Li, Liang
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A simple cognitive model explains movement decisions in zebrafish while following leaders
While moving, animals must frequently make decisions about their future travel direction, whether they are alone or in a group. Here we investigate this process for zebrafish (Danio rerio), which naturally move in cohesive groups. Employing state-of-the-art virtual reality, we study how real fish follow one or several moving, virtual conspecifics (leaders). These data are used to inform, and test, a model of social response that includes a process of explicit decision-making, whereby the fish can decide which of the virtual conspecifics to follow, or to follow in some average direction. This approach is in contrast with previous models where the direction of motion was based on a continuous computation, such as directional averaging. Building upon a simplified version of this model [Sridhar et al., 2021], which was limited to a one-dimensional projection of the fish motion, we present here a model that describes the motion of the real fish as it swims freely in two-dimensions. Motivated by experimental observations, the swim speed of the fish in this model uses a burst and-coast swimming pattern, with the burst frequency being dependent on the distance of the fish from the followed conspecific(s). We demonstrate that this model is able to explain the observed spatial distribution of the real fish behind the virtual conspecifics in the experiments, as a function of their average speed and number. In particular, the model naturally explains the observed critical bifurcations for a freely swimming fish, which appear in the spatial distributions whenever the fish makes a decision to follow only one of the virtual conspecifics, instead of following them as an averaged group. This model can provide the foundation for modeling a cohesive shoal of swimming fish, while explicitly describing their directional decision-making process at the individual level.
Bumblebees display characteristics of active vision during robust obstacle avoidance flight
Insects are remarkable flyers and capable of navigating through highly cluttered environments. We tracked the head and thorax of bumblebees freely flying in a tunnel containing vertically oriented obstacles to uncover the sensorimotor strategies used for obstacle detection and collision avoidance. Bumblebees presented all the characteristics of active vision during flight by stabilizing their head relative to the external environment and maintained close alignment between their gaze and flightpath. Head stabilization increased motion contrast of nearby features against the background to enable obstacle detection. As bees approached obstacles, they appeared to modulate avoidance responses based on the relative retinal expansion velocity (RREV) of obstacles and their maximum evasion acceleration was linearly related to RREVmax. Finally, bees prevented collisions through rapid roll manoeuvres implemented by their thorax. Overall, the combination of visuo-motor strategies of bumblebees highlights elegant solutions developed by insects for visually guided flight through cluttered environments.
The geometry of decision-making in individuals and collectives
Choosing among spatially distributed options is a central challenge for animals, from deciding among alternative potential food sources or refuges to choosing with whom to associate. Using an integrated theoretical and experimental approach (employing immersive virtual reality), we consider the interplay between movement and vectorial integration during decision-making regarding two, or more, options in space. In computational models of this process, we reveal the occurrence of spontaneous and abrupt "critical" transitions (associated with specific geometrical relationships) whereby organisms spontaneously switch from averaging vectorial information among, to suddenly excluding one among, the remaining options. This bifurcation process repeats until only one option-the one ultimately selected-remains. Thus, we predict that the brain repeatedly breaks multichoice decisions into a series of binary decisions in space-time. Experiments with fruit flies, desert locusts, and larval zebrafish reveal that they exhibit these same bifurcations, demonstrating that across taxa and ecological contexts, there exist fundamental geometric principles that are essential to explain how, and why, animals move the way they do.
Artificial lateral line based relative state estimation between an upstream oscillating fin and a downstream robotic fish
The lateral line enables fish to efficiently sense the surrounding environment, thus assisting flow-related fish behaviors. Inspired by this phenomenon, varieties of artificial lateral line systems (ALLSs) have been developed and applied to underwater robots. This article focuses on using the pressure sensor arrays based ALLS-measured hydrodynamic pressure variations (HPVs) for estimating the relative states between an upstream oscillating fin and a downstream robotic fish. The HPVs and relative states are measured in flume experiments in which the oscillating fin and the robotic fish have been locate with upstream-downstream formation in a flume. The relative states include the relative oscillating frequency, amplitude, and offset of the upstream oscillating fin to the downstream robotic fish, the relative vertical distance, the relative yaw angle, the relative pitch angle, and the relative roll angle between the upstream oscillating fin and the downstream robotic fish. Regression models between the ALLS-measured and the mentioned relative states are investigated, and regression models-based relative state estimations are conducted. Specifically, two criteria are proposed firstly to investigate not only the sensitivity of each pressure sensor to the variations of relative state but also the insufficiency and redundancy of the pressure sensors. And thus the pressure sensors used for regression analysis are determined. Then four typical regression methods, including random forest (RF) algorithm, support vector regression, back propagation neural network, and multiple linear regression method are used for establishing regression models between the ALLS-measured HPVs and the relative states. Then regression effects of the four methods are compared and discussed. Finally, the RF-based method, which has the best regression effect, is used to estimate the relative yaw angle and oscillating amplitude using the ALLS-measured HPVs and exhibits excellent estimation performance.
DeepShapeKit : accurate 4D shape reconstruction of swimming fish
In this paper, we present methods for capturing 4D body shapes of swimming fish with affordable small training datasets and textureless 2D videos. Automated capture of spatiotemporal animal movements and postures is revolutionizing the study of collective animal behavior. 4D (including 3D space + time) shape data from animals like schooling fish contains a rich array of social and non-social information that can be used to shed light on the fundamental mechanisms underlying collective behavior. However, unlike the large datasets used for 4D shape reconstructions of the human body, there are no large amounts of labeled training datasets for reconstructing fish bodies in 4D, due to the difficulty of underwater data collection. We created a template mesh model using 3D scan data from a real fish, then extracted silhouettes (segmentation masks) and key-points of the fish body using Mask R-CNN and DeepLabCut, respectively. Next, using the Adam optimizer, we optimized the 3D template mesh model for each frame by minimizing the difference between the projected 3D model and the detected silhouettes as well as the key-points. Finally, using an LSTM-based smoother, we generated accurate 4D shapes of schooling fish based on the 3D shapes over each frame. Our results show that the method is effective for 4D shape reconstructions of swimming fish, with greater fidelity than other state-of-the-art algorithms.
Fish can save energy via proprioceptive sensing
Fish have evolved diverse and robust locomotive strategies to swim efficiently in complex fluid environments. However, we know little, if anything, about how these strategies can be achieved. Although most studies suggest that fish rely on the lateral line system to sense local flow and optimise body undulation, recent work has shown that fish are still able to gain benefits from the local flow even with the lateral line impaired. In this paper, we hypothesise that fish can save energy by extracting vortices shed from their neighbours using only simple proprioceptive sensing with the caudal fin. We tested this hypothesis on both computational and robotic fish by synthesising a central pattern generator (CPG) with feedback, proprioceptive sensing, and reinforcement learning. The CPG controller adjusts the body undulation after receiving feedback from the proprioceptive sensing signal, decoded via reinforcement learning. In our study, we consider potential proprioceptive sensing inputs to consist of low-dimensional signals (e.g. perceived forces) detected from the flow. With simulations on a computational robot and experiments on a robotic fish swimming in unknown dynamic flows, we show that the simple proprioceptive sensing is sufficient to optimise the body undulation to save energy, without any input from the lateral line. Our results reveal a new sensory-motor mechanism in schooling fish and shed new light on the strategy of control for robotic fish swimming in complex flows with high efficiency.
Underwater robot coordination using a bio-inspired electrocommunication system
Due to the challenging communication and control systems, few underwater multi-robot coordination systems are currently developed. In nature, weakly electric fish can organize their collective activities using electrocommunication in turbid water. Inspired by this communication mechanism, we developed an artificial electrocommunication system for underwater robots in our previous work. In this study, we coordinate a group of underwater robots using this bio-inspired electrocommunication. We first design a time division multiple access (TDMA) network protocol for electrocommunication to avoid communication conflicts during multi-robot coordination. Then, we revise a distributed controller to coordinate a group of underwater robots. The distributed controller on each robot generates the required controls based on adjacent states obtained through electrocommunication. A central pattern generator (CPG) controller is designed to adjust the speed of individuals according to distributed control law. Simulations and experimental results show that a group of underwater robots is able to achieve coordination with the developed electrocommunication and control systems.
Energy Saving of Schooling Robotic Fish in Three-Dimensional Formations
It has long been proposed that animals flying in the air and swimming in the water could extract energy from neighbour-induced flows. A large number of mechanisms have been proposed to explain whether, and if so how, animals can save energy by moving in two-dimensional (2D) formations-individuals swim in the horizontal plane. Seldom studies explore the mechanisms in three-dimensional (3D) formations-individuals swim in both horizontal and vertical planes, even though most animals perform 3D behaviour. In this letter, taking a pair of bio-inspired robotic fish as experimental physical models, we explore the energy cost of the follower when swimming close to a neighbour in 3D formations (mainly in the vertical plane). We found the cost of the follower is mainly affected by how it spatiotemporally interacts with the 3D vortices shed by the neighbour in 3D formations. A simple linear correlation was found between the spatial factor (the height difference) and temporal factor (the body phase difference) when the follower saves most energy compared to swimming alone. Preliminary flow visualisations and 3D computational fluid dynamic simulations show this is due to the structure of vortices along the span of the caudal fin's trailing edge. Our studies shed new light on the energy saving control of multiple artificial underwater robots in 3D formations.