Considering realistic characteristics of acoustic localization methods is crucial for roboticists when developing guidance and control algorithms for small and agile underwater robots. Current simulators either rely purely on geometric distancing, i.e. do not consider dynamic effects such as robot motion during acoustic signal propagation, or they are too complex for usage by non-communication experts and, thus, vulnerable to misconfiguration. We propose an open ROS-based framework that extends existing robot simulators (e. g. Gazebo) by simulating the effects of realistic acoustic ranging for underwater robot localization. Thus, our simulator enables realistic real-time analysis and evaluation of guidance, navigation, and control algorithms in software in-the-loop systems. For this purpose, we incorporate and encapsulate the non-trivial characteristics of acoustic communication and ranging such as robot motion during signal propagation, packet reception failure, and modem timings. This ensures the applicability of the tool by roboticists who are typically non-experts in acoustic communication and guarantees accurate and realistic simulation results. We demonstrate the functionality and performance of our framework and validate it on real-world experimental data on the example of a two-way ranging method. Our open-source release includes well-defined interfaces and parameters as well as a tutorial. This targets other roboticists who can either use our framework directly or easily adapt it to their individual setup, e. g., by adding further acoustic-ranging protocols.

Aerobatic quadrotors have been a very active field of research for the last two decades. Their huge community boosted the development of computational light-weight planning and control algorithms. In contrast and despite recent progress, research on agile micro autonomous underwater vehicles (µAUV) is still in its infancy. Both vehicle classes share a close relationship. They achieve high speeds of multiple bodylengths per second. At the same time they are subject to limited onboard resources such as sensors and computing power.In this work, we explore and exploit the potential synergies between aerobatic drones and hydrobatic µAUVs. In order to demonstrate the possible transfer of concepts we build on a state-of-the-art quadrotor trajectory planning framework and extend it to incorporate hydrodynamic effects. Furthermore, we study in a series of experiments the performance of the transferred concepts and show that various quadrotor simplifications match well for hydrobatic µAUVs.

Exploration and monitoring of hazardous fields in marine environments is one of the most promising tasks to be performed by fleets of low-cost micro autonomous underwater vehicles (μAUVs). In contrast to vehicles in other domains, underwater robots are forced to perform all computations onboard as no powerful communication links are available underwater. This puts the focus on computationally efficient field exploration algorithms. We propose CBTS-GMRF – an extremely light-weight tree-search exploration framework suitable for embedded computing. With our framework we build on recent work in POMDP-exploration and field belief representations based on efficient Gaussian Markov random fields (GMRF). We propose a reward function for energy-efficient field exploration together with a sparse trajectory parameterization. By reducing both, energy consumption and computational complexity, we enable underwater field exploration with μAUVs. We benchmark the performance of our exploration framework in simulation against state-of-the-art exploratory planning schemes and provide an experimental study using a low-cost micro diving agent. In order to support community-wide algorithm benchmarking, our code and robot design can be accessed online.

Despite the recent progress, guidance, navigation, and control (GNC) are largely unsolved for agile micro autonomous underwater vehicles (μAUVs). Hereby, robust and accurate self-localization systems which fit μAUVs play a key role and their absence constitutes a severe bottleneck in micro underwater robotics research. In this work we present, first, a small-size low-cost high performance vision-based self-localization module which solves this bottleneck even for the requirements of highly agile robot platforms. Second, we present its integration into a powerful GNC-framework which allows the deployment of μAUVs in fully autonomous mission. Finally, we critically evaluate the performance of the localization system and the GNC-framework in two experimental scenarios.

Navigation and control are a largely unsolved problems for micro autonomous underwater vehicles (μAUVs). The main challenges are due to the lack of accurate underwater localization systems, which fit on-board of μAUVs. In this work, we present an integrated navigation and control architecture consisting of a low-cost embedded localization module and an underwater way-point tracking controller, which fulfills the requirements of μAUVs. The performance of the navigation and control system is benchmarked in two different experimental scenarios.

Micro autonomous underwater vehicles (μAUVs) allow early-stage experimental testing even in small tanks. In contrast to large facilities, these tanks are usually not equipped with high-fidelity motion capture systems due to high cost and reduced required accuracy for proof-of-concept testing.In this work, we introduce low-cost and open-source motion capture architecture based on fiducial markers for small research tanks. We propose a highly modular approach which allows straight-forward adaptation to individual user needs. We demonstrate the performance of our architecture in two experimental setups.

Reinforcement learning (RL) allows robots to solve control tasks through interaction with their environment. In this paper we study a model-based value-function RL approach, which is suitable for computationally limited robots and light embedded systems. We develop a diving agent, which uses the RL algorithm for underwater depth stabilization. Simulations and experiments with the micro diving agent demonstrate its ability to learn the depth stabilization task.

We present the new HippoCampus micro underwater vehicle, first introduced in [1]. It is designed for monitoring confined fluid volumes. These tightly constrained settings demand agile vehicle dynamics. Moreover, we adapt a robust attitude control scheme for aerial drones to the underwater domain. We demonstrate the performance of the controller with a challenging maneuver. A submerged Furuta pendulum is stabilized by HippoCampus after a swing-up. The experimental results reveal the robustness of the control method, as the system quickly recovers from strong physical disturbances, which are applied to the system.

We present an approach for estimating the absolute poses of a swarm Micro Autonomous Underwater Vehicles (μAUVs) by decomposing the problem into few absolute position estimations and many relative pose estimations. As power constraints are critical to small mobile robots, we develop an extension of active marker pose estimation using color information to solve the marker correspondence problem, and show that this approach is more energy efficient than reflective estimation approaches. We show the feasibility of this approach by localizing a robot navigating in an underwater test tank environment. Detailed analysis is presented characterizing the noise and error properties when estimating robot poses from fixed on-board markers. Moreover, we provide comparisons in power and computational cost for other popular methods of underwater localization.

A hyperbolic acoustic system for underwater robot self-localization is presented. Anchored transducers send acoustic signals which are observed by a receiver. The system is passive with one-way signal transmission. Time differences of arrival (TDOAs) between the emitted signals are estimated by the receiver via cross-correlation. These TDOAs are fed to an Extended Kalman Filter to estimate the global position of the receiver. We describe the complete signal processing chain as well as challenges in hardware and software design. Experimental results in air and water show the feasibility of the system. This paper demonstrates that acoustic one-way localization is possible with off-the-shelf hardware in experimental test tanks.

The HippoCampus platform is a low-cost micro autonomous underwater vehicle for swarm robotics research. This paper presents the hardware and software design, the communication link, instrumentation, and control system. The quadrotor design enables the vehicle to perform agile maneuvers in a very confined test tank. Vehicle navigation is based on the on-board sensor suite. Autonomous path following is presented and experimental results are evaluated.