Shape Classification Using Hydrodynamic Detection via a Sparse Large-Scale 2D-Sensitive Artificial Lateral Line

Artificial lateral lines are fluid flow sensor arrays, bio-inspired by the fish lateral line organ, that measure a local hydrodynamic environment. These arrays are used to detect objects in water, without relying on light, sound, or on an active beacon. This passive sensing method, called hydrodynamic imaging, is complementary to sonar and vision systems and is suitable for collision avoidance and near-field covert sensing. This sensing method has so far been demonstrated on a biological scale from several to tens of centimeters. Here, we present measurements using a large-scale artificial lateral line of 3.5 meters, consisting of eight all-optical 2D-sensitive flow sensors. We measure the fluid flow as produced by the motion of five different objects, towed across a swimming pool. This results in repeatable stimuli, whose measurements demonstrate a complementary aspect of 2D-sensing. These measurements are both used for constructing temporal hydrodynamic signatures, which reflect the object’s shape, and for flow-feature based near-field object classification. For the latter, we present a location-invariant feature extraction method which, using an Extreme Learning Machine neural network, results in a classification F1-score up to 98.6% with selected flow features. We find that, compared to the traditional sensing dimension parallel to the sensor array, the novel transverse fluid velocity component bears more information about the object shape. The classification of objects via hydrodynamic imaging thus benefits from 2D-sensing and can be scaled up to a supra biological scale of several meters

Analytical and Computational Modeling of Robotic Fish Propelled by Soft Actuation Material-based Active Joints", The

Abstract-Soft actuation materials, such as Ionic PolymerMetal Composites (IPMCs), are gaining increasing interest in robotic applications since they lead to compact and biomimetic designs. In this paper, we propose the use of soft actuation materials as active joints for propelling biomimetic robotic fish. An analytical model is developed to compute the thrust force generated by a two-link tail and the resulting moments in the active joints. The computed joint moments can be combined with internal dynamics of actuation materials to provide realistic kinematic constraints for the joints. Computational fluid dynamics (CFD) modeling is also adopted to examine the flow field, the produced thrust, and the bending moments in joints for the two-link tail. Good agreement is achieved between the analytical modeling and the CFD modeling, which points to a promising two-tier framework for the understanding and optimization of robotic fish with a multi-link tail. We also show that, comparing to a one-link bending tail, a two-link tail is able to produce much higher thrust and more versatile maneuvers, such as backward swimming

Collective responses of a large mackerel school depend on the size and speed of a robotic fish but not on tail motion

So far, actuated fish models have been used to study animal interactions in small-scale controlled experiments. This study, conducted in a semi-controlled setting, investigates robot5interactions with a large wild-caught marine fish school (∼3000 individuals) in their natural social environment. Two towed fish robots were used to decouple size, tail motion and speed in a series of sea-cage experiments. Using high-resolution imaging sonar and sonar-video blind scoring, we monitored and classified the school's collective reaction towards the fish robots as attraction or avoidance. We found that two key releasers—the size and the speed of the robotic fish—were responsible for triggering either evasive reactions or following responses. At the same time, we found fish reactions to the tail motion to be insignificant. The fish evaded a fast-moving robot even if it was small. However, mackerels following propensity was greater towards a slow small robot. When moving slowly, the larger robot triggered significantly more avoidance responses than a small robot. Our results suggest that the collective responses of a large school exposed to a robotic fish could be manipulated by tuning two principal releasers—size and speed. These results can help to design experimental methods for in situ observations of wild fish schools or to develop underwater robots for guiding and interacting with free-ranging aggregated aquatic organisms.This work was financed by the Norwegian Research Council (grant 204229/F20) and Estonian Government Target Financing (grant SF0140018s12). JCC was partially supported by a grant from Iceland, Liechtenstein and Norway through the EEA Financial Mechanism, operated by Universidad Complutense de Madrid. We are grateful to A. Totland for his technical help. The animal collection was approved by The Royal Norwegian Ministry of Fisheries, and the experiment was approved by the Norwegian Animal Research Authority. The Institute of Marine Research is permitted to conduct experiments at the Austevoll aquaculture facility by the Norwegian Biological Resource Committee and the Norwegian Animal Research Committee (Forsøksdyrutvalget)

Repeated Path Planning for Mobile Robots in Dynamic Environments

This thesis presents a global navigation strategy for repeated traverse of mobile robots in dynamic environments. The research is motivated by the fact that most real-world applications of mobile robotics imply repeated traverse between predefined target points in large, uncertain real-world domains. The goal of this study is to develop a framework for navigation in large-scale dynamic environments that permits the robot to fulfill its assignment by adapting to use of easily traversable paths. While mobile robots usually tackle the problem of path planning in uncertain environments by implementing obstacle avoidance routines, in this approach the robot also learns to use routes along which obstacles occur less often. The few alternative approaches that consider the problem of global obstacle avoidance function in well-structured environments using an a priori known topological map. The approach reported in this thesis uses a grid-based map for path planning. It is argued that a grid-based map together with a stochastic wavefront planner offers a greater number of alternatives to the path planning problem and that the robot adapts to be able to use the best routes even when very little a priori knowledge of the environment is available. To adapt to dynamic environments the robot uses case-based reasoning to remember the paths followed and to reason about their traversability. While case-based reasoning is usually applied for planning problems in static environments, this approach also works in dynamic environments and uses a unique similarity measure to reduce the solution space. This thesis also offers an alternative decision-making strategy for mobile robots in hazardous environments that is based on the concept of irreversible decisions. The experiments made in simulated and real environments demonstrate that this approach to global navigation permits the robot to increase the predictability of its behavior and to minimize the risk of collision an time delays

Repeated Path Planning for Mobile Robots in Dynamic Environments

This thesis presents a global navigation strategy for repeated traverse of mobile robots in dynamic environments. The research is motivated by the fact that most real-world applications of mobile robotics imply repeated traverse between predefined target points in large, uncertain real-world domains. The goal of this study is to develop a framework for navigation in large-scale dynamic environments that permits the robot to fulfill its assignment by adapting to use of easily traversable paths. While mobile robots usually tackle the problem of path planning in uncertain environments by implementing obstacle avoidance routines, in this approach the robot also learns to use routes along which obstacles occur less often. The few alternative approaches that consider the problem of global obstacle avoidance function in well-structured environments using an a priori known topological map. The approach reported in this thesis uses a grid-based map for path planning. It is argued that a grid-based map together with a stochastic wavefront planner offers a greater number of alternatives to the path planning problem and that the robot adapts to be able to use the best routes even when very little a priori knowledge of the environment is available. To adapt to dynamic environments the robot uses case-based reasoning to remember the paths followed and to reason about their traversability. While case-based reasoning is usually applied for planning problems in static environments, this approach also works in dynamic environments and uses a unique similarity measure to reduce the solution space. This thesis also offers an alternative decision-making strategy for mobile robots in hazardous environments that is based on the concept of irreversible decisions. The experiments made in simulated and real environments demonstrate that this approach to global navigation permits the robot to increase the predictability of its behavior and to minimize the risk of collision an time delays