Tianjiang Hu

Ground Stereo Vision-Based Navigation for Autonomous Take-off and Landing of UAVs: A Chan-Vese Model Approach

This article aims at flying target detection and localization of a fixed-wing unmanned aerial vehicle (UAV) autonomous take-off and landing within Global Navigation Satellite System (GNSS)-denied environments. A Chan-Vese model–based approach is proposed and developed for ground stereo vision detection. Extended Kalman Filter (EKF) is fused into state estimation to reduce the localization inaccuracy caused by measurement errors of object detection and Pan-Tilt unit (PTU) attitudes. Furthermore, the region-of-interest (ROI) setting up is conducted to improve the real-time capability. The present work contributes to real-time, accurate and robust features, compared with our previous works. Both offline and online experimental results validate the effectiveness and better performances of the proposed method against the traditional triangulation-based localization algorithm.

Bio-inspired Flow Sensing and Prediction for Fish-like Undulating Locomotion: A CFD-aided Approach

Feedback flow information is of significance to enable underwater locomotion controllers with higher adaptability and efficiency within varying environments. Inspired from fish sensing their external flow via near-body pressure, a computational scheme is proposed and developed in this paper. In conjunction with the scheme, Computational Fluid Dynamics (CFD) is employed to study the bio-inspired fish swimming hydrodynamics. The spatial distribution and temporal variation of the near-body pressure of fish are studied over the whole computational domain. Furthermore, a filtering algorithm is designed and implemented to fuse near-body pressure of one or multiple points for the estimation on the external flow. The simulation results demonstrate that the proposed computational scheme and its corresponding algorithm are both effective to predict the inlet flow velocity by using near-body pressure at distributed spatial points.

Computational Hydrodynamics and Statistical Modeling on Biologically Inspired Undulating Robotic Fins: A Two-Dimensional Study

Undulation fishes, whose propulsion is mainly achieved by undulating ribbon fins, are good at maneuvering or stabilizing at low speeds. This paper suggests and proposes a two-dimensional approximate computational model, which is used to conduct an initial analysis on undulation propulsion scheme. It is believed that this undulating mode has a better potential for exploitation in artificial underwater systems. Hydrodynamics of two-dimensional undulating fins under a series of kinematical parameter sets is explored via numerical simulation. The periodicity of undulation forces and moments is studied. The effects of inlet velocity, wavelength, undulation frequency, and undulation amplitude are investigated. Furthermore, a dimensionless two-parameter model for undulation surge force is established with a given wavelength (in terms of, a single wavelength or a dual wavelength) using statistical method. The work in this paper is able to provide studies on bionic undulation mode. It has also formed a meaningful basis for three-dimensional (3D) hydrodynamics and corresponding control methods in bionic undulation robots.

Stereo Vision Guiding for the Autonomous Landing of Fixed-Wing UAVs: A Saliency-Inspired Approach

It is an important criterion for unmanned aerial vehicles (UAVs) to land on the runway safely. This paper concentrates on stereo vision localization of a fixed-wing UAVs autonomous landing within global navigation satellite system (GNSS) denied environments. A ground stereo vision guidance system imitating the human visual system (HVS) is presented for the autonomous landing of fixed-wing UAVs. A saliency-inspired algorithm is presented and developed to detect flying UAV targets in captured sequential images. Furthermore, an extended Kalman filter (EKF) based state estimation is employed to reduce localization errors caused by measurement errors of object detection and pan-tilt unit (PTU) attitudes. Finally, stereo-vision-dataset-based experiments are conducted to verify the effectiveness of the proposed visual detection method and error correction algorithm. The compared results between the visual guidance approach and differential GPS-based approach indicate that the stereo vision system and detection method can achieve the better guiding effect.

Effective motion control of the biomimetic undulating fin via iterative learning

The biomimetic undulating fin, RoboGnilos, is inspired by natural fish that generally swim via undulations of a long dorsal or anal fin. However, the present performance of this fin-type underwater propulsor can hardly be satisfactory in velocity, efficiency, or maneuverability, and retains a long distance to practical applications. This paper examines the dynamics of the undulating fin, and proposes an iterative learning approach based motion control to improve its steady propulsion velocity. This iterative learning controller is cooperated with a filter, to reduce the measurement noise, and a curve fitting component, to keep the necessary phase difference between neighbored fin rays. The detailed iterative learning based motion control algorithm is designed and implemented in the biomimetic undulating fin. The experimental results validate that the proposed learning motion control can effectively improve the propulsion of RoboGnilos. For instance, the steady propulsion velocity may be enhanced by over 40% with specified parameters.

Learning Control for Biomimetic Undulating Fins: An Experimental Study

Learning control should focus on imitating natural fish’s adaptability to complex and dynamic environment to some extent, rather than mimicking streamlined shapes or specific actuators to develop more mechanical prototypes. In this paper, an experimental study on a proposed learning control of the robotic undulating fin, RoboGnilos, is suggested and explored. This study takes inspirations from biological world to practical control algorithms. In detail, an iterative learning scheme based control is studied with the cooperation of a filter to reduce the measurement noise, and a curve fitting component to keep the necessary phase difference between neighboring fin rays. Moreover, the iterative learning control algorithm is designed and implemented for practical applications. The experimental results validate that the proposed learning control can effectively improve the propulsion of RoboGnilos. For instance, the steady propulsion velocity may be enhanced by over 40% with some specified parameters.

Localization Framework for Real-Time UAV Autonomous Landing: An On-Ground Deployed Visual Approach

One of the greatest challenges for fixed-wing unmanned aircraft vehicles (UAVs) is safe landing. Hereafter, an on-ground deployed visual approach is developed in this paper. This approach is definitely suitable for landing within the global navigation satellite system (GNSS)-denied environments. As for applications, the deployed guidance system makes full use of the ground computing resource and feedbacks the aircraft’s real-time localization to its on-board autopilot. Under such circumstances, a separate long baseline stereo architecture is proposed to possess an extendable baseline and wide-angle field of view (FOV) against the traditional fixed baseline schemes. Furthermore, accuracy evaluation of the new type of architecture is conducted by theoretical modeling and computational analysis. Dataset-driven experimental results demonstrate the feasibility and effectiveness of the developed approach.

Kinematic modeling and dynamic analysis of the long-based undulation fin of Gymnarchus Niloticus

Within median and/or paired fin (MPF) propulsion, many fish routinely use the long-based undulatory fins as the sole means of locomotion In this paper, the long-based undulatory fin of an Amiiform fish“G niloticus”was investigated We brought forward a simplified physical model and a kinematic model to simulate the undulations of the long-based dorsal fin Further, the equilibrium equations of the undulatory fin were obtained by applying the membrane theory of thin shells in which the geometrical non-linearity of the structure is taken into account Last, we apply the derived kinematic model and equilibrium equations of the undulatory fin to analyze the thrust and propulsive efficiency varying with the aspect ratio of the fin and the maximum swing amplitude.

BioDKM: Bio-inspired domain knowledge modeling method for humanoid delivery robots' planning

A bio-inspired human domain knowledge modeling method, BioDKM, is proposed and developed to make delivery robots think more humanly and act more effectively. This presented method focused on feasible fusion between artificial intelligent and bionics in the field of tasks planning or scheduling in delivery robots. BioDKM is designed and implemented with several components, in terms of human knowledge, workflow (WF), hierarchical task network (HTN), and planner. In detail, WF is utilized as the human domain knowledge modeling tool, because of its convenient applications, friendly user interface and explicit representation. Moreover, WF can effectively complement conventional HTN planning with great convenience to formalize human domain knowledge. Translation from WF to HTN is also considered and established to make task planning smooth. Finally, examples and simulations are carried out to validate the effectiveness of this proposed bio-inspired domain knowledge modeling method.

Iterative learning control for a class of systems with hysteresis

Hysteresis characteristics is highly nonlinear, has memory and is common in engineering systems. Its presence introduces uncertainties and nonlinearity in dynamic modelling and thus difficulties in achieving a good control design. This paper studies the suitability of iterative learning control (ILC) to compensate hysteresis uncertainties for a class of continuous-time dynamic systems. We examine dynamic systems with Preisach model hysteresis nonlinearity. It is shown that this class of systems possess properties of continuity and repeatability which are required for ILC. Furthermore, anticipatory iterative learning control (or A-type ILC) is applied to overcome the uncertainties and nonlinearity introduced by hysteresis. Simulation results are presented to validate the effectiveness of ILC laws to eliminate tracking error due to hysteresis uncertainties.