C-Ray is an amphibious robot that is capable of swimming in water and crawling on land using its undulating fins, enabling operations in a wide range of environments. The robot can be modeled as a hybrid dynamical system whose dynamics and propulsion change when the robot transitions between water and land. Most importantly, the direction of wave travel in the robot's fins is reversed between its swimming and crawling locomotion styles. To operate autonomously, C-Ray requires both accurate identification of when transitions between water and land occur and robust state estimation in littoral environments where the transition dynamics are highly discontinuous and transient. This paper presents a hybrid observer for estimating continuous states and identifying state-driven mode switches for C-Ray, enabling autonomous water/land-transitions. The proposed observer is a combination of the multiplicative extended Kalman filter (MEKF) and the salted Kalman filter, a newly proposed Kalman filter for mapping state uncertainty during hybrid transitions. We also propose an altitude and sea floor geometry observer and incorporate this directly into the MEKF. The performance is evaluated in simulations.

Underwater navigation is an area of increasing research interest due to its fundamental complexity and industrial applications. However, due to convenience and current theoretical understanding, the vast majority of underwater platforms utilize thrusters, while other forms of propulsion, such as undulatory locomotion, have been given limited exposure. This paper provides the first real-time motion planning framework that produces energy and time efficient paths with empirical local optimality for articulated swimming robots in 3D, called SIMP. SIMP utilizes learned associations between parameterized dynamically feasible undulatory gaits with their expected energy cost, velocity, and swept-out volume of the robot during execution, to formulate a simplified optimization problem that decides the path to be followed with the corresponding consecutive gaits, and navigates the robot safely in complex 3D environments. The proposed pipeline is tested in numerical experiments with realistic dynamics for a 10 link underwater snake robot (USR) with anguilliform gaits, in simulated cluttered environments of significant challenge, displaying real-time replanning performance of more than 1 Hz.

Robust underwater motion planning of autonomous underwater vehicles (AUVs) in dynamic cluttered environments is a problem that has yet to be addressed in depth. Due to advances in technology and computational capacity, AUVs are expected to operate safely and autonomously in increasingly challenging environments, necessitating methods that are able to safely navigate robots in real-time. Though, most solutions remain overly cautious and conservative. This paper proposes RUMP, a novel locally-optimal motion planning framework for robust real-time autonomous underwater navigation in 3D cluttered environments consisting of observed static and dynamic obstacles. The problem is modeled using path optimization and can be solved in real-time with a common nonlinear solver. The constructed objective function allows deciding the local goal during optimization to both maximize safety within a planning horizon and minimize the expected distance to the target position. Furthermore, path safety is considered for the entire transition between consecutive states, utilizing a novel approach for continuous spatiotemporal collision checks. The proposed formulation provides safe performance even in environments with obstacles that may move orders of magnitude faster than the AUV itself. Simulation experiments, in different challenging scenarios of obstacles moving up to 100 times faster than the robot, showcase robustness and efficient real-time performance of more than 15 Hz.

This paper presents a 3D multiple object detection and tracking framework for identifying and quantifying changes in fish behaviour through tracking the 3D position, distance and speed of fish with respect to an underwater stereo camera. The framework consists of six essential modules based on 3D object detection to identify fish and multiple object tracking algorithms to track the fish in sequential frames. In particular, the latest version of Yolo (Yolov7) is utilised for object detection and the deep SORT algorithm is used for multiple object tracking. The framework was tested using videos captured from an underwater stereo camera in an industrial-scale sea-based fish farm. The results showed that the framework was able to accurately detect and track multiple fish in 3D. The fish position, distance and speed relative to the camera were also successfully detected. The results of this study demonstrate the effectiveness of this framework in identifying and quantifying changes in fish behaviour. The proposed novel framework has the potential to greatly enhance our understanding of fish behaviour in their natural habitats, leading to new insights into fish ecology and behaviour, while at the same time, it can enable researchers to study fish behaviour in a more detailed and accurate way.

Autonomous operation in underwater environ-ments is, arguably, one of the most complex domains. It requires safe operations under the presence of unpredictable surge, currents, uncertainty, and dynamic obstacles that challenges to the highest degree real-time motion planning; the primary focus of this paper. Although previous work addressed the problem of safe real-time 3D navigation in cluttered underwater environments, it did not account explicitly for disturbances, currents, dynamic obstacles, or uncertainty growth. This paper presents ResiPlan, a novel motion planning framework that utilizes past information of errors monitoring the path follower's performance, along with estimation of dynamic obstacles and uncertainty, to produce adaptive paths by adjusting the safety margins accordingly. Extensive numerical experiments and simulations validate the safety guarantees of the technique, in a variety of different environments with various types of disturbance, showcasing the strong potential to be utilized for operations in challenging underwater environments.

This paper proposes and experimentally validates a straight line path following controller for underwater snake robots in the presence of constant irrotational currents of unknown direction and magnitude. An integral line-of-sight guidance law is presented, which is combined with a sinusoidal gait pattern and a directional controller that steers the robot toward and along the desired path. The stability of the proposed control scheme in the presence of ocean currents is investigated by using Poincaré map analysis. Simulation results are presented to illustrate the performance of the proposed path following controller for both lateral undulation and eel-like motion. In addition, the performance of the path following controller is investigated through experiments with a physical underwater snake robot. The experimental results show that the proposed control strategy successfully steers the robot toward and along the desired path in the presence of an unknown constant irrotational current in the inertial frame.

Increasing efficiency by improving the locomotion methods is a key issue for underwater robots. Hence, an accurate dynamic model is important for both controller design and efficient locomotion methods. This paper presents a model of the kinematics and dynamics of a planar, underwater snake robot aimed at control design. Fluid contact forces and torques are modeled using analytical fluid dynamics. The model is derived in a closed form and can be utilized in modern model-based control schemes. The proposed model is easily implemented and simulated, regardless of the number of robot links. Simulation results with a ten link robotic system are presented.

Increasing efficiency by improving the locomotion methods is a key issue for underwater robots. Consequently, an accurate dynamic model is important for both controller design and for the development of efficient locomotion methods. This paper presents a model of the kinematics and dynamics of an underwater snake robot moving in a vertical plane in 3D. The fluid contact forces (hydrodynamic forces) and torques (fluid moments) are modeled using analytical fluid dynamics. Hydrodynamic forces and torques, i.e. linear and nonlinear drag forces, current effects, added mass and fluid torque effects, are considered. In addition, this modeling approach also takes into account the hydrostatic forces (gravitational forces and buoyancy). The model is given in a closed form and is thus in a form that is well-suited for modern model-based control schemes. The proposed model is easily implemented and simulated, regardless of the number of robot links. Simulation results for lateral undulation and eel-like motion with a ten link robotic system are presented.