Chaolei Wang

Novel Method for the Modeling and Control Investigation of Efficient Swimming for Robotic Fish

In this paper, analytical techniques and fuzzy logic method are applied to the dynamic modeling and efficient swimming control of a robotic fish. The bioinspired robotic fish, which follows an exact replica of a live mackerel (Scomber scombrus), is modeled by treating the undulating body and flapping tail independently using analytical methods. Comparing the results of simulations and experiments shows the feasibility of the dynamic model. Using this model, we found that the harmonic control of the Strouhal number and caudal fin angle of attack is a principal mechanism through which the robotic fish can obtain high thrust efficiency while swimming. The fuzzy control method, which is based on the knowledge of the robotic fishs dynamic behavior, has successfully utilized this principal mechanism. By comparing the thrust performance of the robotic fish with different control methods via simulation, we established that the fuzzy controller was able to achieve faster acceleration and smaller steady-state error than what could be achieved from an open-loop and conventional proportional-integral-derivative controller. The thrust efficiency during steady state was superior to that with conventional control methods. We also found that, when using the fuzzy control method, robotic fish can always swim near a “universal” Strouhal number that approximates to the swimming of live fish.

Monocular vision and IMU based navigation for a small unmanned helicopter

Focusing on the problem of autonomous flight for a small unmanned helicopter in GPS-denied environments, this paper proposes a navigation system which combines monocular visual and inertial measurements. The homography based visual odometry, as well as the inertial measurement from an IMU, can estimate the motion of a small unmanned helicopter. An extended Kalman filter is used to fuse the estimations of both visual and inertial sensors to provide the available navigation data. Finally, the effectiveness of this system is verified by real experiments. The Vision/IMU navigation has almost the same performance as the traditional GPS/IMU navigation.

Vision-Aided Inertial Navigation for Small Unmanned Aerial Vehicles in GPS-Denied Environments

This paper presents a vision-aided inertial navigation system for small unmanned aerial vehicles (UAVs) in GPS-denied environments. During visual estimation, image features in consecutive frames are detected and matched to estimate the motion of the vehicle with a homography-based approach. Afterwards, the visual measurement is fused with the output of an inertial measurement unit (IMU) by an indirect extended Kalman filter (EKF). A delay-based approach for the measurement update is developed to introduce the visual measurement into the fusion without state augmentation. This method supposes that the estimated error state is stable and invariant during the second half of one visual calculation period. Simulation results indicate that delay-based navigation can reduce the computational complexity by about 20% compared with general augmented Vision/INS (inertial navigation system) navigation, with almost the same estimate accuracy. Real experiments were also carried out to test the performance of the proposed navigation system by comparison with the augmented filter method and a referential GPS/INS navigation.

Monocular visual SLAM for small UAVs in GPS-denied environments

This paper presents a monocular visual simultaneous localization and mapping (SLAM) system for a small unmanned aerial vehicle (UAV) in GPS-denied environments. A single camera is used to measure the motion of the vehicle and detect features (landmarks) for the map building. The SLAM estimates the positions of the UAV and the features by an extended Kalman filter (EKF), which takes the velocity estimated by the fusion of the inertial and visual measurements as the input. An inverse depth method is adopted for the feature initialization. Both simulations and experiments are carried out to verify the effectiveness of this system.

Rao-Blackwellized visual SLAM for small UAVs with vehicle model partition

Purpose – The purpose of this paper is to present a Rao–Blackwellized particle filter (RBPF) approach for the visual simultaneous localization and mapping (SLAM) of small unmanned aerial vehicles (UAVs). Design/methodology/approach – Measurements from inertial measurement unit, barometric altimeter and monocular camera are fused to estimate the state of the vehicle while building a feature map. In this SLAM framework, an extra factorization method is proposed to partition the vehicle model into subspaces as the internal and external states. The internal state is estimated by an extended Kalman filter (EKF). A particle filter is employed for the external state estimation and parallel EKFs are for the map management. Findings – Simulation results indicate that the proposed approach is more stable and accurate than other existing marginalized particle filter-based SLAM algorithms. Experiments are also carried out to verify the effectiveness of this SLAM method by comparing with a referential global positioni...

A fuzzy-based threshold method applied in SIFT for visual navigation of small UAVs

In this paper, a fuzzy-based threshold method is proposed in image processing of SIFT which is used in visual navigation of small UAVs. By adjusting the thresholds of SIFT online fuzzily, it provides a stable number of features detected from aerial image sequence. The visual navigation estimates the motion of the small UAV by tracking and matching these features extracted from the consecutive image frames. Real experiments verify the stability and advantage of this method in comparison with the fixed and PID-based threshold methods. This proposed method is able to stabilize the number of features regardless of the changes of the aerial images.

Structural target recognition algorithm for visual guidance of small unmanned helicopters

In this paper we present a vision based guidance approach for small unmanned helicopters. The approach is integrated with Hu invariant moments based structural target recognition algorithm for visual acquisition of the target (a helipad) and a guidance model for locating the target. A color filter is designed to eliminate noises from the image by using HSL model. The improved structural target recognition algorithm can work robustly under the condition that the image is distorted by perspective projection. Besides, a visual guidance model is designed and implemented to locate the target accurately. With the help of the visual guidance, small unmanned helicopters can land on the target automatically. Finally, experiments are carried out to test the effectiveness and robustness of our algorithms.

A fuzzy robust path following controller for a small unmanned air vehicle

This paper proposes a nonlinear controller that allows the unmanned air vehicle (UAV) to follow a predefined path. The controller only needs the information of the inertial speed of the UAV and the distance between the vehicle and the desired path. In order to improve the performance of the controller, a parameter of the controller is adjusted by a fuzzy logic rule. Lyapunov stability arguments are used to demonstrate that the path following error will be regulated to zero, even in the presence of wind disturbances. Simulation results in Matlab are given to show the effectiveness of the controller.

Planar Smooth Path Guidance Law for a Small Unmanned Aerial Vehicle with Parameter Tuned by Fuzzy Logic

A guidance law has been designed to guide the small unmanned aerial vehicle towards the predefined horizontal smooth path. The guidance law only needs the mathematical expression for the predefined path, the positions, and the velocities of the vehicle in the horizontal inertial frame. The stability of the guidance law has been demonstrated by the Lyapunov stability arguments. In order to improve the path following performance, one of the parameters of the guidance law is tuned by using the fuzzy logic which will still keep its stability. The simulation experiments in the Matlab/Simulink environment to realize the square-, circular-, and the athletics track-style paths following are given to verify the effectiveness of the proposed method. The simulation results show that the path following performance will be improved with smaller overshoot and oscillation amplitude and shorter arrival time with the parameter tuned.

Combined of Lyapunov-stable and active disturbance rejection control for the path following of a small unmanned aerial vehicle

This article proposes a composite path following controller that allows the small fixed-wing unmanned aerial vehicle to follow a predefined path. Assuming that the vehicle is equipped with an autopilot for altitude and airspeed maintained well, the controller design adopts the hierarchical control structure. With the inner-loop controller design based on the notion of active disturbance rejection control which will respond to the desired roll angle command, the core part of the outer-loop controller is designed based on Lyapunov stability theorem to generate the desired course rate for the straight-line paths. The bank to turn maneuver is used to transform the desired course rate to the desired roll angle command. Both the hardware-in-the-loop simulation in the X-Plane simulator and actual experimental flight tests have been successfully achieved, which verified the effectiveness of the proposed method.