Wireless communications increasingly enable ubiquitous connectivity for a large number of nodes, applications and scenarios. One of the less explored scenarios is aquatic communications, specially when considering near-shore and short-range communications. Overwater communications are impaired by a number of distinguishing dynamic factors, such as tides, waves or node mobility, that lead to a widely fluctuating and unpredictable channel. In this work, we empirically characterize near-shore, overwater channels at 2.4 GHz under realistic conditions, including tidal variations, and relatively short TX-RX separations. To this end, we conducted experiments in a coastal estuarine region and on a harbor to characterize Shore-to-Shore (S2S) and Shore-to-Vessel (S2V) communication channels, respectively, and to identify major factors impairing communication in such scenarios. The empirical results show that constructive/destructive interference patterns, varying reflecting surface, and node mobility (i.e. travel direction and particular maneuvers) have a relevant and noticeable impact on the received signal strength. Thus, a set of parameters should be simultaneously considered for improving the performance of communication systems supporting S2S and S2V links, namely tidal variations, reflection surface changes, antenna height, TX-RX alignment and TX-RX separation. The results useful provide insights into realistic S2S and S2V link design and operation.

This paper addresses the source localization problem of an acoustic fish-tag using the Time-of-Arrival measurement of an acoustic signal, transmitted by the fish-tag. The Time-of-Arrival measurements denote the pseudo-range information between the acoustic receiver and the fish-tag, except that the Time-of-Transmission of the acoustic signal is unknown. Starting with the pseudo-range measurement equation, a globally valid quasi-linear time-varying measurement model is presented that is independent of the Time-of-Transmission of the acoustic signal. Using this measurement model, an Uniformly Globally Asymptotically Stable (UGAS), three stage estimation strategy (eXogenous Kalman Filter) is designed to estimate the position of an acoustic fish-tag and evaluated against a benchmark Extended Kalman Filter based estimator. The efficacy of the developed estimation method is demonstrated experimentally, in presence of intermittent observations using an array of receivers mounted on three Unmanned Surface Vessels (USVs).

We present Dolphin, an extensible programming language for autonomous vehicle networks. A Dolphin program expresses an orchestrated execution of tasks defined compositionally for multiple vehicles. Building upon the base case of elementary one-vehicle tasks, the built-in operators include support for composing tasks in several forms, for instance according to concurrent, sequential, or event-based task flow. The language is implemented as a Groovy DSL, facilitating extension and integration with external software packages, in particular robotic toolkits. The paper describes the Dolphin language, its integration with an open-source toolchain for autonomous vehicles, and results from field tests using unmanned underwater vehicles (UUVs) and unmanned aerial vehicles (UAVs).

Mission planning and execution for autonomous vehicles is crucial for their effective and efficient operation during scientific exploration, or search and rescue missions, to mention a few. Automated Planning has shown to be a useful tool for “high level” mission planning, that is, allocating tasks to vehicles while following given constraints (e.g., energy, collision avoidance). In this paper, we focus on making mission planning flexible and robust. That is, a human mission coordinator can modify tasks during the mission execution, so the tasks have to be dynamically reallocated during the process. Moreover, we assume that communication might not be reliable when vehicles are “outside”, i.e., performing the tasks, and thus we enforce vehicles to come back to their safe spots regularly. To address these requirements, we have developed two models, namely “all tasks” and “one round”, and integrated them to the control software. We have evaluated our approach in a field experiment focused on a mine-hunting scenario.

Supervision and control of Autonomous underwater vehicles (AUVs) has traditionally been focused on an operator determining a priori the sequence of waypoints of a single vehicle for a mission. As AUVs become more ubiquitous as a scientific tool, we envision the need for controlling multiple vehicles which would impose less cognitive burden on the operator with a more abstract form of human-in-the-loop control. Such mixed-initiative methods in goal-oriented commanding are new for the oceanographic domain and we describe the motivations and preliminary experiments with multiple vehicles operating simultaneously in the water, using a shore-based automated planner.

Obtaining synoptic observations of dynamic ocean phenomena such as fronts, eddies, oxygen minimum zones and blooms has been challenging primarily due to the large spatial scales involved. Traditional methods of observation with manned ships are expensive and, unless the vessel can survey at high-speed, unrealistic. Autonomous underwater vehicles (AUVs) are robotic platforms that have been making steady gains in sampling capabilities and impacting oceanographic observations especially in coastal areas. However, their reach is still limited by operating constraints related to their energy sources. Unmanned aerial vehicles (UAVs) recently introduced in coastal and polar oceanographic experiments have added to the mix in observation strategy and methods. They offer a tantalizing opportunity to bridge such scales in operational oceanography by coordinating with AUVs in the water-column to get in-situ measurements. In this paper, we articulate the principal challenges in operating UAVs with AUVs making synoptic observations for such targeted water-column sampling. We do so in the context of autonomous control and operation for networked robotics and describe novel experiments while articulating the key challenges and lessons learned.

Multiple UAVs can be used to cover a region effectively. Area coverage involves two stages - area decomposition into cells and path planning inside the cells. The area is decomposed using sweeping technique. For path planning inside the cells, a novel method is developed where optimal number of lanes are generated to minimize the number of UAV turns to accomplish the mission in minimum time. Also to allow operator interaction and precedence in coverage, a novel lawnmower/Zamboni planner is designed. Simulation results are presented for multiple UAVs are presented and flight test results for one UAV performing a persistent mission is presented.

A fundamental knowledge of the trade-off between sensor cooperation and autonomous vehicles' (AV) trajectory planning is pivotal towards characterizing the sensing capabilities of wireless sensor networks that employ AV for collecting the data and to ensure their successful integration in large scale sensor deployments. We formulate the problem of efficient data gathering as a mixed integer linear programming (MILP) problem that provides a joint optimization of AV trajectory and data-routing. Since MILP formulations are not scalable, we propose an approach to develop heuristics where the joint optimization is decoupled into three sub-problems. The first is to determine clusters of sensors with communication range limitations. The second is to efficiently connect the clusters. The third is to design the route inside the cluster that will minimize the cost of data collection. We characterize performance of the proposed heuristics through Monte-Carlo simulations. Performance is measured in terms of (a) the joint energy cost for cooperation and AV movement for different number of sensor nodes and communication ranges of these sensors, and (b) computational effort of the various heuristics. For small deployments, we compare the heuristics to the MILP global optimization and show that the gap between them can be lower than 2% for deployments as large as 18 nodes and typically below 25% for a wide range of scenarios.

In the video we present results obtained from experiments we ran with multiple unmanned vehicles. The vehicles used in the experiments were made in the LSTS, which is introduced in the beginning of the video. A formation control problem using an Autonomous Surface Vehicle and an Autonomous Underwater Vehicle is presented first, then followed by a multiple Unmanned Aerial Vehicles control problem. After both problems an approach is proposed and results are presented.

Autonomous Vehicles (AV) are used to solve the problem of data gathering in large scale sensor deployments with disconnected clusters of sensors networks. Our take is that an efficient strategy for data collection with AVs should leverage i) cooperation amongst sensors in communication range of each other forming a sensor cluster, ii) advanced coding and data storage techniques for easing the cooperation process, and iii) AV route-planning that is both content and cooperation-aware. Our work formulates the problem of efficient data gathering as a cooperative route-optimization problem with communication constraints. We also analyze (network) coded data transmission and storage for simplifying cooperation amongst sensors as well as data collection by the AV. Given the complexity of the problem, we focus on heuristic techniques, such as particle swarm optimization, to calculate the AV's route and the times for communication with each sensor and/or cluster of sensors. We analyze two extreme cases, i.e., networks with and without intra- cluster cooperation, and provide numerical results to illustrate that the performance gap between them increases with the number of nodes. We show that cooperation in a 100 sensor deployment can increase the amount of data collected by up to a factor of 3 with respect to path planning without cooperation.

In this paper, we present a navigation function based approach for docking onto a moving submarine. Docking of a AUV is a difficult problem due to currents and the presence of no fly zones around the submarine. The navigation function approach provides an integration of no-fly zones into the controller and determines the desired heading taking the kinematic constraints of the AUV into account. Simulations are presented to validate the navigation functions based docking solution.

In a search and prosecute mission, multiple heterogeneous unmanned aerial vehicles UAVs that carry different resources need to perform the classify, prosecute and battle damage assessment (BDA) tasks on targets sequentially. Depending on the target resource requirement, it may be necessary to deploy a coalition of UAVs to perform the action. In this paper, we propose coalition formation algorithms that have low computational overhead to determine coalitions for the prosecute and the BDA tasks. We also develop a simultaneous strike mechanism based on Dubins curves for the UAVs to prosecute the target simultaneously. Monte-Carlo simulation results are presented to show how the algorithms work and the effect of increasing the number of BDA tasks on the mission performance.

This video shows a brief overview over Neptus, a command and control infrastructure for heterogeneous teams of autonomous vehicles. Having different types of vehicles at our laboratory and from our partners, there was an increasing need to create a common infrastructure to all these systems. Additionally, a tool to support the entire mission life cycle (planning, execution, review and dissemination) was lacking. Neptus was created to provide vehicle independence and seamless inter-systems communications. Currently, Neptus has been already tested with remotely operated vehicles, autonomous underwater vehicles, unmanned air vehicles, autonomous surface vehicles and wireless sensor networks. Some of these systems were operated simultaneously by various operating consoles that were sharing the same communication infrastructure. The received data was being relayed to a Web server that allowed for the real-time mission following by using a common Web browser.

This paper presents the Mission Review and Analysis module of a C3I (Command, Control, Communication and Information) infrastructure - the Neptus framework. This is a mix-initiative environment that it's being developed in the Underwater Systems and Technology Laboratory (USTL/LSTS) with the goal to support the coordinated operation of heterogeneous teams of vehicles. This includes autonomous and remotely operated underwater, surface, land, and air vehicles and people. People perform a fundamental role, not only in the case of remotely operated vehicles, but also with autonomous vehicles where mix-initiative operation is a requirement. The Neptus is very modular and will be heavily based on services with a distributed architecture. This paper focus mainly on the mission review and analysis where the data collected in a mission is prepared for analysis.

The C3I (command, control, communication and information) Neptus framework which is being developed at the Underwater Systems and Technology Laboratory (USTL/LSTS) is presented. Neptus is a modular mixed initiative framework (human operators in the control loop) for the operation of heterogeneous teams of vehicles such as autonomous and remotely operated underwater, surface, land, and air vehicles. Neptus is composed of mission and vehicle planning, supervision, and post-mission analysis modules which are provided as services across a network. This paper focus mainly on the mission definition module with the presentation of MDL - a XML based language for mission definition

This paper addresses the design of a hybrid feedback control system enabling a car-like, rectangular vehicle to perform a tight L-shaped turn manoeuvre. This case constitutes an effort towards the definition of a general purpose methodology for hybrid feedback control synthesis. The design effort encompasses the synthesis of a set of modalities and mechanisms to detect relevant events enabling the overall motion coordination so that a global goal is attempted. Simulations of the implemented controller have shown a strong robustness with respect to modeling uncertainty and execution errors.

This paper describes a systems engineering approach to the design of an integrated decision support system, IDES, for the planning and control of ATMA, a Portuguese textile company. The paper presents the overall process emphasizing the methodological framework and the main results. Systems engineering methods were crucial in the development of a methodological framework adapted to the specificities of the Portuguese industry. This framework proved invaluable in the process of mapping a conceptual planning and control architecture based on dynamical optimization onto the design of the IDES according to the recommendations of the strategic plan of ATMA. Concepts and techniques from control systems, dynamic optimization, software engineering, enterprise modeling and anthropocentric systems were used in this process.

This paper presents a general architecture for the control of multiple vehicles based on the concept of generalized vehicle. This embodies an extension of the dynamically reconfigurable control architecture previously proposed by the authors (1994, 1995). Besides the capabilities of each vehicle inherent to the individual autonomy, the set of vehicles may be seen providing a pool of functionalists whose reconfiguration enables the emergence of a global features overcoming the single vehicle limitations. Unlike some other approaches, cooperation does not take place only at a deliberative level, but it results from a global coordination level implemented via an adequate protocol specified in the the global mission plan. The design of this architecture was driven by requirements extracted from the analysis of operational scenarios involving multiple vehicles.

In this article, we describe the analysis, design and implementation of a control architecture for a mobile platform to autonomously carry out transportation, surveillance and inspection tasks in semi-structured industrial environments. Based on a hierarchical structure composed by the organization, coordination and functional levels organized linguistically and structured according to the principle of increasing precision with decreasing intelligence, this control architecture permits the real-time parallel execution of tasks.