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We address the problem of motion planning for aerial drones to allow them to fly through confined spaces and narrow gaps between obstacles. Finding safe and feasible trajectories for steering multiple drones towards some desired positions in tight spaces or guiding them through gaps smaller in width than their diameters is impossible without taking the drones’ orientations into account. To incorporate the orientation into the motion planning problem we employ an ellipsoid model of the drone body, and utilize the separating hyperplane theorem for convex sets to derive constraints that guarantee collision avoidance between the ellipsoid-shaped drone and obstacles modeled as ellipsoids, spheres, or polygons. The resulting set of constraints is seamlessly integrated into the motion planning method based on Bezier curve whose properties are exploited for efficient evaluation of the constraints. The efficacy of the proposed method in generating feasible and collision-free trajectories is demonstrated via different simulations involving single and multiple drones.

Bathymetric simultaneous localization and mapping (SLAM) holds high potential for accurate navigation in long endurance autonomous underwater vehicle (AUV) missions. Due to nearly flat seabed topography, the occurrence of invalid loop closures is unavoidable in bathymetric SLAM. Thus, invalid loop closure identification is a crucial component of bathymetric SLAM, a procedure that normally requires a substantial amount of computational resources. An efficient bathymetric SLAM method considering invalid loop closures is proposed in this article, which identifies invalid loop closures via PF-Backend, a robust back end method based on particle filtering theory. Using this set-up, all beliefs of historical loop closures are estimated by a small number of particles and, for each particle, the corresponding map consistency calculated via a global graph optimizer is applied as the measurement update in the particle filter. A loop closure solidification procedure is also discussed to decrease the number of particles in the PF-Backend. The new algorithm is shown to be efficient and capable of providing accurate location and mapping results using play-back experiments.

This article addresses the problem of simultaneous distributed state estimation, and control of linear systems with linear state feedback, subjected to process, and measurement noise, under the constraints of quantized, and rate-limited network data transmission. In the set-up adopted, sensors and actuators communicate through a network with a strongly connected topology. Unlike the case of centralized linear systems, for which the separation principle holds, the above practical assumption prevents the separate design of observers, and controller because each of the nodes does not necessarily have access to the control inputs generated at all the other nodes. We derive a linear distributed Luenberger observer, and a set of sufficient conditions that guarantee ultimate boundedness of the estimation error, and system state vectors, with bounds that depend on the $\mathcal {L}_{\infty }$ norm of the noise signals, and the number of bits used in the transmissions. A numerical example illustrates the performance and effectiveness of the proposed algorithm in controlling a network of open-loop unstable systems.

In this paper we propose a method of cooperative navigation of a fleet of AUVs moving in formation with stringent limitations on the bandwidth of the underlying acoustic communications network. In the methodology proposed, one vehicle is equipped with a DVL and the others are equipped with acoustic modems and range-measuring devices that can only measure ranges to the remaining vehicles in the formation and to an underwater beacon or transponder located at a known position. Based on DVL and range measurements, and through the exchange of information among the vehicles, each vehicle must estimate its own position and the positions of the other vehicles in the fleet. The estimates obtained are then used for geo-referencing purposes as well as formation control. We use methods of distributed and quantised state estimation to solve this problem. We investigate through numerical simulations what type of information should be shared among the vehicles (range measurements or position estimates) for an efficient tradeoff between bandwidth and state estimation performance, which may lead to a better scalability of the formation.

Practical applications of drones are expanding into many new areas due to their fast-evolving technology. Looking further into the future, it is very likely that applications will require more than one drone to tackle a specific task, calling for reliable and efficient algorithms that can generate collision-free trajectories for multiple drones, under timing constraints of real-time applications. In this paper, we study a motion planning method based on the Bezier parametrization of spatial paths with a special focus on the less addressed issue in this method, (inefficient) constraint evaluation, that might hinder its use in real-time trajectory generation for multi-drone applications. We take advantage of the Bezier curves properties to obtain a small-scale optimization problem and find a finite set of inequalities that guarantee constraints satisfaction. We also propose a method to lower the conservatism in the resulting set of inequalities without the need to use unnecessary high-degree Bezier curves. Numerical results illustrate the efficacy of the presented method in reducing the computational costs associated with generating collision-free trajectories for multiple drones and re-planning them online with a receding horizon.

This work addresses the observability analysis for a cooperative range-based navigation system based on the optimization of an index. A nonlinear model is first defined in order to describe the motion of the vehicle and a mobile beacon. Then, the Fisher Information Matrix is introduced to explain how it is related with the observability problem. A unconstrained optimization problem is formulated in order to find the best sequence of actions for the beacon to ensure observability in the system; the unconstrained problem does not take into account physical limitation of the vehicle and beacon. Then, four different scenarios are solved using different constraints; we show that, when the beacon is rotating with variable angular velocity we get a better strategy than rotating with constant velocity, despite that in both scenarios the system is observable. Finally, we show that increasing the energy provided to rotate the beacon does not improve further the observability of the system. These results are important from a theoretical and practical point of view, since they represent a strategy to plan the motion of the beacon to guarantee observability in the system.

This paper addresses the problem of target localization with a single or multiple mobile trackers using range measurements from the trackers to the target. We consider three scenarios: i) the target is fixed, ii) the target’s velocity vector is unknown but constant, and iii) the target’s acceleration vector is unknown but constant. The main contributions of the paper are twofold: i) we derive a set of necessary and sufficient conditions on the motion of the trackers under which the target’s state, that might include the target’s position, velocity and acceleration vectors is globally observable, and ii) we show how the conditions derived lend themselves to an intuitive geometric interpretation that yields valuable guidelines to plan the tracker’s motion. Numerical simulations are included to confirm the conditions derived.

This study describes the development of a Monte Carlo based numerical analysis method to determine the instrumental uncertainty of dimensional measurements with wave gauges. The paper presents the adopted calculation procedure, namely, the mathematical model used in the determination of the amplitude and frequency of the wave gauge measurements. The results show a relative expanded (95%) target instrumental measurement uncertainty between 0.067 7 m and 0.068 2 m for the amplitude and between 0.592 4 Hz, and 0.592 8 Hz for the frequency. The study carried out allows for the conclusion that the use of a Monte Carlo-based numerical approach is suitable to evaluate the measurement uncertainty of parameters fitted to wave displacement analysis.

There are a wide range of techniques available for cooperative motion planning of autonomous vehicles. This paper focuses on one particular type of methods, known as Direct Methods, that solve motion planning expressed as a continuous-time optimal control problem. Out of all of the Direct Methods that were investigated, methods based on Bernstein polynomials were shown to provide the best results, namely, trajectories that prevent inter-vehicular and environmental collisions while satisfying vehicle constraints and keeping energy of temporal-related costs close to optimal.

Accurate time synchronization of the nodes of digital underwater acoustic (UWA) networks is necessary for the effective use of information obtained from various kinds of underwater sensors over a vast water area. The sensors of autonomous underwater vehicles (AUV) are widely used to address new challenges. One of them is time synchronization of sensors on mobile carriers, as well as synchronization of the clocks on mobile nodes of UWA networks, for example, teams of AUVs that perform coordinated and/or cooperated operations. The paper presents theresults of the experiments on using UWA modems for AUV positioning, namely, UWA modems with chip-scale atomic clocks (CSAC) that allow accurate measurements of delays in propagation of UWA signals and, thus, accurate estimation of ranges to their georeferenced sources. In addition, the accuracy of CSAC operation in various situations as well as positioning accuracy of AUVs having UWA modems with integrated CSACs are analyzed based on the experimental results. Also given are practical recommendations on how to “discipline” (steer) CSACs and provide their phase synchronization with a source of timekeeping signals.

Motivated by the increasing availability and quality of miniaturized sensors, computers, and wireless communication devices arid given their enormous potential, the use of wireless sensor networks (WSN) has become widespread. Because in many applications of WSNs one is required to estimate at each local sensor unit the state of a system given the measurements acquired by multiple sensors, there has been a flurry of activity related to the theory of distributed state estimation. This article contains a literature survey of distributed state estimation for discrete-time linear time invariant systems. In order to obtain the proper historical context, we review the state of the art in this field and summarize previous work. To provide the mathematical intuition behind some of the methods, this survey paper reproduces some of the main results given in the literature. It also provides a critical appraisal of the state of the art and affords the reader a comprehensive presentation of the most relevant results published so far. (C) 2019 Elsevier Ltd. All rights reserved.

This article presents a computational framework to efficiently generate feasible and safe trajectories for multiple autonomous vehicle operations. We formulate the optimal motion planning problem as a continuous-time optimal control problem, and approximate its solutions in a discretized setting using Bernstein polynomials. The latter possess convenient properties that allow to efficiently compute and enforce constraints along the vehicles’ trajectories, such as maximum speed and angular rates, minimum distance between trajectories and between the vehicles and known obstacles, etc. Thus, the proposed method is particularly suitable for generating trajectories in real-time for safe operations in complex environments and multiple vehicle missions. We show, using a rigorous mathematical framework, that the solution to the discretized optimal motion planning problem converges to that of the continuous-time one. The advantages of the proposed method are investigated through numerical examples.

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.

The general problem of Cooperative Control of multiple heterogeneous vehicles in the presence of severe communication constraints and external disturbances is pervasive in a large spectrum of scientific and commercial applications where groups of vehicles are required to carry out missions that cannot possible be achieved with a single vehicle [1] . It is important to stress that cooperation in the sense adopted here cannot be reduced to scheduling the actions of each and every vehicle in a group using a common time frame according to some pre-defined plan and dispatching specific sub-missions to each vehicle, which will then be asked to execute a specific task with only scarce information being exchanged among the group (.e.g. fast covering of the seabed by using a group of vehicles, each one of them in charge of acquiring visual and acoustic data over specific portions of the terrain). In the scope of many practical scientific and practical applications, far more complex missions must be addressed that require the concerted operation of groups of vehicles towards completion of a given goal (for example, cooperative motion control of ASVs and AUVs for acoustic and visual surveying). In this situation, the vehicles must necessarily exchange motion-related or environmental-related information parsimoniously and fulfil the mission objectives by adopting a distributed (non-centralized) decision architecture.

The past few years have seen a steady rise of public interest in ocean science and technology as evident through the United Nations proclaiming the coming decade as the United Nations Decade of Ocean Science for Sustainable Development. To appropriately address this challenge, it is essential to possess the right set of information dissemination tools. In this paper, we present a steady effort towards one such tool, called the Networked Marine Systems Simulator or NetMarSyS. Here, we describe a platform to model and visualize the behaviour of marine vehicles in three-dimensional space for surface and subsurface applications. The simulator is suitable for a wide range of applications, from a single-vehicle lawnmower survey to a complex multi-vehicle cooperative mission scenario. We use Robot Operating System, Unity3D game engine and WebSocket server technology to create a distributed multi-agent networked system where in multi-vehicle simulation is carried out. This allow us to support Hardware-in-the-Loop, Software-in-the-Loop simulations and real-time mission visualization. We highlight the competence of the visualizing software using an reference underwater-docking mission comprising of 2 vehicles, and one underwater structure.

The Sustainable Development Goals (SDGs), also known as the Global Goals, were adopted by all United Nations Member States in 2015 as a universal call to action to end poverty, protect the planet and ensure that all people enjoy peace and prosperity by 2030. All sectors of the society need to be involved in order to achieve those goals. This paper represents an initiative among several universities to explicitly look at the UN SDGs from the higher education perspective, in the field of marine systems and robotics, fostering self- reflection, developing specific actions addressing several goals. Currently half-way in the project life, the preliminary results and the activities performed up to now are very promising, bringing academia closer to the implementation of the Agenda 2030.

With the advent of cheap smart sensors installed on board marine vehicles and the increasing computational power of small embedded processors there is tremendous potential for the implementation of new strategies to control marine systems on the basis of input-output plant data. The emerging field of smart sensors affords a unique opportunity to have access to on-line measurement of dynamical systems’ variables seamlessly, at a low price. By applying a data-driven control algorithm to a marine vehicle, the paper introduces a new perspective on how data can be used in the control loop in marine systems. Classical control methodologies start by developing a model of the plant to be controlled, after which a number of control design techniques can be used. Recent advances in so-called model-free data-driven control methodologies, in particular unfalsified control, hold promise to merge the identification and control phases. Unfalsified control techniques build on the construction of a bank of controllers for a given plant, in which there exists at least one controller that meets the desired performance specification and a falsification unit. The latter is implemented using a cost function that directly evaluates the performance of the controllers (in and out of the feedback loop) using measured input and output data. At each sampling time, the performance of the controllers is assessed and the controllers that do not meet the pre-defined performance specification criteria will be falsified and removed from the bank of the controllers, after which an active controller will be selected among the unfalsified ones. In this paper, by presenting the results of the application of unfalsified control to the problem of Dynamic Positioning (DP) of marine vessels subjected to environmental forces, we aim to attract the attention of researchers in the field of marine control to the new perspective of using data to directly control marine system.

This paper presents a solution to the problem of multiple vehicle cooperative path following (CPF) that takes explicitly into account the constraints on the vehicles inputs and the topology of the inter-vehicle communications network. The solution involves decoupling the original constrained CPF problem into two sub-problems: i) single vehicle constrained path following and ii) multi-agent system (MAS) coordination. The first is solved by adopting a sampled-data model predictive control (MPC) scheme, whereas the latter is tackled by using a distributed control law with an event triggered communication (ETC) mechanism. We show that this design methodology yields a stable closed-loop CPF system: the path following error for each vehicle is globally asymptotically stable (GAS) and the coordination errors between the vehicles are bounded. A simulation example consisting of three autonomous vehicles following a given 2D- desired formation illustrates the efficacy of the CPF strategy proposed.

In many scientific and commercial missions at sea, it is of crucial importance to localize one ore more targets underwater. This objective meets with difficulties due to the unavailability of GPS underwater and the high cost of the equipment classically used for target localization. For these reasons, there has been a surge of interest in the problem of range-based underwater target localization, defined as that of localizing a group of unknown (fixed/moving) targets resorting to surface vehicles (called trackers) equipped with sensors capable of measuring their distances to the targets. In this setup, the surface sensor nodes should be placed according to some optimality criterion aimed at maximizing the information available for target tracking. In addition, the number of sensor nodes should be kept to a minimum in order to avoid the use of a complex and expensive network of vehicles that must be positioned and controlled in a coordinated manner. Motivated by these considerations, in this paper we study the problem of multiple target localization using two trackers and present a constructive method to generate a family of optimal waypoints for the trackers. The use of two surface sensors is motivated by the fact that a single surface sensor does not provide enough positioning accuracy for multiple target localization simultaneously. For two and three targets, we provide a family of analytical solutions for optimal sensor node placements. Further, the proposed solutions can be extended to more than three targets under some well-defined constraints on the target configuration. The optimal sensor positions are derived by maximizing the determinants of appropriately defined Fisher information matrices associated with each of the targets. It is shown that the optimal sensor configuration thus obtained lends itself to an interesting and useful geometrical interpretation. Simulation examples illustrate the results derived.

This paper presents a solution to the problem of path following control for autonomous marine vehicles (AMVs) subject to input constraints and constant ocean current disturbances. We propose two nonlinear control strategies: the first is obtained by using a Lyapunov-based design method, while the second is developed by adopting a Model Predictive Control (MPC) framework. We show that, with the proposed control strategies, the path-following error is globally asymptotically stable (GAS). Simulations with a kinematic model of the vehicle support the theoretical results. Simulations with a realistic model of the Medusa class of AMVs show the robustness of the proposed control strategies.

This paper presents a geophysical navigation system for small, affordable underwater robotic vehicles that exploits the information provided by geomagnetic features observed on the seafloor in order to correct the unavoidable drift in position error incurred by dead-reckoning navigation. The approach described combines a sequential estimation algorithm previous developed by the authors with a complementary profile correlation method. The outcome of this integration enhances the estimated position of a marine robotic vehicle in difficult operation conditions.

A spatio-temporal noise robust filtering method is proposed to separate 2D linear wave fields into incident and reflected irregular waves using water surface elevation gauges. The method is based on phased array methodologies, using delay-and-sum beamforming techniques with optimal noise attenuation. The method is developed for an arbitrary number of gauges and can be applied in real-time to surface elevation signals corrupted with measurement noise. The impact of the number of gauges and the distance between them on the performance achievable with the method is thoroughly analysed and a methodology to choose their optimum values is proposed. The robustness of the proposed method with respect to measurement noise is compared with the real-time method proposed by Frigaard and Brorsen and is shown to be adequate for noise levels higher than 1%. Separation errors smaller than 3% and 7% are obtained for simulated signals without noise and with 10% added noise, respectively.

We draw inspiration from the behavior of single-celled organisms to present a chemotaxis-inspired Adaptive Biased Random Walk (ABRW) guidance-control law for an Autonomous Underwater Vehicle (AUV). We build on previous results available in the literature to derive a random-walk based guidance-control law for an AUV to track-in and localize a potential chemical source in a turbulence-dominated environment. The ABRW-Strategy makes use of common plume-tracking and heuristic schemes for real-time path planning of the AUV. We further draw out a more comprehensive study of the guidance-strategy and extend the work for implementation in the Medusa class of vehicles that are developed in-house by Instituto Superior Tecnico (IST). The performance of the system is assessed via Hardware-in-the-loop (HIL) simulations to illustrate the viability of using random-walk for chemical source localization. The results obtained are encouraging for in-water tests with an autonomous vehicle of the Medusa class aiming at the validation of the proposed guidance-strategy in real-time experiments.

This paper proposes an event-triggered communication (ETC) mechanism to solve the nonlinear multi-agent system synchronization (coordination) problem for networks whose communication topologies are described by weight-balanced, strongly connected digraphs. To reduce communications among vehicles, each agent uses the dynamic model of its neighbors to estimate the states of its neighboring agents. In order to decide when each agent will broadcast its state, two types of event triggering functions are proposed: one that is purely time-dependent, and another, called state-dependent, that depends on the estimated states of the neighbors and the local information of the agent itself. We show that with the proposed ETC mechanism, coordination of a multi agent system is achieved. The proposed strategy also guarantees that the minimum of the inter-event times for every agent is strictly positive, hence Zeno behavior is excluded. The results are illustrated via simulations.

This chapter introduces an event-driven, logic-based communication system for decentralized control of a network of nonlinear systems (agents) with the objective of driving their outputs along predefined paths at desired speeds, while holding a desired formation pattern compatible with the paths. An extended cooperative path following (CPF) framework is adopted where communications among agents take place at discrete time instants, instead of continuously. The communication system takes into account explicitly the topology of the communications network, the fact that communications are discrete, and the cost of exchanging information among agents. The theoretical framework adopted allows for the consideration of communication losses and bounded delays. Conditions are derived under which the resulting multi-agent closed-loop system is input-to-state stable, that is stable and with guaranteed levels of performance in the presence of bounded external disturbances and measurement noise. The set-up derived is used to solve the problem of CPF control of multiple underactuated autonomous marine vehicles. The results of experimental field tests with a group of marine vehicles are presented and discussed.

We present a direct method for the solution of nonlinear optimal control problems based on Bernstein polynomial approximations. We show, using a rigorous setting, that the proposed method yields consistent approximations of time continuous optimal control problems. We demonstrate that the method can also be used for the estimation of optimal control problems costates. This result leads to the formulation of the Covector Mapping Theorem for Bernstein polynomial approximation. Finally, we exploit the numerical and geometric properties of Bernstein polynomials, and illustrate the advantages of the method through numerical examples.

We propose an integrated motion planning, geophysical navigation, and control (PNC) system for autonomous underwater vehicles (AUVs) that makes explicit use of geophysical information for both planning and navigation. The PNC system exploits the information provided by geomagnetic features observed on the sea-floor to correct the inevitable drift in the vehicle position estimates using dead-reckoning (DR). The three components of the PNC system are: a path planner that enhances the exploratory strength of the Rapidly-exploring Random Tree (RRT) algorithm with transition tests to choose new potential states, a sequential estimation algorithm, and a path following controller. The performance of the system is assessed via hardware in the loop (HIL) simulations to illustrate the significance of using magnetic data for navigation of AUVs. The results obtained are encouraging for inwater tests with an autonomous vehicle of the Medusa class aiming at the validation of the proposed combined system in real-time experiments

This paper presents a method for optimal trajectory planning with applications to drone cinematography. Aerial cinematography with drones is growing fast due to their maneuverability and ability to create unique visual effects. However, planning optimal drone trajectories and camera movements is still a major challenge to autonomous aerial filming. The trajectories must meet the objectives on aesthetic quality of the videos while satisfying several constraints imposed by drone dynamics, gimbal mechanical limits and surrounding obstacles. In this paper, we propose a novel formulation of the problem by decoupling the gimbal and the drone control systems. The problem is formulated as an optimization problem taking into account the gimbal rotation limits and collision avoidance constraints as well as camera angle driven objective functions to ensure feasible and smooth drone trajectories that generate visually pleasing videos. We evaluate the efficacy of our method through simulations and real-world experiments in an outdoor environment.

Multirotor unmanned aerial vehicles (UAVs) have experienced a very fast-paced technological development over the past years. Flight control systems have evolved from simple stability augmentation systems, barely enabling an external pilot to remotely fly a multirotor UAV, to full-fledged command augmentation systems, opening up the possibilities of autonomous operations of multirotors. Due to their small size, low cost, and high agility, multirotors draw a plethora of applications, including multiple vehicles operations, where the vehicles cooperate and jointly execute a mission, in constrained complex environments such as crowd monitoring and utility line inspection.

This paper addresses the problem of optimal acoustic sensor placement for underwater target localization in 3-D using range measurements only. By adopting an estimation theoretical framework, the optimal geometric sensor formation that will yield the best achievable performance in terms of target positioning accuracy is computed by maximizing the determinant of an appropriately defined Fisher information matrix (FIM). For mathematical tractability, it is assumed that the measurements of the ranges between the target and a set of acoustic sensors are corrupted with white Gaussian noise. For the sake of completeness, an explicit analytical result for a generic $n$ -sensor network is first obtained for the case when there is no uncertainty in the prior knowledge about the target position. The result is then extended to the practical case where the target is known to lie inside a region of uncertainty. The optimal sensor configuration thus obtained lends itself to an interesting and useful geometrical interpretation. In addition, the “spreading” of the configuration is shown to depend on the number of range measurements, target depth, and the probability distribution function that characterizes the prior knowledge about the target position. Results are also obtained for the problem of optimal sensor placement with constraints, namely, by considering that the sensors can be either located at the sea surface or distributed between the surface and the seabed. The connection between 2-D and 3-D scenarios is clarified. Simulation examples illustrate the key results derived.

Summary The work described in this paper is motivated by the need to develop efficient and robust estimation filters with application to terrain-aided navigation of underwater robotic vehicles. One of the main problems addressed is the development of navigation particle filters that can deal with the scarcity of landmarks and the terrain ambiguity that characterize vast regions of the ocean floor. As a contribution to solve this problem, the paper proposes three novel particle filter algorithms and assesses their estimation efficiency and robustness to non-informative measurements using two well-known benchmarking tests. The performance of the new filters in these tests demonstrates their potential to solve a class of nonlinear problems that include, but are not limited to, the type of underwater navigation problem that motivated the present work. Our study concludes by examining the performance of the filters in terms of determining the position and velocity of an autonomous underwater vehicle in the presence of unknown ocean currents. When applied to terrain-aided navigation, the novel particle filter formulations formulations mitigate filter divergence issues frequently caused by terrain symmetries and are more robust than other well-known versions when used in scenarios with poor terrain information. The theoretical developments presented and the results obtained in simulations are validated using real data acquired during tests with an autonomous marine robot. Copyright © 2016 John Wiley & Sons, Ltd.

This study evaluated sleep duration, travelling habits and jet lag effects in kite surfers according to their competition level. Ninety-four male kite surfers (34.3 ± 8.8 years) were evaluated through an online questionnaire in order to collect information on training volume, clinical history, anthropometric profile, sleep habits, fluid and fruit intake and jet lag effects on athletic performance. Mean sleep duration was 07 h 19 min ± 01 h 12 min on weekdays with 82.3% sleeping less than 8 h/night. Sleep duration was less on weekdays (p = 0.002) and weekends (p = 0.011) in kite surfers‘ squad members (SM) compared to no squad members (NoSM). Greater jet lag symptoms were reported following west-to-east flights. Kite surfers with SM arrived earlier at the competition destination (p = 0.019), were more likely to implement strategies to minimize travel effects (p = 0.003), but reported more symptoms of jet lag than did NoSM (p = 0.041). Travel effects were positively correlated with the distance trav...

We propose a numerical algorithm for multiple-vehicle motion planning that explicitly takes into account the vehicle dynamics, temporal and spatial specifications, and energy-related requirements. As a motivating example, we consider the case where a group of vehicles is tasked to reach a number of target points at the same time (simultaneous arrival problem) without colliding among themselves and with obstacles, subject to the requirement that the overall energy required for vehicle motion be minimized. With the theoretical setup adopted, the vehicle dynamics are explicitly taken into account at the planning level. This paper formulates the problem of multiple-vehicle motion planning in a rigorous mathematical setting, describes the optimization algorithm used to solve it, and discusses the key implementation details. The efficacy of the method is illustrated through numerical examples for the simultaneous arrival problem. The initial guess to start the optimization procedure is obtained from simple geometrical considerations, e.g., by joining the desired initial and final positions of the vehicles via straight lines. Even though the initial trajectories thus obtained may result in intervehicle and vehicle/obstacle collisions, we show that the optimization procedure that we employ in this paper will generate collision-free trajectories that also minimize the overall energy spent by each vehicle and meet the required temporal and spatial constraints. The method developed applies to a very general class of vehicles; however, for clarity of exposition, we adopt as an illustrative example the case of wheeled robots.

This paper addresses the problem of range-based autonomous underwater vehicle (AUV) localization in the presence of unknown ocean currents. In the setup adopted, the AUV is equipped with an attitude and heading reference system, a depth sensor, and an acoustic device that provides measurements of its distance to a set of stationary beacons. We consider the situation where the number of active beacons is not known in advance and may vary with time. The objective is to simultaneously localize the AUV and beacons, that is, to find their positions underwater. We start by deriving conditions under which it is possible to reconstruct the initial condition of the system under study. We consider the design model where the states evolve continuously with time, but the range measurements are only available at discrete instants of time, possibly in a nonuniform manner. For trimming maneuvers that correspond to AUV trajectories with constant linear and angular velocities expressed in the body frame, we show that if either the position of one of the beacons or the initial position of the AUV is known, then even without depth information the system is weakly observable (i.e., the set of states that are indistinguishable from a given initial configuration contains only a set of finite isolated points). If depth measurements are also available, then the system is observable even in the presence of unknown constant ocean currents. Equipped with these results, we then propose a novel observer for simultaneous AUV and beacon localization. The mathematical setup exploited borrows from minimum-energy estimation theory applied to continuous-time processes with discrete measurements, projection filters, and multiple-model estimation techniques. Convergence analysis of the resulting observer system yields conditions under which the estimation errors converge to a small neighborhood of the origin (whose size depends on the magnitude of the process and measurement noise). The results of field experiments with a robotic marine vehicle show the efficacy of the simultaneous AUV/multiple beacon localization system proposed.

In the classical set-up adopted for marine geotechnical surveys, one or more acoustic sources carried by a vessel emit signals that penetrate the seafioor, reflect or refract off geological features, and travel back to multiple acoustic receivers, or hydrophones, placed along cables (streamers) towed by the same vessel. Processing the data obtained by the hydrophones yields information about the physical properties of the Earth’s crust. In this set-up, the acoustic sources and receivers are coupled and the geometry of the latter is fixed. The EC WiMUST project aims to contribute to the development of a new breed of reflection seismology systems whereby the source and the receivers are physically decoupled and free to change their spatial configurations. The project puts forward a novel concept where shorter streamers are towed by autonomous marine vehicles (AMVs) that are required to maintain a possibly time-varying formation. The paper describes the first steps taken by 1ST, in cooperation with project partners, towards the development of the networked navigation and control systems of the WiMUST vehicles. We first address the problem of modeling a coupled streamer-vehicle system and propose a solution based on Kane’s method to obtain a dynamic simulation model. A brief description of the on-going work on model parameter identification is given. We then focus on the cooperative navigation problem and propose a simplified version of a nonlinear least squares approach previously described in the literature. Finally, we address the problem of cooperative control of a fleet of streamer-vehicle systems. We give an overview of cooperative path-following and target/trajectory tracking techniques and discuss possible adaptations to align them with the technical requirements imposed by the WiMUST concept. Preliminary simulation and experimental results are shown.

The paper addresses the problems of range-based marine vehicle positioning and target localization. Vehicle positioning aims to estimate the positions of one or more vehicles from a sequence of range measurements to fixed or moving acoustic beacons with known locations. In this context, the vehicles must execute sufficiently exciting maneuvers so as to maximize the range-based information available for multiple vehicle positioning. Using an estimation theoretical setting, the vehicle trajectories are computed by maximizing the determinant of a suitably defined Fisher information matrix (FIM), subject to inter-vehicle collision avoidance and vehicle maneuvering constraints. A numerical solution is proposed for the general case. Analytical solutions are obtained in the case of one vehicle and one beacon, when the latter undergoes trajectories that are straight lines, pieces of arcs, or a combination thereof. The theoretical analysis is complemented with practical experiments that focus on the dual problem of underwater target localization. The objective is to estimate the position of a moving underwater target by using range measurements between the target and a vehicle, called a tracker, undergoing a trajectory that can be measured on-line. The experimental set-up includes a surface and an autonomous underwater vehicle of the MEDUSA*-class playing the roles of tracker and target, respectively. In the methodology adopted for system implementation the tracker runs three key algorithms simultaneously, over a sliding time window: i) tracker motion planning, ii) tracker motion control, and iii) target motion estimation based on range data acquired on-line.

We address the problem of range-based marine vehicle positioning in the presence of unknown but constant ocean currents. The goal is to estimate the position of one or more vehicles from a sequence of range measurements to fixed or moving acoustic beacons with known locations. In contrast to most range-based positioning algorithms, we address the case where the currents are unknown and seek to estimate them explicitly as well. This increases the complexity of the problem at hand and raises interesting observability issues. In particular, the vehicles must undergo sufficiently exciting maneuvers so as to maximize the range-based information available for joint current/multiple vehicle position estimation. The main contribution of the paper is the computation of vehicle trajectories for range-based vehicle positioning system in the presence of constant, unknown currents by maximizing the determinant of a suitable Fisher information matrix (FIM), subject to collision avoidance and maneuvering constraints. A numerical solution is proposed for the general set-up of multiple vehicles and beacons. Analytical solutions are obtained for the case of one vehicle and one static beacon. The efficacy of the strategies proposed for vehicle trajectory optimization is shown by numerical simulations.

This paper describes the work developed towards the implementation of magnetic navigation (MAGNAV) methods for small, affordable underwater robotic vehicles. MAGNAV constitutes a particular implementation of a broader class of methods designated as geophysical navigation, which encompasses the already classical terrain-aided, or terrain-referenced, navigation approach. Relying on prior work by the authors, the paper proposes a particle filter navigation algorithm which may be combined with other position estimation methods to increase the robustness of the estimation process. The results of a series of tests performed in the water with a marine robotic vehicle in order to assess the impact of different sensor-vehicle configurations on the quality of the magnetic data used in MAGNAV are reported. The magnetic navigation solution proposed is validated in simulated trials using real magnetic and navigation data acquired with an autonomous marine vehicle in real trials using standard navigation sensors and an affordable total field magnetometer. The results obtained illustrate the high potential of the approach for geophysical navigation of autonomous underwater vehicles.

"CADDY - Cognitive Autonomous Diving Buddy" is an FP7 project that that is devoted to developing a cognitive underwater robotic system that will help divers during their activities in this hazardous environment. The envisioned resulting system will play a threefold role similar to those that a human buddy diver should have: buddy "observer", buddy "slave", and buddy "guide". During the second year of the project, validation trials were organized in Croatia with the purpose of testing all developed algorithms that will enable the three roles of the CADDY system. The trials were structured in five experiments. This paper is devoted to providing a concise overview of the conducted experiments and major results.

We address the general problem of multiple target localization and pursuit using measurements of the ranges from the targets to a set of autonomous pursuing vehicles, referred to as trackers. We develop a general framework for targets with models exhibiting uncertainty in the initial state, process, and measurement noise. The main objective is to compute optimal motions for the trackers that maximize the range-based information available for target localization and at the same time yield good target pursuit performance. The solution proposed is rooted in an estimation-theoretical setting that involves the computation of an appropriately defined Bayesian Fisher Information Matrix (FIM). The inverse of the latter yields a posterior Cramer–Rao Lower Bound (CRLB) on the covariance of the targets’ state estimation errors that can be possibly achieved with any estimator. Using the FIM, sufficient conditions on the trackers’ motions are derived for the ideal relative geometry between the trackers and the targets for which the range information acquired is maximal. This allows for an intuitive understanding of the types of ideal tracker trajectories. To deal with realistic constraints on the trackers’ motions and the requirement that the trackers pursue the targets, we then propose a model predictive control (MPC) framework for optimal tracker motion generation with a view to maximizing the predicted range information for target localization while taking explicitly into account the trackers’ dynamics, strict constraints on the trackers’ states and inputs, and prior knowledge about the targets’ states. The efficacy of the MPC is assessed in simulation through the help of representative examples motivated by operational scenarios involving single and multiple targets and trackers.

One of the key elements for the successful execution of multiple AUV missions is the availability of reliable and efficient cooperative motion planning systems. In a representative mission, this system should have the capability to generate collision-free trajectories from the AUVs’ initial positions to desired goal positions while optimizing some mission specific performance metric. The trajectories must also satisfy different constraints imposed by environmental conditions, AUV dynamics, and underwater communication limitations. This calls for the availability of algorithms that can deal with a large number of constraints and complex cost functions. In this paper we address the cooperative motion planning problem for a group of AUVs with time, energy, and active navigation constraints. We focus on two challenging problems. In the first problem the trajectories should guide the AUVs to a desired formation with minimum energy consumption, while in the second the information content of the path should be maximized for accurate single-beacon AUV positioning. We formulate the problems as optimal control problems to explicitly address the objectives and constraints. We employ the “direct multiple shooting” method along with a structure-exploiting optimization solver to efficiently solve the optimal control problem. Numerical results illustrate the efficacy of the methodology adopted for cooperative motion planning.

This papers addresses the problem of coverage path planning (CPP) for autonomous underwater vehicles (AUVs). The proposed offline planner employs a B-spline map representation of the target structure or region to be inspected. The method can be applied for both convex and non-convex shapes using an unified framework. The computed coverage path is described by a cubic B-spline curve that respects the kinodynamics constraints of a holomic vehicle. Due to the differentiability property of cubic B-splines, the desired velocity and the acceleration commands for the AUV are implicitly obtained from the coverage path. Simulation results show the planner performance in different mission scenarios.

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.

This article proposes a novel methodology for the detection of mooring line breakage in thruster assisted position mooring (PM) systems, when no measurements of the tensions on the mooring lines are available. For dynamic positioning (DP) of marine vessels moored to the seabed via a turret-based spread mooring system, thrusters provide only complementary assistance to the mooring system, which is responsible for generating a large part of the forces and moments required for station keeping. However, in extreme weather conditions thruster assistance is essential to avoid mooring line failure. Once a mooring line is parted, the remaining lines must withstand an increase in the tension forces required to compensate for the lost tension in the ruptured line. This in turn may lead to a cascade breakage of the mooring lines. Hence, it is of paramount importance to detect any line breakage as soon as it occurs to compensate for the lost tension by proper use of DP thruster assistance. As a contribution to solving this problem, in this paper we propose a methodology that builds on Dynamic Hypothesis Testing (DHT) whereby a set of hypotheses are assessed, at each sampling time, using the measured inputs and outputs of the thruster assisted position mooring system. While the first hypothesis corresponds to the assumption that all mooring lines are intact, the remaining hypotheses are built assuming that a single, or multiple line breakage events have taken place. At each sampling time, the inputs and outputs to the system are used to generate the conditional probability of each hypothesis being true. The conditional probabilities are then used to evaluate which hypothesis is more probable to be compatible with the collected measurements. In addition, we find conditions for any pair of hypothesis to be distinguishable. Numerical simulations, carried out using a high fidelity nonlinear PM simulator, illustrate the efficiency of the proposed methodology. Detection of mooring line failures using Dynamic Hypothesis Testing This work was supported in part by FCT [UID/EEA/50009/2013] and the European Commission under the H2020-ICT-2014 WiMUST Project (Grant Agreement No. 645141) and was the result of a collaborative effort between ISR/IST and AMOS; the Norwegian Research Council is acknowledged as the main sponsor of AMOS.

In many of the marine applications, the availability of an accurate system to localize underwater targets using a group of surface vehicles equipped with acoustic sensors is of paramount importance. In this context, the surface vehicles must keep a well-defined formation with respect to the targets so as to maximize the range-based information available for target localization. In this paper, a target position estimation algorithm together with a simple formation control law to maintain an optimal configuration that maximizes range-related information for target localization and tracking are presented. The setup studied consists of a group of surface vehicles moving in formation and a target performing a predefined mission. The optimal formation is computed by maximizing the determinant of an appropriately defined Fisher information matrix (FIM), subject to inter-vehicle collision avoidance and vehicle manoeuvring constraints. A centralized approach for the formation control is considered, where the trajectory planning is carried out by a supervisor that computes the control actions for each agent. The formation control is complemented with a collision avoidance logic based on a discrete event law. Simulations show that this strategy leads to an interesting and well defined behavior of the formation for localization and tracking.

The innovative usage of robotic vehicles for seismic surveying where, in sharp contrast to classical solutions, acoustic sources and streamers are carried by Autonomous Surface and Underwater Vehicles (ASVs & AUVs), respectively poses significant scientific and technical challenges. Among these, is the requirement of reducing the streamers length to improve the maneuverability and endurance of the towing vehicles without compromising the resolution of bottom images. A possible approach to tackle this challenge is to use vector sensors (VS) that are both compact and have high spatial discrimination performance. Such a sensing device - known as the Dual Accelerometer Vector Sensor (DAVS) - was designed, developed and tested in the framework of the WiMUST project. This work describes the experimental setup adopted and analyzes the data acquired during engineering trials carried out at the Lisbon Oceanarium marina, in Portugal, with a sparker mounted on an ASV and a DAVS mounted on a companion AUV. The Oceanarium marina is a very shallow site where the direct, surface and bottom reflected arrivals overlap in time. The results show that DAVS data can be used to attenuate noise and undesired surface reflections, and separate bottom and surface arrivals. Therefore, they suggest that a few VS can achieve a performance similar to that of longer hydrophone streamers and be advantageous in challenging survey areas where the traditional seismic setup can not be adopted.

This paper addresses the problem of optimal AUV (Autonomous Underwater Vehicle) motion generation to maximise the range-related information available for vehicle positioning subject to the condition that the overall trajectory will drive the vehicle from an initial to a final target point. To this end we resort to a task prioritisation technique using a null-space-based projection approach. This allows decoupling the motion objectives from those of maximising an observability based criterion. Preliminary results on this topic have been presented in previous work. Here, a careful singularity analysis of the null space projector operator is formalized and discussed. A saturation management algorithm that copes with vehicle velocity command saturation by properly assigning the velocity vector to the two tasks is also proposed. The stability of the resulting control law is analysed using a Lyapunov-based approach. Robustness against uncertainty in the initial position of the vehicle is also handled by comparing two alternative procedures: the first aims at optimising the worst case scenario, that is, maximising the minimum of the observability metric inside the uncertainty region; the second, a novel approach, aims at optimising the most frequent observability metric inside the uncertainty region. The work is completed by combining the guidance algorithm with an observer for the estimation of the vehicle's position and its associated uncertainty. Results of numerical simulations show the effectiveness of the proposed strategy.