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.