The rise of lightweight, low-cost underwater vehicle-manipulator systems (UVMS) has made autonomous underwater manipulation increasingly accessible. Yet, most current research remains limited to isolated tasks, such as trajectory tracking or compensation of unknown payloads. Detailed experimental analyses that go beyond a proof-of-concept are particularly rare.We present a comprehensive open-source software framework for fully automated pick-and-place studies. We build upon our previous work on a task-priority control framework and extend it to enable fully autonomous manipulation. This includes a high-level decision-making process to coordinate the pick-and-place sequence and a grasp detection method to verify the successful pick-up of the object. We demonstrate this framework on the widely-used platform of a BlueROV2 and an Alpha 5 manipulator.Extensive quantitative experimental studies (100+ trials) show the picking and placing to be highly accurate, with mean position errors of <5 mm and <10 mm, respectively. We additionally validate our grasp detection approach and analyze trajectory tracking sensitivity to varying payloads and speeds. These results provide a baseline of what accuracy is currently achievable with state-of-the-art lightweight hardware under ideal research conditions. The code is available at https://github.com/HippoCampusRobotics/uvms.

Similarly to aerial drones, small-scale underwater robots are prone to external wrenches resulting from disturbances such as water currents or collisions. Estimating the external wrench acting on an underwater robot is challenging due to non-linear hydrodynamic effects and the bottleneck of being limited to onboard sensing.We build on a model-based approach for aerial wrench estimation and extend it to the underwater domain. Various modifications are applied, such as capturing hydrodynamic effects, and new sensory information is integrated, for example, via Doppler velocity log (DVL).We evaluate the performance of the proposed approach through a series of experiments. Moreover, we assess the effect of fusing various sensor configurations and their respective influence on the wrench estimate, including low-cost vs. high-end IMU and DVL. Our adapted approach from the aerial domain delivers good results in estimating external wrenches on underwater robots. While the IMU quality is found to be less important, considering the underwater domain-specific damping terms is critical.

This work investigates the use of multiple Autonomous Surface Vehicles (ASVs) as Communication/Navigation Aids (CNAs) to enhance the navigation and state estimation of an Autonomous Underwater Vehicle (AUV). Our approach builds on recent advancements in low-cost sensors and platforms, which enable novel AUV applications across fundamental science, commercial industries, and defense. We consider six different combinations of Kalman Filter and Factor Graph localization solutions on three datasets, covering 53 minutes and 3.1 kilometers of operation. We first present the solution using the measurements from all three ASVs, before occluding measurements from two of the ASVs to assess the effect of reduced observability on localization performance.

Time-effective and accurate source localization with mobile robots is crucial in safety-critical scenarios, e.g. leakage detection. This becomes particular challenging in realistic cluttered scenarios, i.e. in the presence of complex current flows or wind. Traditional methods often fall short due to simplifications or limited onboard resources.We propose to combine source localization with a Gaussian Markov Random Field (GMRF). This allows to improve source localization hypotheses by building on the GMRF’s concentration and flow field belief that are continuously updated by gathered measurements. We introduce the upstream source proximity (USP) as a natural metric that exploits the joint knowledge represented in the field belief’s concentration and flow field, i.e. predicting sources upstream. As a result, our method yields a computationally efficient source localization and field belief module providing substantially more stable gradients than conventional concentration gradient-based methods.We demonstrate the suitability of our approach in a series of numerical experiments covering complex source location scenarios. With regard to computational requirements, the method achieves update rates of 10Hz on a RaspberryPi4B.

Environmental monitoring via UAVs offers unprecedented aerial observation capabilities. However, the limited flight durations of typical multirotors and the demands on human attention in outdoor missions call for more autonomous solutions. Addressing the specific challenges of precise UAV landings – especially amidst wind disturbances, obstacles, and unreliable global localization – we introduce a mobile hub concept. This hub facilitates continuous mission cycling for unmodified off-the-shelf UAVs. Our approach centers on a small landing platform affixed to a robotic arm, adeptly correcting UAV pose errors in windy conditions. Compact enough for installation in an economy car, the system emphasizes two novel strategies. Firstly, external visual tracking of the UAV informs the landing controls for both the drone and the robotic arm. The arm compensates for UAV positioning errors and aligns the platform’s attitude with the UAV for stable landings, even on small platforms under windy conditions. Secondly, the robotic arm can transport the UAV inside the hub, perform maintenance tasks like battery replacements, and then facilitate direct relaunches. Importantly, our design places all operational responsibility on the hub, ensuring the UAV remains unaltered. This ensures broad compatibility with standard UAVs, only necessitating an API for attitude setpoints. Experimental results underscore the efficiency of our model, achieving safe landings with minimal errors (≤ 7 cm) in winds up to 5 Beaufort (8.1 m/s). In essence, our mobile hub concept significantly boosts UAV mission availability, allowing for autonomous operations even under challenging conditions.

Confined and cluttered aquatic environments present a number of significant challenges with respect to inspection by robotic platforms, including localisation and communications. Some of these can be mitigated by using collaborative heterogeneous multi-robot teams. An important element of such a system is collaborative control. This paper addresses this challenge by presenting an Image-Based Visual Servoing (IBVS), leader-follower control system for heterogeneous aquatic robots. Experiments were conducted in an uncluttered pond to demonstrate the capabilities of the system. The results show robots can maintain tracking each other with maximum$x$and$y$displacements of 0.42 m and 0.41 m, the maximum projection distance in the xy-plane of maintaining formation is 0.45 m, showing the stability and feasibility of deploying such system on underwater platforms.

Exploration and monitoring of hazardous environments, such as legacy nuclear storage ponds, constitute safety-critical missions to be performed by small-scale underwater robots. These monitoring tasks require fully actuated robot platforms in order to allow for hovering while inspecting objects of interest in detail. A severe bottleneck arises from the restricted access points commonly encountered in these surveillance missions that pose strict limitations on the vehicle dimensions. However, small-scale underwater robots usually possess underactuated propulsion systems and are, thus, only partially suitable for these missions. In this work, we investigate and exploit the idea of reconfigurability. Following the idea of the whole is more than the sum of its parts, we daisy-chain multiple small-scale underwater robots with revolute joints to enable shape reconfigurations. In combination with a centralized sliding mode control scheme, the robot platform is able to change its shape depending on the current task, see Fig. 1. While the straight configuration fits well through tight passages such as inspection holes, the robot can reconfigure itself towards a triangular shape that enables the neutrally buoyant robot to hover at areas of interest, e. g. for inspection tasks. Finally, we examine our proposed concept in a series of simulations and experiments. Moreover, we demonstrate the performance of key elements such as reconfiguration and navigation, discuss their limitations, and point out future directions.

Accurate localization systems are still a bottleneck for Unmanned Underwater Vehicles (UUVs). In recent years, fiducial markers have become a readily available, low-cost option. However, an in-depth analysis of marker detection accuracy in the underwater domain has yet to be performed. We propose a methodology to evaluate fiducial marker systems, namely the popular AprilTag system, in experiments. Our study especially focuses on aspects crucial for highly agile micro underwater robots, such as dynamic motions and the calibration medium. This class of robots is typically extensively studied in research tanks which motivates a first focus on clear-water settings. However, the proposed method and the findings can be transferred to similar scenarios. We demonstrate the importance of calibrating underwater and that the detection accuracy decreases linearly with camera distance and could therefore easily be compensated for. Moreover, we identify a suitable camera that maximizes the detection rate during highly dynamic motions. In sum, this work is an initial step towards application-relevant design strategies for designing low-cost, accessible localization systems for agile, mobile robots.

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.