The development of novel autonomous underwater gliders has been hindered by limited shape diversity, primarily due to the reliance on traditional design tools that depend heavily on manual trial and error. Building an automated design framework is challenging due to the complexities of representing glider shapes and the high computational costs associated with modeling complex solid-fluid interactions. In this work, we introduce an AI-enhanced automated computational framework designed to overcome these limitations by enabling the creation of underwater robots with non-trivial hull shapes. Our approach involves an algorithm that cooptimizes both shape and control signals, utilizing a reducedorder geometry representation and a differentiable neural-network-based fluid surrogate model. This end-to-end design workflow facilitates rapid iteration and evaluation of hydrodynamic performance, leading to the discovery of optimal and complex hull shapes across various control settings. We validate our method through wind tunnel experiments and swimming pool gliding tests, demonstrating that our computationally designed gliders surpass manually designed counterparts in terms of energy efficiency. By addressing challenges in efficient shape representation and neural fluid surrogate models, our work paves the way for the development of highly efficient underwater gliders, with implications for long-range ocean exploration and environmental monitoring.

This paper introduces the design, fabrication, and autonomous control of MicroASV, a low-cost, centimeter-scale autonomous surface Vehicle (ASV). MicroASV has a square footprint with a side length of 85 mm. Its propulsion system consists of four custom water jets arranged in a “Diamond” shaped actuator configuration, powered by magnetically coupled brushless motors. This setup allows for complete 2D mobility, enabling forward and backward motion, lateral translation, and in-place rotation. The MicroASV is built using commercially available motors and 3D-printed components, creating a modular, appendage-free structure that is simple to assemble. An onboard camera and inertial measurement unit (IMU) are integrated to enable real-time localization, with position and heading controllers developed to provide autonomous feedback control. Preliminary experiments validate the platform's effectiveness in motion, sensing, and control, establishing MicroASV as a valuable tool for studying centimeter-scale ASV control, both individually and in collective swarm operations.

Micro Autonomous Surface Vehicles (MicroASVs) offer significant potential for operations in confined or shallow waters and swarm robotics applications. However, achieving precise and robust control at such small scales remains highly challenging, mainly due to the complexity of modeling nonlinear hydrodynamic forces and the increased sensitivity to self-motion effects and environmental disturbances, including waves and boundary effects in confined spaces. This paper presents a physics-driven dynamics model for an over-actuated MicroASV and introduces a data-driven optimal control framework that leverages a weak formulation-based online model learning method. Our approach continuously refines the physics-driven model in real time, enabling adaptive control that adjusts to changing system parameters. Simulation results demonstrate that the proposed method substantially enhances trajectory tracking accuracy and robustness, even under unknown payloads and external disturbances. These findings highlight the potential of data-driven online learning-based optimal control to improve MicroASV performance, paving the way for more reliable and precise autonomous surface vehicle operations.

Safe motion planning is essential for autonomous vessel operations, especially in challenging spaces such as narrow inland waterways. However, conventional motion planning approaches are often computationally intensive or overly conservative. This paper proposes a safe motion planning strategy combining Model Predictive Control (MPC) and Control Barrier Functions (CBFs). We introduce a time-varying inflated ellipse obstacle representation, where the inflation radius is adjusted depending on the relative position and attitude between the vessel and the obstacle. The proposed adaptive inflation reduces the conservativeness of the controller compared to traditional fixed-ellipsoid obstacle formulations. The MPC solution provides an approximate motion plan, and high-order CBFs ensure the vessel’s safety using the varying inflation radius. Simulation and real-world experiments demonstrate that the proposed strategy enables the fully-actuated autonomous robot vessel to navigate through narrow spaces in real time and resolve potential deadlocks, all while ensuring safety.

In autonomous robot navigation, the trajectories from path planners are considered to be safe regions, and deviations could endanger vessels. Model Predictive Control (MPC) stands as a popular choice for trajectory tracking problems as it naturally addresses operational constraints, such as dynamics and control constraints. Nevertheless, achieving robustness in changing environments like oceans and rivers, which are constantly subject to significant external disturbances, remains an ongoing challenge for MPC. It must consistently keep the system within a predefined safe region (such as a reference trajectory) even in the presence of model inaccuracies and perturbations. To address this challenge, we present a robust model predictive control strategy utilizing Control Barrier Functions (CBFs), which increases the disturbance-rejection abilities. We verify our method on an autonomous surface vessel in simulation and natural waters, both with external disturbances. Specifically, compared with the traditional MPC method, our proposed MPC-CBF strategy reduces tracking errors by 17.82% and 40.26% in simulations and field experiments, respectively. Although the control effort slightly increases by 7.78% and 4.20%, respectively, these results clearly demonstrate the enhanced resilience of MPC-CBF to disturbances.

Accurate control of autonomous marine robots still poses challenges due to the complex dynamics of the environment. In this paper, we propose a Deep Reinforcement Learning (DRL) approach to train a controller for autonomous surface vessel (ASV) trajectory tracking and compare its performance with an advanced nonlinear model predictive controller (NMPC) in real environments. Taking into account environmental disturbances (e.g., wind, waves, and currents), noisy measurements, and non-ideal actuators presented in the physical ASV, several effective reward functions for DRL tracking control policies are carefully designed. The control policies were trained in a simulation environment with diverse tracking trajectories and disturbances. The performance of the DRL controller has been verified and compared with the NMPC in both simulations with model-based environmental disturbances and in natural waters. Simulations show that the DRL controller has 53.33% lower tracking error than that of NMPC. Experimental results further show that, compared to NMPC, the DRL controller has 35.51% lower tracking error, indicating that DRL controllers offer better disturbance rejection in river environments than NMPC.

Within the next several years, there will be a high level of autonomous technology that will be available for widespread use, which will reduce labor costs, increase safety, save energy, enable difficult unmanned tasks in harsh environments, and eliminate human error. Compared to software development for other autonomous vehicles, maritime software development, especially in aging but still functional fleets, is described as being in a very early and emerging phase. This presents great challenges and opportunities for researchers and engineers to develop maritime autonomous systems. Recent progress in sensor and communication technology has introduced the use of autonomous surface vehicles (ASVs) in applications such as coastline surveillance, oceanographic observation, multi-vehicle cooperation, and search and rescue missions. Advanced artificial intelligence technology, especially deep learning (DL) methods that conduct nonlinear mapping with self-learning representations, has brought the concept of full autonomy one step closer to reality. This article reviews existing work on the implementation of DL methods in fields related to ASV. First, the scope of this work is described after reviewing surveys on ASV developments and technologies, which draws attention to the research gap between DL and maritime operations. Then, DL-based navigation, guidance, control (NGC) systems and cooperative operations are presented. Finally, this survey is completed by highlighting current challenges and future research directions.

Autonomous latching is essential for autonomous surface vessels (ASV) to reach full independence from human intervention. As part of the ASV Roboat project, a new solution for self-latching maneuvers has been developed and is presented here. We propose a system that has the key requirements of full integration with the navigation control system and zero-gap connection with the dock, the latter being essential for wireless charging of the ASV. Dedicated markers are used to identify docking targets, relying on computer vision algorithms to determine distance and bearing to the target. In its idle state, the locking solution uses mechanical power-off brakes, minimizing energy consumption while ensuring the boat stays in position indefinitely once docked. A prototype of the proposed mechanism has been built and installed in Roboat. Experimental tests showing the mechanism performance and capability to autonomously approach the docking station are discussed in this work.

Autonomous underwater vehicles (AUVs) are spe-cialized robots that are commonly used for seafloor surveying and ocean water sampling. Computational design approaches have emerged to reduce the effort required to design both individual AUVs as well as fleets. As the number and scale of underwater missions increases beyond the capabilities of a single vehicle, fleet level design will become more important. Depending on the mission, the optimal fleet may consist of multiple distinct types of AUVs designed to a variety of specifications. Moreover, the AUVs may differ in both continuous parameters (such as battery capacity) and discrete parameters (such as number and model of thrusters). In this work, we present a computational pipeline for designing these heterogeneous AUV fleets. Using a novel shape design space based on a graph grammar and deformation cages, we can express a variety of AUV architectures with different topologies, component selections, and dimensions. We search this space using a combination of discrete graph search and gradient-based continuous optimization, enabled by a differentiable AUV simulator. Finally, we formulate heterogeneous fleet design as a modified knapsack problem, and solve it using an efficient backtracking-based algorithm. We evaluate our pipeline on a simulated mission with nonuniform design requirements-surveying a section of seafloor with varying depth-and show that the best heterogeneous fleet outperforms the best fleet composed of a single vehicle type.

Unmanned aerial vehicles (UAVs) have broad applications including disaster response, transportation, photography, and mapping. A significant bottleneck in the development of UAVs is the limited availability of automatic tools for task-specific co-design of a UAV's shape and controller. The development of such tools is particularly challenging as UAVs can take many forms, including fixed-wing planes, radial copters, and hybrid topologies, with each class of topology showing different advantages. In this work, we present a computational design pipeline for UAVs based on a graph grammar that can search across a wide range of topologies. Graphs generated by the grammar encode different topologies and component selections, while continuous parameters encode the dimensions and properties of each component. We further augment the shape representation with deformation cages, which allow expressing a variety of wing shapes. Each UAV design is associated with an LQR controller with tunable continuous parameters. To search over this complex discrete and continuous design space, we develop a hybrid algorithm that combines discrete graph search strategies and gradient-based continuous optimization methods using a differentiable UAV simulator. We evaluate our pipeline on a set of simulated flight tasks requiring dynamic motions, showing that it discovers novel UAV designs that outperform canonical UAVs typically made by engineers.

Autonomous surface vessels (ASVs) always carry payloads such as passengers and cargoes. The change in the payload can sometimes be several times the weight of the vessel. The payload can cause significant changes in the dynamics of the vessel, thereby degrading the performance of the controller. This paper proposes an adaptive nonlinear model predictive control (A-NMPC) strategy for ASV trajectory tracking, which allows real-time changes in dynamics caused by severe payload variation. First, a nonlinear dynamic model that updates with the vessel’s payload is established. Then a pressure sensing method is proposed to estimate the payload of the vessel. Further, a parametric cost function that considers changing dynamics, as well as input and state constraints, is formulated in the NMPC algorithm. The tracking ability of A-NMPC is systematically studied on three different sizes of vessels in the simulation where the payload of these vessels changes eight times their inherent weight. Numerical results show that when the payload changes greatly the vessels with A-NMPC can accurately track the reference trajectory while the vessels with conventional NMPC cannot. Finally, the tracking experiments with a quarter-scale vessel in a swimming pool further verify the effectiveness of the proposed A-NMPC strategy.

We propose a scalable cooperative control approach which coordinates a group of rigidly connected autonomous surface vessels to track desired trajectories in a planar water environment as a single floating modular structure. Our approach leverages the implicit information of the structure's motion for force and torque allocation without explicit communication among the robots. In our system, a leader robot steers the entire group by adjusting its force and torque according to the structure's deviation from the desired trajectory, while follower robots run distributed consensus-based controllers to match their inputs to amplify the leader's intent using only onboard sensors as feedback. To cope with the nonlinear system dynamics in the water, the leader robot employs a nonlinear model predictive controller (NMPC), where we experimentally estimated the dynamics model of the floating modular structure in order to achieve superior performance for leader-following control. Our method has a wide range of potential applications in transporting humans and goods in many of today's existing waterways. We conducted trajectory and orientation tracking experiments in hardware with three custom-built autonomous modular robotic boats, called Roboat, which are capable of holonomic motions and onboard state estimation. Simulation results with up to 65 robots also prove the scalability of our proposed approach.

This paper presents a novel autonomous surface vessel (ASV), called Roboat II for urban transportation. Roboat II is capable of accurate simultaneous localization and mapping (SLAM), receding horizon tracking control and estimation, and path planning. Roboat II is designed to maximize the internal space for transport, and can carry payloads several times of its own weight. Moreover, it is capable of holonomic motions to facilitate transporting, docking, and inter-connectivity between boats. The proposed SLAM system receives sensor data from a 3D LiDAR, an IMU, and a GPS, and utilizes a factor graph to tackle the multi-sensor fusion problem. To cope with the complex dynamics in the water, Roboat II employs an online nonlinear model predictive controller (NMPC), where we experimentally estimated the dynamical model of the vessel in order to achieve superior performance for tracking control. The states of Roboat II are simultaneously estimated using a nonlinear moving horizon estimation (NMHE) algorithm. Experiments demonstrate that Roboat II is able to successfully perform online mapping and localization, plan its path and robustly track the planned trajectory in the confined river, implying that this autonomous vessel holds the promise on potential applications in transporting humans and goods in many of the waterways nowadays.

We propose a framework for tightly-coupled lidar inertial odometry via smoothing and mapping, LIO-SAM, that achieves highly accurate, real-time mobile robot trajectory estimation and map-building. LIO-SAM formulates lidar-inertial odometry atop a factor graph, allowing a multitude of relative and absolute measurements, including loop closures, to be incorporated from different sources as factors into the system. The estimated motion from inertial measurement unit (IMU) pre-integration de-skews point clouds and produces an initial guess for lidar odometry optimization. The obtained lidar odometry solution is used to estimate the bias of the IMU. To ensure high performance in real-time, we marginalize old lidar scans for pose optimization, rather than matching lidar scans to a global map. Scan-matching at a local scale instead of a global scale significantly improves the real-time performance of the system, as does the selective introduction of keyframes, and an efficient sliding window approach that registers a new keyframe to a fixed-size set of prior "sub-keyframes." The proposed method is extensively evaluated on datasets gathered from three platforms over various scales and environments.

Underwater communication is extremely challenging for small underwater robots which typically have stringent power and size constraints. In our previous work, we developed an artificial electrocommunication system which could be an alternative for the communication of small underwater robots. This paper further presents a new electrocommunication system that utilizes Binary Frequency Shift Keying (2FSK) modulation and deep-learning-based demodulation for underwater robots. We first derive an underwater electrocommunication model that covers both the near-field area and a large transition area outside of the near-field area. 2FSK modulation is adopted to improve the anti-interference ability of the electric signal. A deep learning algorithm is used to demodulate the electric signal by the receiver. Simulations and experiments show that with the same testing condition, the new communication system outperforms the previous system in both the communication distance and the data transmitting rate. In specific, the newly developed communication system achieves stable communication within the distance of 10 m at a data transfer rate of 5 Kbps with a power consumption of less than 0.1 W. The substantial increase in communication distance further improves the possibility of electrocommunication in underwater robotics.

A lateral line system is a flow-responsive organ system, with which fish can effectively sense the surrounding flow field, thus serving functions in flow-aided fish behaviors. Inspired by such a biological characteristic, artificial lateral line systems (ALLSs) have been developed for promoting technological innovations of underwater robots. In this article, we focus on investigating state estimation of a freely swimming robotic fish in multiple motions, including rectilinear motion, turning motion, gliding motion, and spiral motion. The state refers to motion parameters, including linear velocity, angular velocity, motion radius, etc., and trajectory of the robotic fish. Specifically, for each motion, a pressure variation (PV) model that links motion parameters to PVs surrounding the robotic fish is first built; then, a linear regression analysis method is used for determining the model parameters. Based on the acquired PV model, motion parameters can be estimated by solving the PV model inversely using the PVs measured by the ALLS. Finally, a trajectory estimation method is proposed for estimating trajectory of the robotic fish based on the ALLS-estimated motion parameters. The experimental results show that the robotic fish is able to estimate its trajectory in the aforementioned multiple motions with the aid of ALLS, with small estimation errors.

Autonomous robotic boats are devised to transport people and goods similar to self-driving cars. One of the attractive features specially applied in water environment is to dynamically link and join multiple boats into one unit in order to form floating infrastructure such as bridges, markets or concert stages, as well as autonomously self-detach to perform individual tasks.In this paper we present a novel latching system that enables robotic boats to create dynamic united floating infrastructure while overcoming water disturbances. The proposed latching mechanism is based on the spherical joint (ball and socket) that allows rotation and free movements in two planes at the same time. In this configuration, the latching system is capable to securely and efficiently assemble/disassemble floating structures. The vision-based robot controller guides the self-driving robotic boats to latch with high accuracy in the millimeter range. Moreover, in case the robotic boat fails to latch due to harsh weather, the autonomous latching system is capable to recompute and reposition to latch successfully. We present experimental results from latching and docking in indoor environments. Also, we present results in outdoor environments from latching a couple of robotic boats in open water with calm and turbulent currents.

Unmanned surface vehicles (USVs) are typically designed for open area marine applications. In this paper, we present a new autonomy system (Roboat) for urban waterways which requires robust localization, perception, planning, and control. A novel localization system, based on the extended Kalman filter (EKF), is proposed for USVs, which utilizes LiDAR, camera, and IMU to provide a decimeter-level precision in dynamic GPS-attenuated urban waterways. Area and shape filters are proposed to crop water reflections and street obstacles from a pointcloud. Euclidean clustering and multi-object contour tracking are then introduced to detect and track the static and moving objects reliably in urban waters. An efficient path planner is tailored to calculate optimal trajectories to avoid these static and dynamic obstacles. Lastly, a nonlinear model predictive control (NMPC) scheme with full state integration is formulated for the four-control-input robot to accurately track the trajectory from the planner in rough water. Extensive experiments show that the robot is able to autonomously navigate in both the indoor waterway and the cluttered outdoor waterway in the presence of static and dynamic obstacles, implying that Roboat could have a great impact on the future of transportation in many coastal and riverside cities.

Autonomous gait optimization is an essential survival ability for mobile robots. However, it remains a challenging task for underwater robots. This paper addresses this problem for the locomotion of a bio-inspired robotic fish and aims at identifying fast swimming gait autonomously by the robot. Our approach for learning locomotion controllers mainly uses three components: 1) a biological concept of central pattern generator to obtain specific gaits; 2) an onboard sensory processing center to discover the environment and to evaluate the swimming gait; and 3) an evolutionary algorithm referred to as particle swarm optimization. A key aspect of our approach is the swimming gait of the robot is optimized autonomously, equivalent to that the robot is able to navigate and evaluate its swimming gait in the environment by the onboard sensors, and simultaneously run a built-in evolutionary algorithm to optimize its locomotion all by itself. Forward speed optimization experiments conducted on the robotic fish demonstrate the effectiveness of the developed autonomous optimization system. The latest results show that our robotic fish attained a maximum swimming speed of 1.011 BL/s (40.42 cm/s) through autonomous gait optimization, faster than any of the robot's previously recorded speeds.

In this paper, we present the design, modeling, and real-time nonlinear model predictive control (NMPC) of an autonomous robotic boat. The robot is easy to manufacture, highly maneuverable, and capable of accurate trajectory tracking in both indoor and outdoor environments. In particular, a cross type four-thruster configuration is proposed for the robotic boat to produce efficient holonomic motions. The robot prototype is rapidly 3D-printed and then sealed by adhering several layers of fiberglass. To achieve accurate tracking control, we formulate an NMPC strategy for the four-control-input boat with control input constraints, where the nonlinear dynamic model includes a Coriolis and centripetal matrix, the hydrodynamic added mass, and damping. By integrating “GPS” modules and an inertial measurement unit (IMU) into the robot, we demonstrate accurate trajectory tracking of the robotic boat along preplanned paths in both a swimming pool and a natural river. Furthermore, the code generation strategy employed in our paper yields a two order of magnitude improvement in the run time of the NMPC algorithm compared to similar systems. The robot is designed to form the basis for surface swarm robotics testbeds, on which collective algorithms for surface transportation and self-assembly of dynamic floating infrastructures can be assessed.

Underwater communication is particularly challenging for small submarine robots that have limited power and size constraints. Inspired by weakly electric fish, a novel electric current communication (termed electrocommunication) system has been developed for small underwater robots in our previous studies. However, collision problems sometimes occur during multiple robots communication because the electrocommunication network shares a common channel. In this paper, a new CSMA/CA-based electrocommunication system is presented to solve this collision problem. An efficient circuit for communication channel state (busy or idle) detection is proposed. After that, a compact Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) algorithm is introduced and finally implemented on the system to judiciously avoid collision during multiple robot communication. The effectiveness of the proposed CSMA/CA-based communication system for underwater robot groups is well verified by both simulations and experiments with three robotic fish models.

Artificial lateral line has been drawing an increasing attention recently for its potential applications in robotics. Experiments are usually conducted with a bioinspired robot in a controlled environment, where the sensing platform is held stationary or slowly driven with a simple linear motion. In this paper, we conduct a more practical and challenging study where the robot uses artificial lateral line to evaluate its linear velocity while freely swimming. We use onboard artificial lateral line to measure the pressure profiles over the surface of a robotic fish and employ onboard IMU (inertial measurement unit) to record the motion kinematics of the robot while freely swimming at various speeds. We find that 1) pressure changes are greatest on the head of the robot; 2) pressures increase along with the swimming speed and the oscillation amplitude of angular velocity of the robot. Therefore, we propose a nonlinear prediction model which incorporates distributed pressure and angular velocity to estimate the speed of the robot. Online speed evaluation experiment demonstrates the effectiveness and the accuracy of the proposed model.

Communication is challenging for underwater robots. This paper presents the first research into developing an underwater electric current communication system and integrating the system into a small robotic fish. It is notable for its potential for a group of underwater robots communicating within a short distance range in conditions where optical and acoustic methods would meet difficulty. The working principle of the electric current communication is explained by a simplified electric dipole model. After that, systematic design of the electric current communication system is proposed for underwater robots. Communication experiments with the robotic fish demonstrate the effectiveness of the developed electric current communication system. The experimental results show that a remote control system can communicate underwater with our robotic fish over a distance of three meters by use of electric current communication.

Fish possesses a unique sensory organ called the lateral line. The lateral line provides fish with flow-related information. It is accepted that fish can use the lateral line to sense states of its neighbours in schooling behaviors. In this study, we investigate how a focal robotic fish senses the states of its swimming neighbour by using its onboard artificial lateral line system for the first time. Dye flow visualization is used to characterize the large-scale structures of the wake behind a swimming robotic fish. In the experiment, the Reverse Karman Vortex Street generated by the anterior robotic fish was sensed by the artificial lateral line of the focal robot. The results show that the robot's artificial lateral line can detect the beating frequency of its neighbouring robot and the distance between the robots. It is promising that artificial lateral line sensing could become one of the most popular close interaction methods for a group of underwater robots in the near future.

This paper focuses on the dynamic modeling of a self-propelled, multimodal ostraciiform robotic fish, whose three active joints (two pectoral fins and one caudal fin) are actuated by a Central Pattern Generator (CPG) controller. Compared with other dynamic modes for robotic fish, we introduce angle of attack (AoA) theory on the fish modeling, which can be used to further explore the relationship between swimming efficiency and AoA of robotic fish. First, by using the quasi-steady wing theory, AoA of the oscillatory fins are explicitly derived. Then, with the simplification of the robot as a multi-rigid-body mechanism, AoA-based fluid forces acting on the oscillatory fins of the robot are further approximately calculated in a three-dimensional context. Next, by importing the driving signals (generated by CPG control law) into a Lagrangian function, the differential-algebraic equations are employed to establish a hydrodynamic model for steady swimming of the ostraciiform robotic fish for the first time. Finally, comparative results between simulations and experiments for forward and turning gaits of the robot are systematically conducted to show the effectiveness of the built AoA-based dynamic model.