Orientation estimation is a fundamental aspect of navigation and motion control of Autonomous Underwater Vehicles (AUVs). This concept is especially true when position sensors are unavailable and, consequently, navigation and control rely on dead reckoning strategies; in this case, orientation estimation is used in conjunction with speed measurements to update position estimation. When unknown magnetic disturbances are present, the magnetometers of the Inertial Measurement Unit (IMU) are unusable and do not provide an accurate initialization of the vehicle heading angle. This issue can be faced by applying a generalization of the Extended Kalman Filter in which the system state and measurements evolve on matrix Lie groups. The filter is used when the AUV is moving on the sea surface and it provides an estimate of the heading offset by comparing the speed measurements acquired by the Global Positioning System (GPS) and the Doppler Velocity Log (DVL) and by fusing the data coming from the IMU and the Fiber Optic Gyroscope (FOG). The initialization procedure has been validated with a dataset acquired by FeelHippo AUV in Cecina, Italy (September 2021).
A software framework, “ros_acomms,” has been developed to enable transport of ROS messages and other data across low-throughput and high-latency underwater acoustic links. Messages are efficiently marshalled using user-provided configuration data, if available, or automatically via message introspection. A modular set of modem drivers, media-access-control engines, and message queues transport messages from one system to another via a modem. It supports message fragmentation, positive acknowledgment, and custody-transfer routing. It also includes an acoustic link simulator that uses a raytracing model to estimate link performance and latency. While it targets the WHOI Micromodem family of acoustic modems, the modular modem driver implementation has been leveraged to support low-throughput Iridium satellite links and other acoustic modems. It has been tested and used operationally at sea for remote redirection of autonomous underwater vehicles while providing operators with near real time vehicle telemetry and sensor data.
Recent advances in differentiable rendering, which allow calculating the gradients of 2D pixel values with respect to 3D object models, can be applied to estimation of the model parameters by gradient-based optimization with only 2D supervision. It is easy to incorporate deep neural networks into such an optimization pipeline, allowing the leveraging of deep learning techniques. This also largely reduces the requirement for collecting and annotating 3D data, which is very difficult for applications, for example when constructing geometry from 2D sensors. In this work, we propose a differentiable renderer for sidescan sonar imagery. We further demonstrate its ability to solve the inverse problem of directly reconstructing a 3D seafloor mesh from only 2D sidescan sonar data.
Oceans preservation and protection have become increasingly relevant topics to tackle climate change. To this end, Autonomous Underwater Vehicles (AUVs) provide a useful means to carry out inspection and monitoring operations in full autonomy. A particular scenario in which AUVs are crucial involves the detection and mapping of underwater gas leaks, whether these are due to damaged offshore structures or naturally released from the seafloor. In this context, the proposed work investigates the effects of gas seeps on the navigation performance of AUVs. Indeed, the navigation of underwater vehicles mostly relies on acoustic sensors, as Doppler Velocity Log (DVL), which can be negatively affected by the presence of gas bubbles. The paper explores two solutions, based on two different acoustic sensors working at distinct frequencies: a DVL sensor and an Ultra-Short BaseLine (USBL) device. Both strategies have been implemented and tested during at-sea experiments, where gas leaks have been artificially reproduced. Results showed that both methods suffer from the presence of gas bubbles, causing erroneous DVL measurements and lost of USBL connectivity, respectively.
Underwater exploration and monitoring are particularly challenging for the absence of GPS, limited communications, high hydrodynamic pressure and harsh environmental conditions. Autonomous swarms of underwater robots can play a crucial role for missions such as wide area underwater exploration, environmental monitoring and inspection of existing engineering infrastructures, like oil and gas, and archaeological or historical sites, given their properties of scalability, robustness, flexibility, adaptability, enlarged perception and tasks’ parallelization. Driven by the need to understand the state of art and develop new solutions within the realization of a new swarm of underwater fishes 1 , we provide here a critical review of past and current projects of underwater swarms, focusing on sensors, mission tasks, algorithms, simulation environments and real life proofs of concept. Moreover, we analyze the research directions that can improve the impact of autonomous underwater swarms on environmental preservation and marine sustainable development, also considering the limiting factors imposed on these prospects. 1 This work is part of a new project “Heterogeneous Swarm of Underwater Autonomous Vehicles” funded by the Technology Innovation Institute and developed with Khalifa University, UAE
