible robots that can climb over each other to assemble compliant structures and communicate locally using vibration. In simulation, we demonstrate self-assembly of structures: using only local rules and information, robots build and dissolve bridges in response to local traffic and varying terrain. Unlike previous self-assembling robotic systems that focused on latticebased structures and predetermined shapes, our system takes a new approach where soft robots attach to create amorphous structures whose final self-assembled shape can adapt to the needs of the group.

In this paper we present the design of a fin-like dielectric elastomer actuator (DEA) that drives a miniature autonomous underwater vehicle (AUV). The fin-like actuator is modular and independent of the body of the AUV. All electronics required to run the actuator are inside the 100 mm long 3D-printed body, allowing for autonomous mobility of the AUV. The DEA is easy to manufacture, requires no pre-stretch of the elastomers, and is completely sealed for underwater operation. The output thrust force can be tuned by stacking multiple actuation layers and modifying the Young's modulus of the elastomers. The AUV is reconfigurable by a shift of its center of mass, such that both planar and vertical swimming can be demonstrated on a single vehicle. For the DEA we measured thrust force and swimming speed for various actuator designs ran at frequencies from 1 Hz to 5 Hz. For the AUV we demonstrated autonomous planar swimming and closed-loop vertical diving. The actuators capable of outputting the highest thrust forces can power the AUV to swim at speeds of up to 0.55 body lengths per second. The speed falls in the upper range of untethered swimming robots powered by soft actuators. Our tunable DEAs also demonstrate the potential to mimic the undulatory motions of fish fins.

Can overtrust in robots compromise physical security? We conducted a series of experiments in which a robot positioned outside a secure-access student dormitory asked passersby to assist it to gain access. We found individual participants were as likely to assist the robot in exiting the dormitory (40% assistance rate, 4/10 individuals) as in entering (19%, 3/16 individuals). Groups of people were more likely than individuals to assist the robot in entering (71%, 10/14 groups). When the robot was disguised as a food delivery agent for the fictional start-up Robot Grub, individuals were more likely to assist the robot in entering (76%, 16/21 individuals). Lastly, we found participants who identified the robot as a bomb threat demonstrated a trend toward assisting the robot (87%, 7/8 individuals, 6/7 groups). Thus, we demonstrate that overtrust---the unfounded belief that the robot does not intend to deceive or carry risk---can represent a significant threat to physical security at a university dormitory.

Climbing robots have many potential applications including maintenance, monitoring, search and rescue, and self-assembly. While numerous climbing designs have been investigated, most are limited to stiff components. Flippy (Fig. 1) is a small, flipping biped robot with a soft, flexible body and on-board power and control. Due to its built-in compliance, flipping gait, and corkscrew gripper, it can autonomously climb up and down surfaces held at any angle relative to gravity and transition from one surface to another, without complex sensing or control. In this paper, we demonstrate the robot's ability to flip consistently over a flat Velcro surface and 2D Velcro track, where it reliably climbs vertically, upside down and back to a flat surface, completing all the interior transitions in-between.

Commercially available depth sensing devices are primarily designed for domains that are either macroscopic, or static. We develop a solution for fast microscale 3D reconstruction, using off-the-shelf components. By the addition of lenses, precise calibration of camera internals and positioning, and development of bespoke software, we turn an infrared depth sensor designed for human-scale motion and object detection into a device with mm-level accuracy capable of recording at up to 30Hz.

Adaptive collective systems are common in biology and beyond. Typically, such systems require a task allocation algorithm: a mechanism or rule-set by which individuals select particular roles. Here we study the performance of such task allocation mechanisms measured in terms of the time for individuals to allocate to tasks. We ask: (1) Is task allocation fundamentally difficult, and thus costly? (2) Does the performance of task allocation mechanisms depend on the number of individuals? And (3) what other parameters may affect their efficiency? We use techniques from distributed computing theory to develop a model of a social insect colony, where workers have to be allocated to a set of tasks; however, our model is generalizable to other systems. We show, first, that the ability of workers to quickly assess demand for work in tasks they are not currently engaged in crucially affects whether task allocation is quickly achieved or not. This indicates that in social insect tasks such as thermoregulation, where temperature may provide a global and near instantaneous stimulus to measure the need for cooling, for example, it should be easy to match the number of workers to the need for work. In other tasks, such as nest repair, it may be impossible for workers not directly at the work site to know that this task needs more workers. We argue that this affects whether task allocation mechanisms are under strong selection. Second, we show that colony size does not affect task allocation performance under our assumptions. This implies that when effects of colony size are found, they are not inherent in the process of task allocation itself, but due to processes not modeled here, such as higher variation in task demand for smaller colonies, benefits of specialized workers, or constant overhead costs. Third, we show that the ratio of the number of available workers to the workload crucially affects performance. Thus, workers in excess of those needed to complete all tasks improve task allocation performance. This provides a potential explanation for the phenomenon that social insect colonies commonly contain inactive workers: these may be a 'surplus' set of workers that improves colony function by speeding up optimal allocation of workers to tasks. Overall our study shows how limitations at the individual level can affect group level outcomes, and suggests new hypotheses that can be explored empirically.

Social insects have evolved to self-assemble ad-hoc structures from their bodies to quickly adapt to unexpected obstacles and situations. Inspired by these natural systems, we present an autonomous tread-based robot which is capable of using its own body as a building block for assembling structures. We analytically assess the optimality of the robot design, and experimentally test its ability to climb over like robots under varying conditions. Finally, using a simple self-assembly algorithm relying on only local sensing, robot prototypes are used to demonstrate the self-assembly of a 2D pyramid structure.

There is a widely recognized need for improved STEM education and increased technological literacy. Robots represent a promising educational tool with potentially large impact, due to their broad appeal and wide relevance; however, many existing educational robot platforms have cost as a barrier to widespread use. Here we present AERobot, a simple low-cost robot that can be easily used for introductory programming and robotics teaching, starting from a primary or middle school level. The hardware is open-source and can be built for ~$10 per robot, making it possible for each student to have (and keep) their own robot, while still encompassing a rich sensor suite enabling a variety of activities. A free, open-source graphical programming environment allows students without previous programming experience to command the robot. We report on the results of three sessions of a one-week pilot course held in the summer of 2014 by STEM summer camp i2 Camp.

This study describes the design and implementation of several bioinspired algorithms for providing guidance to an ultra-lightweight micro-aerial vehicle (MAV) using a 2.6 g omnidirectional vision sensor. Using this visual guidance system we demonstrate autonomous speed control, centring, and heading stabilisation on board a 30 g MAV flying in a corridor-like environment. In addition to the computation of wide-field optic flow, the comparatively high-resolution omnidirectional imagery provided by this sensor also offers the potential for image-based algorithms such as landmark recognition to be implemented in the future.

We present a method for setting the pressure of multiple chambers using a single pressure source when they are interconnected via band-pass valves. These valves can be constructed from simple passive devices that behave like leaky check valves. We present the theory of operation and design parameters for individual valves, give a control strategy for serial connections of pressure chambers, and demonstrate the approach by building prototype valves and using them to control serially connected soft-robotic actuators from a single pressure source.

We present a locally reactive algorithm to construct arbitrary shapes with amorphous materials. The goal is to provide methods for robust robotic construction in unstructured, cluttered terrain, where deliberative approaches with pre-fabricated construction elements are difficult to apply. Amorphous materials provide a simple way to interface with existing obstacles, as well as irregularly shaped previous depositions. The local reactive nature of these algorithms allows robots to recover from disturbances, operate in dynamic environments, and provides a way to work with scalable robot teams.

In current robotics research there is a vast body of work on algorithms and control methods for groups of decentralized cooperating robots, called a swarm or collective. These algorithms are generally meant to control collectives of hundreds or even thousands of robots; however, for reasons of cost, time, or complexity, they are generally validated in simulation only, or on a group of a few tens of robots. To address this issue, this paper presents Kilobot, a low-cost robot designed to make testing collective algorithms on hundreds or thousands of robots accessible to robotics researchers. To enable the possibility of large Kilobot collectives where the number of robots is an order of magnitude larger than the largest that exist today, each robot is made with only $14 worth of parts and takes 5 minutes to assemble. Furthermore, the robot design allows a single user to easily operate a large Kilobot collective, such as programming, powering on, and charging all robots, which would be difficult or impossible to do with many existing robotic systems.

We compare our previously developed deterministic [7] and stochastic [3], [4] strategies for allocating tasks in robotic swarms1 consisting of very large populations of highly resource-constrained robots. We study our two task allocation approaches in a simulated scenario in which a collective of insect-inspired micro-aerial vehicles (MAVs) must produce a specified spatial distribution of pollination activity over a crop field. We investigate the approaches' requirements, advantages, and disadvantages under realistic conditions of error in robot localization, navigation, and sensing in simulation. Our results show that the deterministic approach, which requires region-based robot navigation, yields higher task progress in all cases. For robots without such navigation capabilities, the stochastic approach is a feasible alternative, and its resulting task progress is less sensitive to error in localization, error in navigation, and a combination of high error in localization, navigation, and sensing.

We present and compare three different amorphous materials for robotic construction. By conforming to surfaces they are deposited on, such materials allow robots to reliably construct in unstructured terrain. However, using amorphous materials presents a challenge to robotic manipulation. We demonstrate how deposition of each material can be automated and compare their material properties, cost, and cost in time in order to evaluate their suitability for developing amorphous robotic construction system.

A proposed adaptive soft orthotic device performs motion sensing and production of assistive forces with a modular, pneumatically-driven, hyper-elastic composite. Wrapping the material around a joint will allow simultaneous motion sensing and active force response through shape and rigidity control. This monolithic elastomer sheet contains a series of miniaturized pneumatically-powered McKibben-type actuators that exert tension and enable adaptive rigidity control. The elastomer is embedded with conductive liquid channels that detect strain and bending deformations induced by the pneumatic actuators. In addition, the proposed system is modular and can be configured for a diverse range of motor tasks, joints, and human subjects. This modular functionality is accomplished with a decentralized network of self-configuring nodes that manage the collection of sensory data and the delivery of actuator feedback commands. This paper mainly describes the design of the soft orthotic device as well as actuator and sensor components. The characterization of the individual sensors, actuators, and the integrated device is also presented.

We present an approach to designing scalable, decentralized control policies that produce a desired collective behavior in a spatially inhomogeneous robotic swarm that emulates a system of chemically reacting molecules. Our approach is based on abstracting the swarm to an advection-diffusion-reaction partial differential equation model, which we solve numerically using smoothed particle hydrodynamics (SPH), a meshfree technique that is suitable for advection-dominated systems. The parameters of the macroscopic model are mapped onto the deterministic and random components of individual robot motion and the probabilities that determine stochastic robot task transitions. For very large swarms that are prohibitively expensive to simulate, the macroscopic model, which is independent of the population size, is a useful tool for synthesizing robot control policies with guarantees on performance in a top-down fashion. We illustrate our methodology by formulating a model of rabbiteye blueberry pollination by a swarm of robotic bees and using the macroscopic model to select control policies for efficient pollination.

We present a scalable approach to optimizing robot control policies for a target collective behavior in a spatially inhomogeneous robotic swarm. The approach can incorporate robot feedback to maintain system performance in an unknown environmental flow field. We consider systems in which the robots follow both deterministic and random motion and transition stochastically between tasks. Our methodology is based on an abstraction of the swarm to a macroscopic continuous model, whose dimensionality is independent of the population size, that describes the expected time evolution of swarm subpopulations over a discretization of the environment. We incorporate this model into a stochastic optimization method and map the optimized model parameters onto the robot motion and task transition control policies to achieve a desired global objective. We illustrate our methodology with a scenario in which the behaviors of a swarm of robotic bees are optimized for both uniform and nonuniform pollination of a blueberry field, including in the presence of an unknown wind.

The performance of a swarm of robots depends on the hardware quality of the robots in the swarm. A swarm of robots with high-quality sensors and actuators is expected to out-perform a swarm of robots with low-quality sensors and actuators. This paper directly investigates the relationship between hardware quality and swarm performance. We take three common components of swarm algorithms (trail following, swarm expansion, and shape formation) and measure how they are affected by two common types of hardware inaccuracy (communication bearing reception error, and movement error) both in simulation and with E-Puck robots. We find that large amounts of both types of hardware error are required before performance appreciably decreases.

We describe the design of an active soft ankle-foot orthotic device powered by pneumatic artificial muscles for treating gait pathologies associated with neuromuscular disorders. The design is inspired by the biological musculoskeletal system of a human foot and a lower leg, and mimics the muscle-tendon-ligament structure. A key feature of the device is that it is fabricated with flexible and soft materials that provide assistance without restricting degrees of freedom at the ankle joint. Three pneumatic artificial muscles assist dorsiflexion as well as inversion and eversion. The prototype is also equipped with various embedded sensors for gait training and gait pattern analysis. The prototype is capable of 12° dorsiflexion from a resting position of an ankle joint and a 20° dorsiflexion from plantarflexion. Results of early feedback control experiments show controllability of ankle joint angles. Ultimately, we envision a system that not only can provide physical support to improve mobility but also can increase safety and stability during walking, while enhancing muscle usage and encouraging rehabilitation.

Decentralized agent groups typically require complex mechanisms to accomplish coordinated tasks. In contrast, biological systems can achieve intelligent group behaviors with each agent performing simple sensing and actions. We summarize our recent papers on a biologically-inspired control framework for multi-agent tasks that is based on a simple and iterative control law. We theoretically analyze important aspects of this decentralized approach, such as the convergence and scalability, and further demonstrate how this approach applies to real-world applications with a diverse set of multi-agent applications. These results provide a deeper understanding of the contrast between centralized and decentralized algorithms in multi-agent tasks and autonomous robot control.

Modular robots can potentially assemble into a wide range of configurations to locomote in different environments. However, designing locomotion strategies for each configuration is often tedious and has generally relied on a priori known connection geometry. Here we present a framework for 2D modular robots made of square modules assembled with arbitrary geometry, which achieve collective and directed locomotion with no centralized controller. Individual modules communicate locally and provably achieve consensus in coordinating movement in a common travel direction. In experiments with simulations and hardware prototypes, we show that robots achieve effective locomotion, irrespective of the number of modules and their connectivity which can be highly asymmetric.

Biological systems achieve amazing adaptive behavior with local agents performing simple sensing and actions. Modular robots with similar properties can potentially achieve self-adaptation tasks robustly. Inspired by this principle, we present a generalized distributed consensus framework for self-adaptation tasks in modular robotics. We demonstrate that a variety of modular robotic systems and tasks can be formulated within such a framework, including (1) an adaptive column that can adapt to external force, (2) a modular gripper that can manipulate fragile objects, and (3) a modular tetrahedral robot that can locomote towards a light source. We also show that control algorithms derived from this framework are provably correct. In real robot experiments, we demonstrate that such a control scheme is robust towards real world sensing and actuation noise. This framework can potentially be applied to a wide range of distributed robotics applications.

In nature, animal groups achieve robustness and scalability with each individual executes a simple and adaptive strategy. Inspired by this phenomenon, we propose a decentralized control framework for modular robots to achieve coordinated and self-adaptive tasks with each modules performs simple distributed sensing and actuation. In this demonstration, we show that such a framework allows several different modular robotic systems to achieve self-adaptation tasks scalably and robustly, examples tasks include module-formed table and bridge that adapt to constantly-perturbed environment, a 3D relief display that renders sophisticated objects, and a tetrahedral robot that performs adaptive locomotion.

The regulation of cleavage plane orientation is one of the key mechanisms driving epithelial morphogenesis. Still, many aspects of the relationship between local cleavage patterns and tissue-level properties remain poorly understood. Here we develop a topological model that simulates the dynamics of a 2D proliferating epithelium from generation to generation, enabling the exploration of a wide variety of biologically plausible cleavage patterns. We investigate a spectrum of models that incorporate the spatial impact of neighboring cells and the temporal influence of parent cells on the choice of cleavage plane. Our findings show that cleavage patterns generate "signature" equilibrium distributions of polygonal cell shapes. These signatures enable the inference of local cleavage parameters such as neighbor impact, maternal influence, and division symmetry from global observations of the distribution of cell shape. Applying these insights to the proliferating epithelia of five diverse organisms, we find that strong division symmetry and moderate neighbor/maternal influence are required to reproduce the predominance of hexagonal cells and low variability in cell shape seen empirically. Furthermore, we present two distinct cleavage pattern models, one stochastic and one deterministic, that can reproduce the empirical distribution of cell shapes. Although the proliferating epithelia of the five diverse organisms show a highly conserved cell shape distribution, there are multiple plausible cleavage patterns that can generate this distribution, and experimental evidence suggests that indeed plants and fruitflies use distinct division mechanisms.

We present a modular robot design inspired by the creation of complex structures and functions in biology via deformation. Our design is based on the Tensegrity model of cellular structure, where active filaments within the cell contract and expand to control individual cell shape, and sheets of such cells undergo large-scale shape change through the cooperative action of connected cells. Such deformations play a role in many processes, e.g. early embryo shape change and lamprey locomotion. Modular robotic systems that replicate the basic deformable multicellular structure have the potential to quickly generate large-scale shape change and create dynamic shapes to achieve different global functions. Based on this principle, our design includes four different modular components: (1) active links, (2) passive links, (3) surface membranes, and (4) interfacing cubes. In hardware implementation, we show several self-deformable structures that can be generated from these components, including a self-deformable surface, expandable cube, terrain-adaptive bridge [C.-H. Yu et al., 2007]. We present experiments to demonstrate that such robotic structures are able to perform real time deformation to adapt to different environments. In simulation, we show that these components can be configured into a variety of bio-inspired robots, such as an amoeba-like robot and a tissue-inspired material. We argue that self-deformation is well-suited for dynamic and sensing-adaptive shape change in modular robotics.

Desynchronization is a novel primitive for sensor networks: it implies that nodes perfectly interleave periodic events to occur in a round-robin schedule. This primitive can be used to evenly distribute sampling burden in a group of nodes, schedule sleep cycles, or organize a collision-free TDMA schedule for transmitting wireless messages. Here we present Desync a biologically-inspired self-maintaining algorithm for desynchronization in a single-hop network. We present (1) theoretical results showing convergence, (2) experimental results on TinyOS-based Telos sensor motes, and (3) a Desync based TDMA protocol. Desync-TDMA addresses two weaknesses of traditional TDMA: it does not require a global clock and it automatically adjusts to the number of participating nodes, so that bandwidth is always fully utilized. Experimental results show a reduction in message loss under high contention from approximately 58% to less than 1%, as well as a 25% increase in throughput over the default Telos MAC protocol.

We describe decentralized algorithms by which a swarm of simple, independent, autonomous robots can build two-dimensional structures using square building blocks. These structures can (1) exactly match arbitrary user-specified designs, (2) adapt their shape to immovable obstacles, or (3) form a wall of given minimum width around an environmental feature. These three possibilities span the range from entirely prespecified structures to those whose shape is entirely determined by the environment. Robots require no explicit communication, instead using information storage capabilities of environmental elements (a form of "extended stigmergy") to coordinate their activities. We provide theoretical proof of the correctness of the algorithms for the first two types of structures, and experimental support for algorithms for the third.

Modular robots have the potential to achieve a wide range of applications by reconfiguring their shapes to perform different functions. This requires robust and scalable control algorithms that can form a wide range of user-specified shapes, including shapes that adapt to the environment. Here we present a decentralized algorithm for self-organizing of environmentally-adaptive shapes. We apply it to a chain-style modular robot, configured to form a flexible sheet structure. We show that the proposed algorithm is capable of achieving a wide class of environmentally-adaptive shapes, and the module control is simple, scalable, robust and provably correct. The algorithm is also self-maintaining: the shape automatically adapts if the environment changes. Finally, we present several applications which can be achieved within this framework via robot prototypes and simulations, such as a self-balancing table. In our experiments, we demonstrate the algorithm is highly responsive and robust in the face of real-world actuation and sensing noise.

Macro programming a distributed system, such as a sensor network, is the ability to specify application tasks at a global level while relying on compiler-like software to translate the global tasks into the individual component activities. Bayesian networks can be regarded as a powerful tool for macro programming a distributed system in a variety of data analysis applications. In this paper we present our architecture to program a sensor network by means of Bayesian networks. We also present some applications developed on a microphone-sensor network, that demonstrate calibration, classification and anomaly detection

We describe a system in which autonomous robots assemble two-dimensional structures out of square building blocks. A fixed set of local control rules is sufficient for a group of robots to collectively build arbitrary solid structures. We present and compare four versions in which blocks are (1) inert and indistinguishable, (2) uniquely labeled, (3) able to be relabeled by robots, (4) capable of some computation and local communication. Added block capabilities increase the availability of nonlocal structural knowledge, thereby increasing robustness and significantly speeding construction. In this way we extend the principle of stigmergy (storing information in the environment) used by social insects, by increasing the capabilities of the blocks that represent that environmental information. Finally, we describe hardware experiments using a prototype capable of building arbitrary solid 2-D structures

Synchronicity is a useful abstraction in many sensor network applications. Communication scheduling, coordinated duty cycling, and time synchronization can make use of a synchronicity primitive that achieves a tight alignment of individual nodes' firing phases. In this paper we present the Reachback Firefly Algorithm (RFA), a decentralized synchronicity algorithm implemented on TinyOS-based motes. Our algorithm is based on a mathematical model that describes how fireflies and neurons spontaneously synchronize. Previous work has assumed idealized nodes and not considered realistic effects of sensor network communication, such as message delays and loss. Our algorithm accounts for these effects by allowing nodes to use delayed information from the past to adjust the future firing phase. We present an evaluation of RFA that proceeds on three fronts. First, we prove the convergence of our algorithm in simple cases and predict the effect of parameter choices. Second, we leverage the TinyOS simulator to investigate the effects of varying parameter choice and network topology. Finally, we present results obtained on an indoor sensor network testbed demonstrating that our algorithm can synchronize sensor network devices to within 100 μsec on a real multi-hop topology with links of varying quality.

Self-reconfigurable robots are built from modules, which are autonomously able to change the way they are connected, thus changing the overall shape of the robot. This self-reconfiguration process is difficult to control, because it involves the distributed coordination of large numbers of identical modules connected in time-varying ways. We present an approach where a desired shape is grown based on a scalable representation of the desired configuration, which is automatically generated from a 3D CAD model. The size of the configuration is adjusted continually to match the number of modules in the system. This has the advantage that if modules are removed or added, the system automatically adjusts its scale and thus self-repair is obtained as a side effect. This capability is achieved by distributed, local rules for module movement that are independent of the goal configuration. We compare the scale independent approach to one where the desired configuration is grown directly at a fixed scale. We find that the features of the scale independent approach come at the expense of an increased number of moves, messages, and time steps taken to reconfigure.