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.

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.

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.

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.