Formation stability analysis of unmanned multi-vehicles under interconnection topologies

In this paper, the overall formation stability of unmanned multi-vehicle is mathematically presented under interconnection topologies. A novel definition of formation error is first given and followed by the proposed formation stability hypothesis. Based on this hypothesis, a unique extension-decomposition-aggregation scheme is then employed to support the stability analysis for the overall multi-vehicle formation under a mesh topology. It is proved that the overall formation control system consisting of N number of nonlinear vehicles is not only asymptotically, but also exponentially stable in the sense of Lyapunov within a neighbourhood of the desired formation. This technique is shown to be applicable for a mesh topology but is equally applicable for other topologies. Simulation study of the formation manoeuvre of multiple Aerosonde UAVs, in 3D-space, is finally carried out verifying the achieved formation stability result.

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs

Perspectives of auto-correcting lens distortions in mosaic-based underwater navigation

When unmanned underwater vehicles (UUVs) perform missions near the ocean floor, optical sensors can be used to improve local navigation. Video mosaics allow to efficiently process the images acquired by the vehicle, and also to obtain position estimates. We discuss in this paper the role of lens distortions in this context, proving that degenerate mosaics have their origin not only in the selected motion model or in registration errors, but also in the cumulative effect of radial distortion residuals. Additionally, we present results on the accuracy of different feature-based approaches for self-correction of lens distortions that may guide the choice of appropriate techniques for correcting distortions

A review of sensor fusion techniques for underwater vehicle navigation

This work provides a general description of the multi sensor data fusion concept, along with a new classification of currently used sensor fusion techniques for unmanned underwater vehicles (UUV). Unlike previous proposals that focus the classification on the sensors involved in the fusion, we propose a synthetic approach that is focused on the techniques involved in the fusion and their applications in UUV navigation. We believe that our approach is better oriented towards the development of sensor fusion systems, since a sensor fusion architecture should be first of all focused on its goals and then on the fused sensors

Using petri nets to specify and execute missions for autonomous underwater vehicles

This paper presents the design and implementation of a mission control system (MCS) for an autonomous underwater vehicle (AUV) based on Petri nets. In the proposed approach the Petri nets are used to specify as well as to execute the desired autonomous vehicle mission. The mission is easily described using an imperative programming language called mission control language (MCL) that formally describes the mission execution thread. A mission control language compiler (MCL-C) able to automatically translate the MCL into a Petri net is described and a real-time Petri net player that allows to execute the resulting Petri net onboard an AUV are also presented

Visual SLAM for 3D large-scale seabed acquisition employing underwater vehicles

This paper presents a novel technique to align partial 3D reconstructions of the seabed acquired by a stereo camera mounted on an autonomous underwater vehicle. Vehicle localization and seabed mapping is performed simultaneously by means of an Extended Kalman Filter. Passive landmarks are detected on the images and characterized considering 2D and 3D features. Landmarks are re-observed while the robot is navigating and data association becomes easier but robust. Once the survey is completed, vehicle trajectory is smoothed by a Rauch-Tung-Striebel filter obtaining an even better alignment of the 3D views and yet a large-scale acquisition of the seabed

Visual SLAM for underwater vehicles using video velocity log and natural landmarks

A visual SLAM system has been implemented and optimised for real-time deployment on an AUV equipped with calibrated stereo cameras. The system incorporates a novel approach to landmark description in which landmarks are local sub maps that consist of a cloud of 3D points and their associated SIFT/SURF descriptors. Landmarks are also sparsely distributed which simplifies and accelerates data association and map updates. In addition to landmark-based localisation the system utilises visual odometry to estimate the pose of the vehicle in 6 degrees of freedom by identifying temporal matches between consecutive local sub maps and computing the motion. Both the extended Kalman filter and unscented Kalman filter have been considered for filtering the observations. The output of the filter is also smoothed using the Rauch-Tung-Striebel (RTS) method to obtain a better alignment of the sequence of local sub maps and to deliver a large-scale 3D acquisition of the surveyed area. Synthetic experiments have been performed using a simulation environment in which ray tracing is used to generate synthetic images for the stereo system

Distributed protocol for communications among underwater vehicles

Underwater surveying by swarms of autonomous underwater vehicles presents problems in communication among the robots. These problems involve the bandwidth, power consumption, timing, processing power, and other issues. This paper presents a novel approach to communicate and coordinate effectively among underwater vehicles to accomplish this task successfully. The proposed approach solves issues by reducing the number of hops to conserve power, while reducing computation time and bandwidth, effectively utilizing resources to reduce the load on each node. Finally, the simulation results are presented, in order to prove that the proposed approach improves efficiency and effectiveness in communicating among underwater vehicles.

Autonomous underwater vehicles in a hazardous non-deterministic environment

This research investigated the cooperation of multi underwater robots to perform a task. This combined engineering design, electronics and consensus control to create systems capable of achieving the task. Challenges such as underwater radio communications were researched and a simulation framework was created and tested on virtual and real systems.

A new perspective of storm bite on sandy beaches using unmanned aerial vehicles

Aerial imagery collected before and after major storm events is ideal for the assessment of coastal landscape change driven by individual high-magnitude events. Using traditional satellite sensors and manned aerial systems can be challenging due to issues related to cloud cover, mobilization expenses and resolution. Rapid advances in unmanned aerial vehicle (UAV) technology allow for the cost-effective collection of aerial imagery and topography at centimetre resolution suitable for assessing change in coastal ecosystems. In this study we demonstrate the utility of UAV-based photogrammetry to quantify storm-driven sediment dynamics on a sandy beach on the open-coast shoreline of Victoria, Australia. UAV-based aerial photography was collected before and after a major storm event. High-resolution (< 5 cm) aerial imagery and digital surface models were acquired and change-detection techniques were applied to quantify changes in the beachface. An average beach erosion of 12.24 m3/m with a maximum of 28.05 m3/m was observed, and the volume of sand cut from the beachface and retreat of the foredune are clearly illustrated. Following the storm event, erosion was estimated at 7259.94 ± 503.69 m3 along 550 m of beach. By combining the aerial imagery and derived topographic datasets we demonstrate the advantage of UAV-based photogrammetry techniques for rapid high-resolution data collection in semi-remote locations. Its utility in setting unlimited virtual vantage points is also illustrated and the valuable perspective it provides for tracking landscape change discussed.

UAVNet: A Mobile Wireless Mesh Network Using Unmanned Aerial Vehicles

We developed UAVNet, a framework for the autonomous deployment of a flying Wireless Mesh Network using small quadrocopter-based Unmanned Aerial Vehicles (UAVs). The flying wireless mesh nodes are automatically interconnected to each other and building an IEEE 802.11s wireless mesh network. The implemented UAVNet prototype is able to autonomously interconnect two end systems by setting up an airborne relay, consisting of one or several flying wireless mesh nodes. The developed software includes basic functionality to control the UAVs and to setup, deploy, manage, and monitor a wireless mesh network. Our evaluations have shown that UAVNet can significantly improve network performance.

The use of unmanned aerial vehicles and wireless sensor networks for spraying pesticides

The application of pesticides and fertilizers in agricultural areas is of crucial importance for crop yields. The use of aircrafts is becoming increasingly common in carrying out this task mainly because of their speed and effectiveness in the spraying operation. However, some factors may reduce the yield, or even cause damage (e.g., crop areas not covered in the spraying process, overlapping spraying of crop areas, applying pesticides on the outer edge of the crop). Weather conditions, such as the intensity and direction of the wind while spraying, add further complexity to the problem of maintaining control. In this paper, we describe an architecture to address the problem of self-adjustment of the UAV routes when spraying chemicals in a crop field. We propose and evaluate an algorithm to adjust the UAV route to changes in wind intensity and direction. The algorithm to adapt the path runs in the UAV and its input is the feedback obtained from the wireless sensor network (WSN) deployed in the crop field. Moreover, we evaluate the impact of the number of communication messages between the UAV and the WSN. The results show that the use of the feedback information from the sensors to make adjustments to the routes could significantly reduce the waste of pesticides and fertilizers.