In computer-assisted intravascular interventional surgery, extracting detailed information of target vessels from X-ray angiographic images can be meaningful in improving safety and effectiveness. However, large amounts of effort have been dedicated to segmenting the whole blood vessels from the background while ignoring the internal structure, which is limited in clinical application. In this paper, we propose a flexible and universal endpoint-based framework for vessel structural information extraction. The framework first localizes all the endpoints of target vessel segments through a Coarse-to-Fine Keypoint Detection Network (CFKD-Net), in which the designed Multi-branch Feature Aggregation (MFA) module captures both in-patch and cross-patch information to help recognize the points of interest based on global structure. A novel MaskMSELoss is also proposed to disambiguate those irrelevant responses. Then a designed VEssel Segmentation and Analysis (VESA) algorithm will generate the segmentation mask and morphological analysis for each vessel segment simply based on the endpoints. It can also be flexibly applied to analyze variant blood vessels which are not pre-defined before. Extensive experiments on two different coronary artery datasets consistently demonstrate that this framework can achieve state-of-the-art detection performance and successfully extract and analyze target vessel segments. Since the framework shows excellent performance on the coronary arteries with severe deformation and strong noise, it is highly promising for analyzing other vascular images.

In this brief, we investigate the parameter optimization issue of a central pattern generator (CPG) network governed forward and backward swimming for a fully untethered, multijoint biomimetic robotic fish. Considering that the CPG parameters are tightly linked to the propulsive performance of the robotic fish, we propose a method for determination of relatively optimized control parameters. Within the framework of evolutionary computation, we use a combination of dynamic model and particle swarm optimization (PSO) algorithm to seek the CPG characteristic parameters for an enhanced performance. The PSO-based optimization scheme is validated with extensive experiments conducted on the actual robotic fish. Noticeably, the optimized results are shown to be superior to previously reported forward and backward swimming speeds.

This paper is devoted to the active turn control of a free-swimming multilink dolphin-like robot, with emphasis on yaw and pitch controls. With full consideration of both mechanical configuration and propulsive principle of the robot consisting of a yaw joint and multiple pitch joints, a viable approach to perform yaw maneuvers via laterally directed biases is formed, providing an advantage in qualitative and quantitative assessment. Meanwhile, based on the feedback of the pitch angle measured by an onboard gyroscope, a closed-loop control strategy in dorsoventral motions is proposed to achieve agile and swift pitch maneuvers. More remarkably, two hybrid acrobatic stunts, i.e., frontflip and backflip, are first implemented on the physical robot. The latest results obtained demonstrate the effectiveness of the proposed methods. It is also confirmed that the dolphin robot achieves better performance for pitch maneuvers than it does for yaw maneuvers, agreeing well with the biological observations.

In this paper, we present the formulation of a dynamic model of a free-swimming multi-joint robotic fish with a pair of wing-like pectoral fins, in which the whole robot is regarded as a moving multilink rigid body in fluids. Considering that the thrust of fish mainly results from the force of trailing vortex, added lateral pressure, and leading-edge suction force, the dynamic equations of the swimming fish have been derived by summing up the longitudinal force, lateral force, and yaw moment on each propulsive component in the framework of Lagrangian mechanics. Furthermore, using the bio-inspired Central Pattern Generators (CPGs) as the swimming data generator, the overall dynamic propulsive characteristics of the swimming robot are estimated in a mathematical environment (i.e., Mathematica). As a case study, the created dynamic model offers a good guide to seeking pragmatic backward swimming patterns for a carangiform robotic fish, which exemplifies the validity of the CPG-coupled dynamic model.

Presented in this paper is our effort to create a multifunctional swimming robot, i.e., robotic fish, inspired by the well-integrated, configurable multiple control surfaces existing in real fish. By virtue of the hybrid propulsion capability in the tail plus the caudal fin and the maneuverability in accessory fins, a novel, synthesized propulsion scheme composed of multiple artificial control surfaces is proposed, involving the tail plus the caudal fin, pectoral fins, pelvic fin, and dorsal fin. Multimodal locomotion is then accomplished by manipulation of control surfaces, separately or cooperatively, allowing the robot to maneuver more diversely and agilely. In particular, bio inspired Central Pattern Generators (CPGs) based locomotion control is adopted for online swimming gait generation. Aquatic testing has been carried out to demonstrate the improved maneuverability and stability of the robotic fish underwater as well as the effectiveness of the conceived multi-fin mechatronic design.

This paper presents a new turning maneuver generation method for a multilink biomimetic robotic fish, in which smooth step functions are introduced to dynamically trigger directed offsets in active and asymmetric swimming. With the proposed method, three basic turning modes can be unified into a general framework by choosing appropriate step-function combinations and dynamic bias. Furthermore, this method can be employed to maneuver the robotic fish agilely in the path planning, which promises more flexibility and steadiness in potential applications to bio-inspired autonomous underwater vehicles.