Aquatic robots require an accurate and reliable localization system to navigate autonomously and perform practical missions. Kalman filters (KFs) and their variants are typically used in aquatic robots to combine sensor data. The two critical drawbacks of KFs are the requirement for skilled tuning of several filter parameters and the fact that changes to how the Inertial Measurement Unit (IMU) is oriented necessitate modifying the filter. To overcome those problems, this paper presents a novel method of fusing sensor data from a Doppler Velocity Log (DVL) and IMU using an adaptive nonlinear estimator to provide dead reckoning localization for a small autonomous surface vehicle. The proposed method has only one insensitive tuning parameter and is agnostic to the configuration of the IMU. The system was validated using a small ASV in a 2.4×3.6×2.4 m water tank, with a motion capture system as ground truth, and was evaluated against a state-of-the-art method based on KFs. Experiments showed that the average drift error of the nonlinear filter was 0.16 m (s.d. 0.06 m) compared to 0.15 m (s.d. 0.05 m) for the state of the art, meaning that the benefits in terms of tuning and flexible configuration do not come at the expense of performance.

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.

The development of mobile robot platforms for inspection has gained traction in recent years. However, conventional mobile robots are unable to address the challenge of operating in extreme environments where the robot is required to traverse narrow gaps in highly cluttered areas with restricted access, typically through narrow ports. This article presents MIRRAX, a robot designed to meet these challenges by way of its reconfigurable capability. Controllers for the robot are detailed, along with an analysis on the controllability of the robot given the use of mecanum wheels in a variable configuration. Characterization on the robot's performance identified suitable configurations for operating in narrow environments. The experimental validation of the robot's controllability shows good agreement with the theoretical analysis and the capability to address the challenges of accessing entry ports as small as 150-mm diameter, as well as navigating through cluttered environments. This article also presents results from a deployment in a Magnox facility at the Sellafield nuclear site in the U.K.—the first robot to ever do so, for remote inspection and mapping.

In recent years, the inspection of extreme environments using mobile robots has gained traction, as robots are able to mitigate the risk placed on humans and at times achieve what humans are unable to. In some scenarios, the robot is required to operate in cluttered environments with highly restricted access through 150 mm diameter ports. The MIRRAX robot has been designed to meet these challenges with the capability of reconfiguring itself to both access environments and navigate through tightly spaced obstacles. The joints used for reconfiguration of the robot introduce additional challenges for path planning due to the significant changes that can occur between adjacent poses. This paper presents a global path planner for MIRRAX. A Voronoi diagram is first used to generate a sparse graph to represent the topology of the environment, which allows for fast, coarse path planning. The coarse path is then refined via a heuristic pose fitting routine to ensure that the path is both collision-free and reduce unnecessary joint angle changes. The planner has been evaluated in simulation, demonstrating the feasibility of generating collision-free paths through narrow pathways for a reconfigurable robot.

A long short-term memory neural network is used to provide a system model that captures the temporal-dynamics of a holonomic, fully-actuated aquatic surface vehicle. As is true in many fields, new developments in robotics often are made in simulation first before being applied to real systems. To simulate an aquatic or aerial robot, a dynamic system model of the robot is required. The more representative the dynamic model is of the real robot, the smaller the simulation-to-reality gap becomes. The performance of the neural network is compared against a classical parametric model, where coefficients of the parametric model were identified using the same data that was used to train the neural network. The results show that the neural network consistently outperforms the classical parametric model and significantly reduces the error between real velocities and estimated velocities. The neural network also demonstrated the ability to capture complex hydrodynamic effects that were not captured in the parametric model. In addition to the performance improvements, the neural network method can be easily adapted to similarly actuated aquatic vehicles by simply retraining, whereas the classical approach would require manual selection of new equation terms. The neural network model that was created has been used in a vehicle simulation and is presently being used as a research tool.

This work presents a practical method of obtaining a dynamic system model for small omnidirectional aquatic vehicles. The models produced can be used to improve vehicle localisation, aid in the design or tuning of control systems and facilitate the development of simulated environments. The use of a dynamic model for onboard real-time velocity prediction is of particular importance for aquatic vehicles because, unlike ground vehicles, fast and direct measurement of velocity using encoders is not possible. Previous work on model identification of aquatic vehicles has focused on large vessels that are typically underactuated and have low controllability in the sway direction. In this paper it is demonstrated that the procedure for identifying the model coefficients can be performed quickly, without specialist equipment and using only onboard sensors. This is of key importance because the dynamic model coefficients will change with the payload. Two different thrust allocation schemes are tested, one of which is a known method and another is proposed here. Validation tests are performed and the models are shown to be suitable for their intended applications. Significant reduction in model error is demonstrated using the novel thrust allocation method that is designed to avoid deadbands in the thruster responses.