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Automatic control design for robotic systems is becoming more and more popular. However, this usually involves a significant computational cost, due to the expensive and noisy evaluation of candidate solutions through highfidelity simulation or even real hardware. This work aims at reducing the computational cost of automatic design of behavioral arbitrators through the introduction of a two-step approach. In the first step, the structure of the finite state machine governing the behavioral arbitrator is optimized. To this purpose, a more abstracted model of the robotic system is leveraged in order to significantly reduce the computational cost. In the second step, the close-to-hardware, behavioral parameters are fine-tuned using a high-fidelity model. We show that, for a scenario involving a single robot and multiple tasks to be solved sequentially, using the proposed method results in a significant decrease of the computational cost while reaching the same controller performance both in simulation and reality.

This paper presents a thorough experimental evaluation of an extended Gaussian Mixture Probability Hypothesis Density filter which is able to provide state estimates for the maintenance of a multirobot formation, even when the communication fails and the tracking data are insufficient for maintaining a stable formation. The filter incorporates, firstly, absolute poses exchanged by the robots, and secondly, the geometry of the desired formation. By combining communicated data, information about the formation, and sensory detections, the resulting algorithm preserves accuracy in the state estimates despite frequent occurrences of long-duration sensing occlusions, and provides the necessary state information when the communication is sporadic or suffers from short-term outage. Differently from our previous contributions, in which the tracking strategy has only been tested in simulation, in this paper we present the results of experiments with a real multi-robot system. The results confirm that the algorithm enables robust formation maintenance in cluttered environments, under conditions affected by sporadic communication and high measurement uncertainty.

Can we devise a unique methodological framework for modeling and controlling these self-assembling systems?

Ultra-wideband (UWB) localization is one of the most promising indoor localization methods. Yet, non-line-of- sight (NLOS) positioning scenarios remain a challenge and can potentially cause significant localization errors. In this work, we leverage the collaborative paradigm of a multi-robot system by sharing relative positioning information, and thus alleviating error susceptibility in NLOS ranging scenarios. In particular, we detail a decentralized particle filter based localization algorithm which combines an UWB range model with a robot detection model. Finally, we test both collaborative and non-collaborative versions of our algorithm in simulation, in mixed LOS/NLOS scenarios. Results show superior performance for the collabora- tive system when compared to non-collaborative systems utilizing only UWB ranging

Accurate localization is one of the biggest challenges in underwater robotics. The primary reasons behind that are unavailability of satellite-based positioning below the surface, and lack of clear features in natural water bodies for visually aided localization. As such, the common method of choice for external position referencing in underwater robots is the use of acoustic signals for computing range or direction of arrival. To that end, we have developed an acoustic range based navigation system with floating, movable beacons. In this paper, we present an approach for planning the trajectory of acoustic beacons in a way that they provide the best possible navigation support for a group of underwater vehicles. We use an information theoretic approach to beacon path planning that minimizes the group’s position uncertainty. We evaluate our approach with realistic simulations calibrated using real-world data, and present results.

The last decade has seen a growing interest in air quality monitoring using networks of wireless low-cost sensor platforms. One of the unifying characteristics of chemical sensors typically used in real-world deployments is their slow response time. While the impact of sensor dynamics can largely be neglected when considering static scenarios, in mobile applications chemical sensor measurements should not be considered as point measurements (i.e. instantaneous in space and time). In this paper, we study the impact of sensor dynamics on measurement accuracy and locality through systematic experiments in the controlled environment of a wind tunnel. We then propose two methods for dealing with this problem: (i) reducing the effect of the sensor's slow dynamics by using an open active sampler, and (ii) estimating the underlying true signal using a sensor model and a deconvolution technique. We consider two performance metrics for evaluation: localization accuracy of specific field features and root mean squared error in field estimation. Finally, we show that the deconvolution technique results in consistent performance improvement for all the considered scenarios, and for both metrics, while the active sniffer design considered provides an advantage only for feature localization, particularly for the highest sensor movement speed.

We present a method based on covariance intersection for cooperative localization with pairwise range-only relative measurements. Our method was designed for underwater robots equipped with an acoustic communication and ranging system. Range measurements are not sufficient to compute a complete relative $3$D position. Therefore, covariance intersection is performed in a transformed space along their relative estimated positions, while preserving cross-correlations between other state variables. Given the characteristics of the acoustic channel, only one robot can transmit data or a ranging request at a time, hence the pairwise limitation. We also present a heuristic for choosing a peer robot for a range measurement by maximizing mutual information. Our method places no further restrictions on the order, timing or scheduling of relative measurements. We evaluated our method for accuracy and consistency, and present results from simulations as well as outdoor experiments.

Designing effective behavioral controllers for mobile robots can be difficult and tedious; this process can be circumvented by using online learning techniques which allow robots to generate their own controllers online in an automated fash- ion. In multi-robot systems, robots operating in parallel can potentially learn at a much faster rate by sharing information amongst themselves. In this work, we use an adapted version of the Particle Swarm Optimization algorithm in order to accomplish distributed online robotic learning in groups of robots with access to only local infor- mation. The effectiveness of the learning technique on a benchmark task (generating high-performance obstacle avoidance behavior) is evaluated for robot groups of various sizes, with the maximum group size allowing each robot to individually contain and manage a single PSO particle. To increase the realism of the technique, different PSO neighborhoods based on limitations of real robotic communication are tested and com- pared in this scenario. We explore the effect of varying communication power for one of these communication-based PSO neighborhoods. To validate the effectiveness of these learning techniques, fully distributed online learning experiments are run using a group of 10 real robots, generating results which support the findings from our simulations.

This paper gives a high-level overview of our research concerning swarm-intelligent inspection systems for turbine inspection.

We model the dynamics of self-organized robot aggregation inspired by a study on aggregation of gregarious arthropods. In swarms of German cockroaches, aggregation into clusters emerges solely from local interactions between the individuals, whereas the probabilities to join or leave a cluster are a function of the cluster size. Rather than explicitly modeling the spatial distribution of robots in the environment, we propose a population dynamics model that keeps track of the number of robots in clusters of specific size. The model is able to quantitatively and qualitatively predict the dynamics observed in extensive realistic simulation. In particular, we show both by modeling and simulation that the emergence of a giant component requires a minimal communication distance between individuals, whereas the robots remain scattered in the environment otherwise.

Combining networks of static sensors with minimalist robotic swarms might enable a new generation of micromachinery equipped with sensor and actuator networks for inspection, maintenance, and repair. In such a scenario, limited capabilities of swarm members (due to miniaturization or for economical reasons) might render deterministic algorithms unfeasible and require a self-organized approach. Driven by a case study concerned with the autonomous inspection of a jet turbine engine, we identify three main research axes: development of appropriate hardware, modeling and design of self-organized dynamical systems, and synthesis of robot controllers to achieve a desired collective behavior, which is for instance provided by an human operator during runtime. We also present our developments of embedded communication systems for miniature robots, allowing for communication within the swarm and static nodes in the environment. Such networks of static and mobile nodes might allow for sophisticated spatio-temporal coordination, which would otherwise require localization and navigation abilities that are unfeasible on miniature platforms.

We report on real-robot odor source localization experiments carried out in an environment with obstacles in the odor plume. The robot was equipped with an ethanol sensor and a wind direction sensor, and the experiments were carried out in a wind tunnel, i.e. in a controlled environment. An enhanced version of the surge-spiral algorithm was used, which was augmented with a dedicate behavior to manage obstacles (avoid them, or follow their contour). We compare the results in terms of distance overhead and success rate, and discuss the impact of obstacles on plume traversal.

The deployment of robots for Gas Source Lo- calization (GSL) tasks in hazardous scenarios significantly reduces the risk to humans and animals. Gas sensing using mobile robots focuses primarily on simplified scenarios, due to the complexity of gas dispersion, with a current trend towards tackling more complex environments. However, most state-of-art GSL algorithms for environments with obstacles only depend on local information, leading to low efficiency in large and more structured spaces. The efficiency of GSL can be improved dramatically by coupling it with a global knowledge of gas distribution in the environment. However, since gas dispersion in a built environment is difficult to model analytically, most previous work incorporating a gas dispersion model was tested under simplified assumptions, which do not take into consideration the impact of the presence of obstacles to the airflow and gas plume. In this paper, we propose a probabilistic algorithm that enables a robot to efficiently localize gas sources in built environments, by combining a state-of-the-art probabilistic GSL algorithm, Source Term Estimation (STE) with a learned plume model. The pipeline of generating gas dispersion datasets from realistic simulations, the training and validation of the model, as well as the integration of the learned model with the STE framework are presented. The performance of the algorithm is validated both in high-fidelity simulations and real experiments, with promising results obtained under various obstacle configurations.

Taking distributed robotic system research from simulation to the real world often requires the use of small robots that can be deployed and managed in large numbers. This has led to the development of a multitude of these devices, deployed in the thousands by researchers worldwide. This paper looks at the Khepera IV mobile robot, the latest iteration of the Khepera series. This full-featured differential wheeled robot provides a broad set of sensors in a small, extensible body, making it easy to test new algorithms in compact indoor arenas. We describe the robot and conduct an independent performance evaluation, providing results for all sensors. We also introduce the Khepera IV Toolbox, an open source framework meant to ease application development. In doing so, we hope to help potential users assess the suitability of the Khepera IV for their envisioned applications and reduce the overhead in getting started using the robot.

For the multi-robot coverage problem determin- istic deliberative as well as probabilistic approaches have been proposed. Whereas deterministic approaches usually provide provable completeness and promise good performance under perfect conditions, probabilistic approaches are more robust to sensor and actuator noise, but completion cannot be guaranteed and performance is sub-optimal in terms of time to completion. In reality, however, almost all deterministic algorithms for robot coordination can be considered probabilistic when considering the unpredictability of real world factors. This paper investigates experimentally and analytically how probabilistic and deterministic algorithms can be combined for maintaining the robustness of probabilistic approaches, and explicitly model the reliability of a robotic platform. Using realistic simulation and data from real robot experiments, we study system performance of a swarm-robotic inspection system at different levels of noise (wheel-slip). The prediction error of a purely deterministic model increases when the assumption of perfect sensors and actuators is violated, whereas a combination of probabilistic and deterministic models provides a better match with experimental data.

Odor plume tracing is a challenging robotics application, made difficult by the combination of the patchy characteristics of odor distribution and the slow response of the available sensors. This work proposes a graph-based formation control algorithm to coordinate a group of small robots equipped with odor sensors, with the goal of tracing an odor plume to its source. This approach makes it possible to organize the robots in arbitrary and evolving formation shapes, with the aim of improving tracking performance. The algorithm was evaluated in a high-fidelity submicroscopic simulator, using different formations and achieving quick convergence and negligible distance overhead in laminar wind flows.

For the multi-robot coverage problem deterministic deliberative as well as probabilistic approaches have been proposed. Whereas deterministic approaches usually provide provable completeness and promise good performance under perfect conditions, probabilistic approaches are more robust to sensor and actuator noise, but completion cannot be guaranteed and performance is sub-optimal in terms of time to completion. In reality, however, almost all deterministic algorithms for robot coordination can be considered probabilistic when considering the unpredictability of real world factors. This paper investigates experimentally and analytically how probabilistic and deterministic algorithms can be combined for maintaining the robustness of probabilistic approaches, and explicitly model the reliability of a robotic platform. Using realistic simulation and data from real robot experiments, we study system performance of a swarm-robotic inspection system at different levels of noise (wheel-slip). The prediction error of a purely deterministic model increases when the assumption of perfect sensors and actuators is violated, whereas a combination of probabilistic and deterministic models provides a better match with experimental data.

Hunde werden aufgrund ihrer exzellenten Nase oft zur Suche von Minen, Bomben, Drogen oder verschütteten Menschen eingesetzt. Mit elektronischen Geruchssensoren könnten für solche Anwendungen bald auch mobile Roboter zum Einsatz kommen. Neben guten Geruchssensoren und passenden Robotern sind aber auch entsprechende Suchalgorithmen notwendig - und die komplexe Ausbreitung von Rauch und Duftmolekülen in der Luft macht die Suche nach Geruchsquellen zu einer grossen Herausforderung.

We introduce a novel case study in which a group of miniaturized robots screen an environment for undesirable cells, and destroy them. Because miniaturized robots are usually endowed with reactive controllers and minimalist sensing and actuation capabilities, they must collaborate in order to achieve their task successfully. In this paper, we show how aggregation can mediate both collective perception and action while maintaining the scalability of the algorithm. First, we demonstrate the feasibility of our approach by implementing it on a real group of Alice mobile robots, which are only two centimeters in size. Then, we use a combination of both realistic simulations and macroscopic models in order to find optimal parameters that maximize the number of undesirable cells destroyed while minimizing the impact on the healthy population. Finally, we discuss the limitations of these models, both in terms of accuracy, computational cost, and scalability, and we outline the importance of an appropriate multi-level modeling methodology to ensure the relevance and the faithfulness of such models.

Environmental monitoring and mapping operations are an essential tool to combat climate change. An important branch of this domain concerns the construction of reliable gas maps. Adaptive navigation strategies coupled with multi-robot systems improve the outcome of an environmental mapping mission by focusing more efficiently on informative areas. This direction is yet to be explored in the context of gas mapping, which presents peculiar challenges due to the hard-to-sense and expensive-to-model nature of the underlying phenomenon. In this paper, we introduce the application of a multi-robot system to a gas mission with severe time constraints. We study the impact of information-based navigation strategies, coupled with increasing levels of coordination among the robots, on information gathering and consequent map reconstruction performance. We also focus on proposing solutions that inject additional knowledge into the system to enhance the final mapping outcome. We tested the strategies through extensive high-fidelity simulation experiments, and we compared the proposed approaches to three relevant baseline methods.

The remarkable accessibility of modern flying robots makes them an attractive platform for environmental sensing. However, low cost and ease of use are currently incompatible with large payloads, severely limiting the choice of sensor and ultimately modality. This paper describes the design of a system for using a small infrared thermometer to estimate the surface temperature over an area that is large compared to the area measured by the sensor, by mounting it on a flying robot. We leverage a priori knowledge about the spatial statistics of the phenomena under measure in order to plan an informative sampling path, fusing observations by Gaussian process regression. Our approach is designed to be evaluated in an indoor testbed, in which a quadrotor, in cooperation with simulated static sensing nodes, estimates the spatial distribution of surface temperature over a controlled thermal gradient. We perform extensive systematic experimentation both in simulation and our real-world testbed environment, with our algorithm estimating surface temperature to an accuracy of up to 2.1 °C over a 16 m2 area ranging in value from 25-65 °C.

This work focuses on multi-robot coordination based on the Hoplites framework for solving the multi-robot task allocation (MRTA) problem. Three variations of increasing complexity for the MRTA problem, spatial task allocation based on distance, spatial task allocation based on time and distance and persistent coverage have been studied in this work. The Fast Marching Method (FMM) has been used for robot path planning and providing estimates of the plans that robots bid on, in the context of the market. The use of this framework for solving the persistent coverage problem provides interesting insights by taking a high-level approach that is different from the commonly used solutions to this problem such as computing robot trajectories to keep the desired coverage level. A high fidelity simulation tool, Webots, along with the Robotic Operating System (ROS) have been utilized to provide our simulations with similar complexity to the real world tests. Results confirm that this pipeline is a very effective tool for our evaluations given that our simulations closely follow the results in reality. By modifying the replanning to prevent having costly or invalid plans by means of priority planning and turn taking, and basing the coordination on maximum plan length as opposed to time, we have been able to make improvements and adapt the Hoplites framework to our applications. The proposed approach is able to solve the spatial task allocation and persistent coverage problems in general. However, there exist some limitations. Particularly, in the case of persistent coverage, this method is suitable for applications where moderate spatial resolutions are sufficient such as patrolling.

A method for fabricating a fluid container, wherein at least two half containers are mated in said fluid to be contained in said container. This method allows the incorporation of prefabricated devices into each half containers as well as the functional coupling of these devices after mating of the half containers, thus resulting in a functional hybrid MEMS fluid container.

In this contribution, we describe a novel approach to the problem of setting micrometric droplets in motion. First, this paper reviews the state-of-the-art methods to enable millimetric droplet motion, and investigates the scalability of the most promising techniques on the basis of preliminary experiments. Then, we propose a novel approach based on substrates with radial 3D pattern. In the poster, we will investigate this approach in further details using numerical models and systematic experiments.

Because of their excellent olfactory sense, dogs are often used to find bombs, mines, drugs, or people buried by avalanches. For such applications, autonomous mobile robots could be used in the future. Electronic sensors already exist for a wide variety of substances, and are still being actively researched. Mobile robots are an important area of research, too. But beyond a good sensor and a suitable robotic platform, a third component is required: odor source localization algorithms – and due to the complex propagation of odor molecules in the air, tracking down odor sources is still a big challenge.

In the framework of SelfSys, we are studying the yield of surface-tension-driven self-assembly that can be obtained with SU8 filled cylinders having different aspect-ratio (height / diameter). We show here, the fabrication process of our parts and an experiment allowing to observe the surface tension driven self-assembly of SU8 micro cylinders in chains after agitation.

We propose an institutional robotics approachto the design of socially-aware multi-robot systems, wherecooperation among the robots and their social interactions withhumans are guided using institutions. Inspired by the conceptsstemming from economical sciences, robot institutions serve ascoordination artifacts, which specify behavioral rules that areacceptable or desirable given the situation and which can bereplaced by other rules to enforce new acceptable or desirablebehaviors without changing the robot’s core code. In this paperwe propose a formal methodology for consistent design ofcoordinated multi-robot behaviors intended for use in human-populated environments. We illustrate theoretical concepts withpractical examples. Graph-based formations serve as a basisfor coordinated multi-robot behaviors and concepts from theliterature on human-aware navigation provide social rules thatare enforced by the institutions. Experiments are carried outin a high-fidelity robotic simulator to illustrate the applicationof the theoretical concepts.

Applications of robots for gathering data in underwater environments has been limited due to the challenges posed by the medium. We have developed a miniature, agile, easy to carry and deploy Autonomous Underwater Vehicle (AUV) equipped with a suite of sensors for underwater environmental sensing. We have also developed a compact high resolution fast temperature sensing module for the AUV for microstructure and turbulence measurements in water bodies. In this paper, we describe a number of algorithms and subsystems of the AUV that enable autonomous real-world operation, and present the data gathered in an experimental campaign in collaboration with limnologists. We demonstrate adaptive sampling missions where the AUV could autonomously locate a zone of interest and adapt its trajectory to stay in it. Further, it could execute specific behaviors to accommodate special sensing requirements necessary to enhance the quality of the data collected. In these missions, the AUV could autonomously trace a feature and capture horizontal variation in various quantities, including turbidity and temperature fluctuations, allowing limnologists to study lake phenomena in an additional dimension.

Robotic chemical plume tracing is a growing area of research, with envisioned real-world applications including pollution tracking, search and rescue, and ecosystem identification. However, following a chemical signal in the water is not an easy task due to the nature of chemical transport and to limitations in sensing and communication. In this paper, we propose an approach for near-surface waterborne plume tracing using a combined team of autonomous surface and underwater vehicles. All vehicles are equipped with appropriate chemical sensors and acoustic modems. The team moves in a triangular formation, while using the flow direction and the samples obtained to steer the group along the plume. Leader vehicles at the surface implement a formation controller based on Laplacian feedback while the underwater vehicle performs acoustic ranging to the leaders. The solution was evaluated using a CFD simulation of a freshwater plume and a calibrated dynamic model of the MEDUSA autonomous marine vehicles. The group is able to move in a stable formation, sample the salinity, and trace the plume to its source.

One of the main challenges in underwater robot localization is the scarcity of external positioning references. Therefore, accurate inertial localization in between external position updates is crucial for applications such as underwater environmental sampling. In this paper, we present a framework for estimating kinematic and dynamic model parameters used for inertial navigation. Accurate values of these parameters result in better trajectory estimation. Our approach can run online as well as offline, with either choice providing different advantages. Further, our framework can correct errors in the past trajectory at each estimation step. By doing so, we are able to provide improved geo-references for past as well as future spatial measurements made by the robots. This has an impact on adaptive sampling methods, which use geo-tagged measurements for building local spatial distributions and choose future sampling points. We present results from field experiments and demonstrate improvement in trajectory estimation accuracy. We also experimentally show that with optimal parameter estimates, robots can tolerate longer intervals in external positioning updates for a specified acceptable level of estimation error.

Wireless radios are a great consumer of energy in sensor networks. Retrieving data from a remote deployment in an energy-efficient fashion is a difficult problem, and while solutions have been proposed in literature, real-world systems typically implement robust though inefficient methods. In an effort to bring efficient monitoring techniques to real-world environmental sensor networks, we seek to now quantify the performance brought by these algorithms in practical terms, i.e., by their resulting reduction in overall station energy consumption. To this end, we have developed a power monitoring extension board that integrates seamlessly with a commercial environmental sensor network platform. The board is capable of measuring all incoming and outgoing power for a station, and can disconnect subsystems, such as the solar panel or sensor bus, as necessary. The board is currently deployed on an outdoor network and is undergoing extensive testing.

There is considerable worldwide interest in the use of groups of autonomous marine vehicles to carry our challenging mission scenarios, of which marine habitat mapping of complex, non-structured environments is a representative example. Relative positioning and formation control becomes mandatory in many of the missions envisioned, which require the concerted operation of multiple marine vehicles carrying distinct, yet complementary sensor suites. However, the constraints placed by the underwater medium make it hard to both communicate and localise the vehicles, even in relation to each other, let alone maintain them in a formation. As a contribution to overcoming some of these problems, this paper deals with the problem of keeping an autonomous marine vehicle in a moving triangular formation with respect to two leader vehicles. Simple feedback laws are derived to drive a controlled vehicle to its intended position in the formation using acoustic ranges obtained to the leading vehicles with no knowledge of the formation path. The paper discusses the implementation of this solution in the MEDUSA class of autonomous marine vehicles operated by IST and describes the results of trials with these vehicles exchanging information and ranges over an acoustic network.

We explore using particle swarm optimization on problems with noisy performance evaluation, focusing on unsupervised robotic learning. We adapt a technique of overcoming noise used in genetic algorithms for use with particle swarm optimization, and evaluate the performance of both the original algorithmand the noise-resistantmethod for several numerical problems with added noise, as well as unsupervised learning of obstacle avoidance using one or more robots.

Programmable self-assembly of modular robots offers promising means for structure formation at different scales. Rule-based approaches have been previously employed for distributed control of stochastic self-assembly processes. The assembly rate in the process directly depends on the concurrency level induced by the employed ruleset, i.e. the number of concurrent steps necessary to build one instance of the target structure. Our aim here is to design a formal synthesis algorithm to automatically derive rulesets of high concurrency for a given target structure composed of robotic modules. In the literature, self-assembly of (simulated or real) robotic modules has been realized through manually designed rulesets or manually adjusted rulesets generated by employing graph-grammar formalisms or metaheuristic methods. In this work, we employ an extended graph-grammar formalism, adapted for self-assembly of robotic modules, and propose a novel formal synthesis algorithm capable of generating rulesets for robotic modules by natively considering the morphology of their connectors. The synthesized rulesets induce a high level of concurrency in the self-assembly scheme by exploiting controlled information propagation, using solely local communication. Simulation results of microscopic (non-spatial) and submicroscopic (spatial) models of our robotic platform confirm higher performance of rulesets synthesized by our algorithm compared to related work in the literature.

We present an investigation of specialization when considering the execution of collaborative tasks by a robot swarm. Specifically, we consider the stick-pulling problem first proposed by Martinoli et al. [1], [2] and develop a macroscopic analytical model for the swarm executing a set of tasks that require the collaboration of two robots. We show, for constant external conditions, maximum productivity can be achieved by a single species swarm with carefully chosen operational parameters. While the same applies for a two species swarm, we show how specialization is a strategy best employed for changing external conditions.

Two prominent categories for achieving coordinated multirobot displacement are flocking and navigation in formation. Both categories have their own body of literature and characteristics, including their respective advantages and disadvantages. While typically, they are treated separately, we believe that a combination of flock and formation control represents a promising algorithmic solution. Such an algorithm could leverage a combination of characteristics of both categories best suited for a given situation. In this work, we therefore propose two distributed algorithms, able to gradually and reversibly shift between flocking and formation behaviors using a single parameter W. We evaluate them using both simulated and real robots with and without the presence of obstacles.We find that both algorithms successfully trade off flock density for formation error. Furthermore, using a funnel experiment as application case study, we demonstrate that an adaptive shift between flock and formation behavior, using a simple method to define W in real-time using exclusively on-board resources, results in a statistically relevant reduction of traversing time in comparison to a non-adaptive formation control algorithm.

The large number of potential applications for robotic odor source localization has motivated the development of a variety of plume tracking algorithms, the majority of which work in restricted two-dimensional scenarios. In this paper, we introduce a distributed algorithm for 3-D plume tracking using a system of ground and aerial robots in formation. We propose an algorithm that takes advantage of spatially distributed measurements to track the plume in 3-D and lead the robots to the source by integrating three behaviors -- upwind movement, plume centering, and Laplacian feedback formation control. We evaluate this strategy in simulation and with real robots in a wind tunnel. For a source close to the ground, results show that a team of robots running our algorithm reaches the source with low lateral error while also tracing the horizontal and vertical plume shape.

In this work we investigate the applicability of the wing-aileron-tab model for a bridge section model being actively controlled with leading and trailing edge flaps. The structural and aerodynamic model parameters have been extracted experimentally from a total of 140 wind tunnel experiments. Our in-house developed set-up has been employed for this purpose. Four different control strategies, with increasing complexity have been used for the parameter estimation. The first eight flutter derivatives have been obtained from step responses performed without control. The modified versions of these flutter derivatives have been estimated from step responses performed with three different types of active flap control, from which the leading edge and trailing edge flutter derivatives have been derived. The presented results are encouraging, however, further investigation is necessary in order to fully evaluate the wing-aileron-tab model’s applicability.

Inspection of aircraft and power generation machinery using a swarm of miniature robots is a promising application both from an intellectual and a commercial perspective. Our research is motivated by a case study concerned with the inspection of a jet turbine engine by a swarm of miniature robots. This article summarizes our efforts that include multi-robot path planning, modeling of self-organized robotic systems, and implementation of proof-of-concept experiments with real miniature robots. While other research tackles challenges that arise from moving within 3D structured environments at the level of the individual robotic node, the emphasis of our work is on explicitly incorporating the potential limitations of the individual robotic platform in terms of sensor and actuator noise into the modeling and design process of collaborative inspection systems. We highlight difficulties and further challenges on the (lengthy) path towards truly autonomous parallel robotic inspection of complex engineered structures.

Discrete optimization is a difficult task common to many different areas in modern research. This type of optimization refers to problems where solution elements can assume one of several discrete values. The most basic form of discrete optimization is binary optimization, where all solution elements can be either 0 or 1, while the more general form is problems that have solution elements which can assume $n$ different unordered values, where $n$ could be any integer greater than 1. While Genetic Algorithms (GA) are inherently able to handle these problems, there has been no adaption of Particle Swarm Optimization able to solve the general case.

In this paper, we present a time-discrete, incremental methodology for modeling, at the microscopic and macroscopic level, the dynamics of distributed manipulation experiments using swarms of autonomous robots endowed with reactive controllers. The methodology is well-suited for nonspatial metrics since it does not take into account robots’ trajectories or the spatial distribution of objects in the environment. The strength of the methodology lies in the fact that it has been generated by considering incremental abstraction steps, from real robots to macroscopic models, each with well-defined mappings between successive implementation levels. Precise heuristic criteria based on geometrical considerations and systematic tests with one or two real robots prevent the introduction of free parameters in the calibration procedure of models. As a consequence, we are able to generate highly abstracted macroscopic models that can capture the dynamics of a swarm of robots at the behavioral level while still being closely anchored to the characteristics of the physical set-up. Although this methodology has been and can be applied to other experiments in distributed manipulation (e.g., object aggregation and segregation, foraging), in this paper we focus on a strictly collaborative case study concerned with pulling sticks out of the ground, an action that requires the collaboration of two robots to be successful. Experiments were carried out with teams consisting of two to 600 individuals at different levels of implementation (real robots, embodied simulations, microscopic and macroscopic models). Results show that models can deliver both qualitatively and quantitatively correct predictions in time lapses that are at least four orders of magnitude smaller than those required by embodied simulations and that they represent a useful tool for generalizing the dynamics of these highly stochastic, asynchronous, nonlinear systems, often outperforming intuitive reasoning. Finally, in addition to discussing subtle numerical effects, small prediction discrepancies, and difficulties in generating the mapping between different abstractions levels, we conclude the paper by reviewing the intrinsic limitations of the current modeling methodology and by proposing a few suggestions for future work.

Water as a medium poses a number of challenges for robots, limiting the progress of research in underwater robotics vis-a-vis ground or aerial robotics. The primary challenges are satellite based positioning and radio communication being unusable due to high attenuation of electromagnetic waves in water. We have developed miniature, agile, easy to carry and deploy Autonomous Underwater Vehicles (AUVs) equipped with a suite of sensors for underwater environmental sensing. We previously demonstrated adaptive sampling and feature tracking, and gathered data from a lake for limnological research, with the AUV performing inertial navigation. In this paper, we demonstrate a new underwater acoustic positioning system, which allows on-board estimation of AUV position. Our system uses absolute time information from GNSS for initial clock synchronization and uses one-way-travel-time for range measurements, which makes it scalable in the number of robots. It is easily deployable and does not rely on any installed infrastructure in the environment. We describe various hardware and software components of our system, and present results from experiments in Lake Geneva.