Development of Modular Bio-Inspired Autonomous Underwater Vehicle for Close Subsea Asset Inspection

To reduce human risk and maintenance costs, Autonomous Underwater Vehicles (AUVs) are involved in subsea inspections and measurements for a wide range of marine industries such as offshore wind farms and other underwater infrastructure. Most of these inspections may require levels of manoeuvrability similar to what can be achieved by tethered vehicles, called Remotely Operated Vehicles (ROVs). To extend AUV intervention time and perform closer inspection in constrained spaces, AUVs need to be more efficient and flexible by being able to undulate around physical constraints. A biomimetic fish-like AUV known as RoboFish has been designed to mimic propulsion techniques observed in nature to provide high thrust efficiency and agility to navigate its way autonomously around complex underwater structures. Building upon advances in acoustic communications, computer vision, electronics and autonomy technologies, RoboFish aims to provide a solution to such critical inspections. This paper introduces the first RoboFish prototype that comprises cost-effective 3D printed modules joined together with innovative magnetic coupling joints and a modular software framework. Initial testing shows that the preliminary working prototype is functional in terms of water-tightness, propulsion, body control and communication using acoustics, with visual localisation and mapping capability

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Embodied Models and Neurorobotics

Neuroscience has become a very broad field indeed: each year around 30,000 researchers and students from around the ... We trace a path from neuron to cognition via computational neuroscience, but what is computational neuroscience

Locomation strategies for amphibious robots-a review

In the past two decades, unmanned amphibious robots have proven the most promising and efficient systems ranging from scientific, military, and commercial applications. The applications like monitoring, surveillance, reconnaissance, and military combat operations require platforms to maneuver on challenging, complex, rugged terrains and diverse environments. The recent technological advancements and development in aquatic robotics and mobile robotics have facilitated a more agile, robust, and efficient amphibious robots maneuvering in multiple environments and various terrain profiles. Amphibious robot locomotion inspired by nature, such as amphibians, offers augmented flexibility, improved adaptability, and higher mobility over terrestrial, aquatic, and aerial mediums. In this review, amphibious robots' locomotion mechanism designed and developed previously are consolidated, systematically The review also analyzes the literature on amphibious robot highlighting the limitations, open research areas, recent key development in this research field. Further development and contributions to amphibious robot locomotion, actuation, and control can be utilized to perform specific missions in sophisticated environments, where tasks are unsafe or hardly feasible for the divers or traditional aquatic and terrestrial robots

Design and control of amphibious robots with multiple degrees of freedom

This thesis presents the design and realization of two generations of robot elements that can be assembled together to construct amphibious mobile robots. These elements, designed to be individually waterproof and having their own battery, motor controller, and motor, have been used to actually construct a snake, a boxfish and a salamander robot. Central pattern generator (CPG) models inspired from those found in vertebrates have been used for online trajectory generation on these robots and implemented on their onboard locomotion controllers. CPGs proved to be an interesting way of controlling complex robots, providing a simple interface which hides the complexity of the robot to the end user. Online learning algorithms that can be used to dynamically adapt the locomotion parameters to the environment have been implemented. Finally, this work also shows how robotics can be a useful tool to verify biological hypotheses. For instance, the salamander robot has been used to test a model of CPG for salamander locomotion

Intersection between natural and artificial swimmers: a scaling approach to underwater vehicle design.

Approximately 72% of the Earth’s surface is covered by water, yet only 20% has been mapped [1]. Autonomous Underwater Vehicles (AUVs) are one of the main tools for ocean exploration. The demand for AUVs is expected to increase rapidly in the coming years [2], so there is a need for faster and more energy efficient AUVs. A drawback to using this type of vehicle is the finite amount of energy that is stored onboard in the form of batteries. Science and roboticists have been studying nature for ways to move more efficiently. Phillips et al. [3] presents data that contradicts the idea that fish are better swimmers than conventional AUVs when comparing the energetic cost of swimming in the form of the Cost of Transport (COT). The data presented by Phillips et al. only applies to AUVs at higher length and naval displacement (mass) scales, so the question arises of whether an AUV built at different displacements and length scales is more efficient than biological animals and if current bio-inspired platforms are better than conventional AUVs. Besides power requirements, it is also useful to compare the kinematic parameters of natural and artificial swimmers. In this case, kinematic parameters indicate how fast the swimmer travels through the water. Also, they describe how fast the propulsion mechanism must act to reach a certain swimming speed. This research adopts the approach of Gazzola et al. [4] where the Reynolds number is associated with a dimensionless number, Swim number (Sw) in this case, that has all the kinematic information. A newly developed number that extends the swim number to conventional AUVs is the Propulsion number (Jw), which demonstrates excellent agreement with the kinematics of conventional AUVs. Despite being functionally similar, Sw and Jw do not have a one-to-one relationship. Sw, Jw, COT represent key performance metrics for an AUV, herein called performance criteria, which can be used to compare existing platforms with each other and estimate the performance of non-existent designs. The scaling laws are derived by evaluating the performance of 229 biological animals, 163 bioinspire platforms, and 109 conventional AUVs. AUVs and bio-inspired platforms have scarce data compared with biological swimmers. Only 5% of conventional and 38% of bio-inspired AUVs have kinematic data while 30% of conventional and 18% of bio-inspired AUVs have energetic data. The low amount of performance criteria data is due to the nature of most conventional AUVs as commercial products. Only recently has the COT metric been included in the performance criteria for bio-inspired AUVs. For this reason, the research here formulates everything in terms of allometric scaling laws. This type of formulation is used extensively when referring to biological systems and is defined by an exponential relationship f (x) = axb, where x is a physical parameter of the fish or vehicle, like length or displacement. Scaling laws have the added benefit of allowing comparisons with limited data, as is the case for AUVs. The length and displacement scale (physical scale) must be established before estimating the performance criteria. Scale is primarily determined by the payload needed for a particular application. For instance, surveying the water column in deep water will require different scientific tools than taking images of an oyster bed in an estuary. There is no way to identify the size of an AUV until it is designed for that application, since these scientific instruments each have their own volume, length, and weight. A methodology for estimating physical parameters using computer vision is presented to help determine the scale for the vehicle. It allows accurate scaling of physical parameters of biological and bio-inspired swimmers with only a side and top view of the platform. A physical scale can also be determined based on the vehicle’s overall volume, which is useful when determining how much payload is needed for a particular application. Further, this can be used in conjunction with 3D modeling software to scale nonexistent platforms. Following the establishment of a physical scale, which locomotion mode would be most appropriate? Unlike conventional AUVs that use propeller or glider locomotion, bio-inspired platforms use a variety of modes. Kinematics and energy expenditures are different for each of these modes. For bio-inspired vehicles, the focus will be on the body-caudal fin (BCF) locomotion, of which four types exist: anguilliform, carangiform, thunniform, and ostraciiform. There is ample research on anguilliform and carangiform locomotion modes, but little research on thunniform and ostraciiform modes. In order to determine which locomotion mode scales best for a bio-inspired AUV, this research examines the power output and kinematic parameters for all four BCF modes. In order to achieve this, computational fluid dynamics simulations are performed on a 2D swimmer for all four modes. Overset meshes are used in lieu of body-fitted meshes to increase stability and decrease computational time. These simulations were used to scale output power over several decades of Reynolds numbers for each locomotion mode. Carangiform locomotion was found to be the most energy efficient, followed by anguilliform, thunniform, and ostraciiform. In order to utilize the above scaling laws in designing a novel platform, or comparing an existing one, there must be a unifying framework. The framework for choosing a suitable platform is presented with a case study of two bio-inspired vehicles and a conventional one. The framework begins by determining how the platform can be physically scaled depending on the payload. Based on the physical scale and derived scaling laws, it then determines performance criteria. It also describes a method for relative cost scaling for each vehicle, which is not covered in the literature. The cost scaling is based on the assumption that all payloads and materials are the same. The case study shows that a conventional AUV performs better on all performance criteria and would cost less to build

Modeling, Control and Energy Efficiency of Underwater Snake Robots

This thesis is mainly motivated by the attribute of the snake robots that they are able to move over land as well as underwater while the physiology of the robot remains the same. This adaptability to different motion demands depending on the environment is one of the main characteristics of the snake robots. In particular, this thesis targets several interesting aspects regarding the modeling, control and energy efficiency of the underwater snake robots. This thesis addresses the problem of modeling the hydrodynamic effects with an analytical perspective and a primary objective to conclude in a closed-form solution for the dynamic model of an underwater snake robot. Two mathematical models of the kinematics and dynamics of underwater snake robots swimming in virtual horizontal and vertical planes aimed at control design are presented. The presented models are derived in a closed-form and can be utilized in modern modelbased control schemes. In addition, these proposed models comprise snake robots moving both on land and in water which makes the model applicable for unified control methods for amphibious snake robots moving both on land and in water. The third model presented in this thesis is based on simplifying assumptions in order to derive a control-oriented model of an underwater snake robot moving in a virtual horizontal plane that is well-suited for control design and stability analysis. The models are analysed using several techniques. An extensive analysis of the model of a fully immersed underwater snake robot moving in a virtual horizontal plane is conducted. Based on this analysis, a set of essential properties that characterize the overall motion of underwater snake robots is derived. An averaging analysis reveals new fundamental properties of underwater snake robot locomotion that are useful from a motion planning perspective. In this thesis, both the motion analysis and control strategies are conducted based on a general sinusoidal motion pattern which can be used for a broad class of motion patterns including lateral undulation and eel-like motion. This thesis proposes and experimentally validates solutions to the path following control problem for biologically inspired swimming snake robots. In particular, line-of-sight (LOS) and integral line-of-sight (I-LOS) guidance laws, which are combined with a sinusoidal gait pattern and a directional controller that steers the robot towards and along the desired path are proposed. An I-LOS path following controller for steering an underwater snake robot along a straight line path in the presence of ocean currents of unknown direction and magnitude is presented and by using a Poincaré map, it is shown that all state variables of an underwater snake robot, except for the position along the desired path, trace out an exponentially stable periodic orbit. Moreover, this thesis presents the combined use of an artificial potential fields-based path planner with a new waypoint guidance strategy for steering an underwater snake robot along a path defined by waypoints interconnected by straight lines. The waypoints are derived by using a path planner based on the artificial potential field method in order to also address the obstacle avoidance problem. Furthermore, this thesis considers the energy efficiency of underwater snake robots. In particular, the relationship between the parameters of the gait patterns, the forward velocity and the energy consumption for the different motion patterns for underwater snake robots is investigated. Based on simulation results, this thesis presents empirical rules to choose the values for the parameters of the motion gait pattern of underwater snake robots. The experimental results support the derived properties regarding the relationship between the gait parameters and the power consumption both for lateral undulation and eel-like motion patterns. Moreover, comparison results are obtained for the total energy consumption and the cost of transportation of underwater snake robots and remotely operated vehicles (ROVs). Furthermore, in this thesis a multi-objective optimization problem is developed with the aim of maximizing the achieved forward velocity of the robot and minimizing the corresponding average power consumption of the system

Physics-based Machine Learning Methods for Control and Sensing in Fish-like Robots

Underwater robots are important for the construction and maintenance of underwater infrastructure, underwater resource extraction, and defense. However, they currently fall far behind biological swimmers such as fish in agility, efficiency, and sensing capabilities. As a result, mimicking the capabilities of biological swimmers has become an area of significant research interest. In this work, we focus specifically on improving the control and sensing capabilities of fish-like robots. Our control work focuses on using the Chaplygin sleigh, a two-dimensional nonholonomic system which has been used to model fish-like swimming, as part of a curriculum to train a reinforcement learning agent to control a fish-like robot to track a prescribed path. The agent is first trained on the Chaplygin sleigh model, which is not an accurate model of the swimming robot but crucially has similar physics; having learned these physics, the agent is then trained on a simulated swimming robot, resulting in faster convergence compared to only training on the simulated swimming robot. Our sensing work separately considers using kinematic data (proprioceptive sensing) and using surface pressure sensors. The effect of a swimming body\u27s internal dynamics on proprioceptive sensing is investigated by collecting time series of kinematic data of both a flexible and rigid body in a water tunnel behind a moving obstacle performing different motions, and using machine learning to classify the motion of the upstream obstacle. This revealed that the flexible body could more effectively classify the motion of the obstacle, even if only one if its internal states is used. We also consider the problem of using time series data from a `lateral line\u27 of pressure sensors on a fish-like body to estimate the position of an upstream obstacle. Feature extraction from the pressure data is attempted with a state-of-the-art convolutional neural network (CNN), and this is compared with using the dominant modes of a Koopman operator constructed on the data as features. It is found that both sets of features achieve similar estimation performance using a dense neural network to perform the estimation. This highlights the potential of the Koopman modes as an interpretable alternative to CNNs for high-dimensional time series. This problem is also extended to inferring the time evolution of the flow field surrounding the body using the same surface measurements, which is performed by first estimating the dominant Koopman modes of the surrounding flow, and using those modes to perform a flow reconstruction. This strategy of mapping from surface to field modes is more interpretable than directly constructing a mapping of unsteady fluid states, and is found to be effective at reconstructing the flow. The sensing frameworks developed as a result of this work allow better awareness of obstacles and flow patterns, knowledge which can inform the generation of paths through the fluid that the developed controller can track, contributing to the autonomy of swimming robots in challenging environments