Abstract: A water-air amphibious cross-medium bio-robotic flying fish includes a body, pitching pectoral fins, variable-structure pectoral fins, a caudal propulsion module, a sensor module and a controller. The caudal propulsion module is controlled to achieve underwater fish-like body-caudal fin (BCF) propulsion, and the variable-structure pectoral fins is adjusted to achieve air gliding and fast splash-down diving motions of the bio-robotic flying fish. The coordination between the caudal propulsion module and the pitching pectoral fins is controlled to achieve the motion of leaping out of water during water-air cross-medium transition. The ambient environment is detected by the sensor module, and the motion mode of the bio-robotic flying fish is controlled by the controller.

Abstract: A control method, system and device for a flexible carbon cantilever beam actuated by a smart material is provided. The new control method, system and device aims to solve the problems of control overflow and instability that are likely to occur in the distributed parameter system constructed in the prior art. The method includes: acquiring an elastic displacement of the flexible carbon cantilever beam in real time as input data; and obtaining, based on the input data, a control torque through a distributed parameter model constructed in advance, and performing vibration control on the flexible carbon cantilever beam. The new control method, system and device improves the control accuracy and stability of the distributed parameter system.

Abstract: A single-stage small-sample-object detection method based on decoupled metric is provided to solve the following problems: low detection accuracy of existing small-sample-object detection methods, the mutual interference between classification and regression in a non-decoupled form, and over-fitting during training of a detection network in case of small samples. The method includes: obtaining a to-be-detected image as an input image; and obtaining a class and a regression box corresponding to each to-be-detected object in the input image through a pre-constructed small-sample-object detection network DMNet, where the DMNet includes a multi-scale feature extraction network, a decoupled representation transformation module, an image-level distance metric learning module and a regression box prediction module.

Abstract: A water-air amphibious cross-medium bio-robotic flying fish includes a body, pitching pectoral fins, variable-structure pectoral fins, a caudal propulsion module, a sensor module and a controller. The caudal propulsion module is controlled to achieve underwater fish-like body-caudal fin (BCF) propulsion, and the variable-structure pectoral fins is adjusted to achieve air gliding and fast splash-down diving motions of the bio-robotic flying fish. The coordination between the caudal propulsion module and the pitching pectoral fins is controlled to achieve the motion of leaping out of water during water-air cross-medium transition. The ambient environment is detected by the sensor module, and the motion mode of the bio-robotic flying fish is controlled by the controller.

Abstract: A biomimetic robotic manta ray includes a head cabin, a central cabin, a pair of pectoral fins and a caudal fin cabin. The pectoral fin includes a crank-rocker mechanism and a bevel gear mechanism. The biomimetic robotic manta ray achieves undulatory propulsion through a coordinated periodic motion of the crank-rocker mechanism. A complex closed motion trail of the tail end of the pectoral fin of the manta ray is traced through the coordination of the bevel gear mechanism and the crank-rocker mechanism. The biomimetic robotic manta ray achieves a combined motion of two vertical undulations superimposed on the pectoral fin of a natural manta ray. The motion trail, which has an important effect on the efficient motion of the manta ray, of the tail end of the pectoral fin is approximately simulated.

Abstract: A biomimetic robotic manta ray includes a head cabin, a central cabin, a pair of pectoral fins and a caudal fin cabin. The pectoral fin includes a crank-rocker mechanism and a bevel gear mechanism. The biomimetic robotic manta ray achieves undulatory propulsion through a coordinated periodic motion of the crank-rocker mechanism. A complex closed motion trail of the tail end of the pectoral fin of the manta ray is traced through the coordination of the bevel gear mechanism and the crank-rocker mechanism. The biomimetic robotic manta ray achieves a combined motion of two vertical undulations superimposed on the pectoral fin of a natural manta ray. The motion trail, which has an important effect on the efficient motion of the manta ray, of the tail end of the pectoral fin is approximately simulated.

Abstract: A motion control method and system for a biomimetic robotic fish based on an adversarial structured control, includes: taking the accuracy and speed of motion to the target point as a reward term, and taking a power sum of servomotors as a loss term to construct an optimization objective function; optimizing parameters of a central pattern generator model that generates a global control quantity of a servomotor, after curing its parameters, optimizing the parameters of the servomotor compensation control model; iteratively optimizing the parameters of the model; obtaining the global control signal and compensation control signal of the biomimetic robotic fish through the trained model, and using the linear combination of the two sets of output signals as the control signal of the servomotor of the robotic fish to realize the motion control of the fish.

Abstract: A gliding depth control method for a biomimetic gliding robotic dolphin includes: obtaining a preset gliding depth and a preset yaw angle; obtaining an estimated velocity by a sliding mode observer based on depth information and inertial navigation information, and obtaining a control quantity of pectoral fins on both sides of the biomimetic gliding robotic dolphin by a yaw controller in combination with the preset yaw angle; obtaining a segmented diving velocity reference trajectory by constructing and segmenting a Bézier curve; obtaining a diving control quantity by a model predictive control method in combination with the estimated velocity; obtaining a target position of a piston through a buoyancy principle, and obtaining a control quantity of the piston according to a current position of the piston; and controlling the biomimetic gliding robotic dolphin to glide based on the control quantity of the piston and the control quantity of the pectoral fins.