In this paper, we propose a mapping technique that builds a continuous representation of the environment from range data. The strategy presented here encodes the probability of points in space to be occupied using 2.5D B-spline surfaces. For a fast update rate, the surface is recursively updated as new measurements arrive. The proposed B-spline map is less susceptible to precision and interpolation errors that are present in occupancy grid-based methods. From simulation and experimental results, we show that this approach leverages the floating point resolution of continuous metric maps and the fast update/access/merging advantages of discrete metric maps. Thus, the proposed method is suitable for online robotic tasks such as localization and path planning, requiring minor modification to existing software that usually operates on metric maps.

This paper presents a 2D B-spline mapping framework for representing unstructured environments in a compact manner. While occupancy-grid and landmark-based maps have been successfully employed by the robotics community in indoor scenarios, outdoor long-term autonomous operations require a more compact representation of the environment. This work tackles this problem by interpolating the data of a high frequency sensor using B-spline curves. Compared to lines and circles, splines are more powerful in the sense that they allow for the description of more complex shapes in the scene. In this work, spline curves are continuously tracked and aligned across multiple sensor readings using lightweight methods, making the proposed framework suitable for robot navigation in outdoor missions. In particular, a Simultaneous Localization and Mapping (SLAM) algorithm specifically tailored for B-spline maps is presented here. The efficacy of the proposed framework is demonstrated by Software-in-the-Loop (SiL) simulations in different scenarios.

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.

Robotic odor source localization is a promising tool with numerous applications in safety, search and rescue, and environmental science. In this paper, we present an algorithm for odor source localization using multiple cooperating robots equipped with chemical sensors. Laplacian feedback is employed to maintain the robots in a formation, introducing spatial diversity that is used to better establish the position of the flock relative to the plume and its source. Robots primarily move upwind but use odor information to adjust their position and spacing so that they are centered on the plume and trace its structure. Real-world experiments were performed with an ethanol plume inside a wind tunnel, and used to both validate the algorithm and assess the impact of different formation shapes.

This paper addresses the problem of time-coordination of a team of cooperating multirotor unmanned aerial vehicles that exchange information over a supporting time-varying network. A distributed control law is developed to ensure that the vehicles meet the desired temporal assignments of the mission, while flying along predefined collision-free paths, even in the presence of faulty communication networks, temporary link losses, and switching topologies. In this paper, the coordination task is solved by reaching consensus on a suitably defined coordination state. Conditions are derived under which the coordination errors converge to a neighborhood of zero. Simulation and flight test results are presented to validate the theoretical findings. Note to Practitioners-This paper presents an approach which enables a fleet of multirotor UAVs to follow a set of desired trajectories and coordinate along them, thus satisfying specific spatial and temporal assignments. The proposed solution can be employed in applications in which multiple vehicles are tasked to execute cooperative, collision-free maneuvers, and accomplish a common goal in a safely manner. An example is sequential monitoring, in which the UAVs have to visit and monitor a set of points of interest, while maintaining a desired temporal separation between each other. In this paper, we also simulate a scenario in which the vehicles, positioned in a square room, are required to exchange position with each other. It is shown that the proposed control algorithm not only ensures that the UAVs arrive at the final destinations at the same time, but also guarantees safety, i.e., the vehicles avoid collision with each other at all times.

The use of groups of autonomous marine vehicles has enormous potential in numerous marine applications, perhaps the most relevant of which is the surveying and exploration of the oceans, still widely unknown and misunderstood. In many mission scenarios requiring the concerted operation of multiple marine vehicles carrying distinct, yet complementary sensor suites, relative positioning and formation control becomes mandatory. However, the constraints placed by the medium make it hard to both communicate and localize vehicles, even in relation to each other. In this paper, we deal with the challenging problem of keeping an autonomous underwater vehicle in a moving triangular formation with respect to 2 leader vehicles. We build upon our previous theoretical work on range-only formation control, which presents simple feedback laws to drive the controlled vehicle to its intended position in the formation using only ranges obtained to the leading vehicles with no knowledge of the formation path. We then introduce the real-world constraints associated with the use of autonomous underwater vehicles, especially the low frequency characteristics of acoustic ranging and its unreliability. We discuss the required changes to implement the solution in our vehicles, and provide simulation results using a full dynamic and communication model. Finally, we present the results of real world trials using MEDUSA-class autonomous marine vehicles.

This paper describes a decentralized coordination strategy for multi robot patrolling missions. To this effect, the theory of Gaussian Processes (usually used for estimation purposes) is suitably adapted to tackle the problem of harbour patrolling. The introduction of a time varying dependency in the probabilistic formulation (thus allowing for the sampled field to be dynamic, i.e., changing in time) makes the proposed solution suitable for the type of mission considered. Moreover, the advantages of Voronoi tessellations are exploited to automatically distribute the vehicles over the environment. The resulting algorithm takes into account several constraints and can be tailored based on the communication and computational capabilities of the robots, thus making it suitable for heterogeneous systems. Numerical simulations and experiments involving three autonomous marine surface vehicles in a harbour scenario at the Parque Expo site in Lisbon are discussed.

There is increasing interest in the use of Autonomous Underwater Vehicles (AUVs) to substantially improve the means available for ocean exploration and exploitation. A key element in the operation of certain classes of AUVs is the availability of good underwater positioning systems to localize one or more vehicles simultaneously based on information received on-board a support ship or a set of autonomous surface vehicles. In an interesting operational scenario, the set of autonomous surface vehicles carries a network of acoustic units that measure the elevation and azimuth angles between the target and each of the receivers. Motivated by these considerations, in this paper we address the problem of determining the optimal geometric configuration of an acoustic sensor network at the ocean surface that will maximize the angle-related information available for underwater target positioning. It is assumed that the angle measurements are corrupted by white Gaussian noise, the variance of which is distance-dependent. Using the Cramer-Rao lower bound inequality, the trace of the inverse of the Fisher Information matrix (also called the Cramer-Rao Bound matrix) for the problem at hand is used to determine the sensor configuration that yields the minimum possible covariance of any unbiased target estimator. It is shown that the optimal configuration lends itself to an interesting geometrical interpretation and that the spreading of the sensor configuration depends explicitly on the intensity of the measurement noise and the target depth. Simulation examples illustrate the key results derived.

Worldwide, there has been increasing interest in the use of Autonomous Underwater Vehicles (AUVs) to drastically change the means available for ocean exploration and exploitation. Representative missions include marine habitat mapping, pipeline inspection, and archaeological surveying. Central to the operation of some classes of AUVs is the availability of good underwater positioning systems to localize one or more vehicles simultaneously based on information received on-board a support ship or a set of autonomous surface vehicles. In an interesting operational scenario the AUV is equipped with an acoustic pinger and the set of surface vehicles carry a network of acoustic receivers that measure the ranges between the emitter and each of the receivers. Motivated by these considerations, in this paper we address the problem of determining the optimal geometric configuration of an acoustic sensor network at the ocean surface that will maximize the range-related information available for underwater target positioning. It is assumed that the range measurements are corrupted by white Gaussian noise, the variance of which is distance-dependent. Furthermore, we also assume that an initial estimate of the target position is available, albeit with uncertainty. The Fisher Information Matrix and the maximization of its determinant are used to determine the sensor configuration that yields the most accurate “expected” positioning of the target, the position of which is expressed by a probabilistic distribution. It is shown that the optimal configuration lends itself to an interesting geometrical interpretation and that the “spreading” of the sensor configuration depends explicitly on the intensity of the range measurement noise, the probabilistic distribution that defines the target position, and the target depth. Simulation examples illustrate the key results derived.

This paper addresses the problem of observability of the relative motion of two AUVs equipped with velocity and depth sensors, and inter-vehicle ranging devices. We start by exploiting nonlinear observability concepts to analyze, using observability rank conditions, some types of relative AUV motions. Because rank conditions only provide binary information regarding observability, we then derive a specific observability index (metric) and study its dependence on the types of relative motions executed by the vehicles. In particular, it is shown that the degradation of observability depends on the range and angle between the relative velocity and position vectors. The problem addressed bears affinity with that of single beacon localization. For this reason, the results derived are validated experimentally in a equivalent, single beacon navigation scenario.

This paper addresses the problem of estimating the position and attitude of a rigid body when the available measurements consist only of distances (or ranges) between a set of body fixed beacons and a set of earth fixed landmarks. To this effect, a maximum likelihood (ML) estimator is derived by solving an optimization problem on the special Euclidean group SE(n);n = 2,3 using intrinsic gradient and Newton-like algorithms. The theoretical tools used borrow from optimization theory on Riemannian manifolds. Supported by recent results on performance bounds for estimators on Riemannian manifolds, the intrinsic variance lower bound (IVLB) is derived for the problem at hand. Simulation results are presented to illustrate the estimator performance and to validate the tightness of the IVLB in a wide range of signal to noise ratio scenarios