Hengli Liu

Autonomous obstacle avoidance of an unmanned surface vehicle based on cooperative manoeuvring

Autonomous obstacle avoidance is important in unmanned surface vehicle (USV) navigation. Although the result of obstacle detection is often inaccurate because of the inherent errors of LIDAR, conventional methods typically emphasize on a single obstacle-avoidance algorithm and neglect the limitation of sensors and safety in a local region. Conventional methods also fail in seamlessly integrating local and global obstacle avoidance algorithms. This paper aims to present a cooperative manoeuvring approach including both local and global obstacle avoidance.,The global algorithm used in our USV is the Artificial Potential Field-Ant Colony Optimization (APF-ACO) obstacle-avoidance algorithm, which plans a relative optimal path on the specified electronic map before the cruise of USV. The local algorithm is a multi-layer obstacle-avoidance framework based on a single LIDAR to present an efficient solution to USV path planning in the case of sensor errors and collision risks. When obstacles are within a layer, the USV uses a corresponding obstacle-avoidance algorithm. Then the USV moves towards the global direction according to fuzzy rules in the fuzzy layer.,The presented method offers a solution for obstacle avoidance in a complex environment. The USV follows the global trajectory planed by the APF-ACO algorithm. While, the USV can bypass current obstacle in the local region based on the multi-layer method effectively. This fact was validated by simulations and field trials.,The method presented in this paper takes advantage of algorithm integration that remedies errors of obstacle detection. Simulation and experiments were also conducted for performance evaluation.

People detection and tracking using RGB-D cameras for mobile robots:

People detection and tracking is an essential capability for mobile robots in order to achieve natural human–robot interaction. In this article, a human detection and tracking system is designed and validated for mobile robots using color data with depth information RGB-depth (RGB-D) cameras. The whole framework is composed of human detection, tracking and re-identification. Firstly, ground points and ceiling planes are removed to reduce computation effort. A prior-knowledge guided random sample consensus fitting algorithm is used to detect the ground plane and ceiling points. All left points are projected onto the ground plane and subclusters are segmented for candidate detection. Meanshift clustering with an Epanechnikov kernel is conducted to partition different points into subclusters. We propose the new idea of spatial region of interest plan view maps which are employed to identify human candidates from point cloud subclusters. Here, a depth-weighted histogram is extracted online to feature a human candidate. Then, a particle filter algorithm is adopted to track the human’s motion. The integration of the depth-weighted histogram and particle filter provides a precise tool to track the motion of human objects. Finally, data association is set up to re-identify humans who are tracked. Extensive experiments are conducted to demonstrate the effectiveness and robustness of our human detection and tracking system.

A novel algorithm of autonomous obstacle-avoidance for mobile robot based on LIDAR data

Autonomous obstacle-avoidance is an important problem of mobile robot (MR) navigation, of which LIDAR is a kind of key equipment. A mobile robot can implement obstacle-avoidance behaviors with a specific algorithm based on LIDAR data. However, a mobile robot may encounter local minimum because of unexpected environment, and the algorithm only gets the suboptimal solution. Besides, it cannot avoid the current obstacles accurately due to measuring errors of LIDAR. To solve the problem, a novel integrated algorithm based on laser data is proposed in this paper. The simulation and experiment demonstrate that the integrated algorithm is feasible.

A novel method of unmanned surface vehicle autonomous cruise

Purpose – This paper aims to propose a new method for combining global path planning with local path planning, to provide an efficient solution for unmanned surface vehicle (USV) path planning despite the changeable environment. Path planning is the key issue of USV navigation. A lot of research works were done on the global and local path planning. However, little attention was given to combining global path planning with local path planning. Design/methodology/approach – A search of shortcut Dijkstra algorithm was used to control the USV in the global path planning. When the USV encounters unknown obstacles, it switches to our modified artificial potential field (APF) algorithm for local path planning. The combinatorial method improves the approach of USV path planning in complex environment. Findings – The method in this paper offers a solution to the issue of path planning in changeable or unchangeable environment, and was confirmed by simulations and experiments. The USV follows the global path based...

Denoising and tracking of sonar video imagery for underwater security monitoring systems

As the acoustic images are low-quality, it is difficult to use these images for scientific research and practical applications directly. Although the PSNR of sonar images were improved through existing methods, denoised images were lack of clarity so that the outline of objects and details had not been better preserved. Subsequently, this impacted accuracy of target detection. In this paper, we propose a collaborative tracking algorithm based on Mean-Shift and reference-template-based matching (RTM). We construct a set of underwater security monitoring sonar system and carry out our experiment in Huangpu River. The algorithm represented in this paper can effectively improve the signal noise ratio of the sonar image, an increase strength of about 3 ~ 4db. Target tracking satisfies the real-time requirements, which shows our method is effective.

People perception from RGB-D cameras for mobile robots

Understanding how humans move through the scene is a key issue of decision-making for an autonomous mobile robot in crown people zones. So accurately detecting and tracking people from a mobile platform can help improve interaction effective and efficient. In this paper, we proposed a people detection and tracking system using combination of a several new techniques for mobile robots, plan-view maps, depth weighted histograms, and GNN data association. We proposed a spatial region of interest based plan-view maps to detect human candidates. Firstly, point cloud sub-clusters were segmented for candidate detection. Two different plan-view maps, named occupancy map and height map, were employed to identify human candidates from point cloud sub-clusters. Meanwhile, a depth weighted histogram was extracted to feature a human candidate. Then, a particle filter algorithm was adopted to track humans motion. Finally, data association was set up to re-identify humans which were tracked. Extensive experiments demonstrated the effectiveness and robustness of our human detection and tracking system.

A method of vision-based navigation for rescue robots using motion information

An effective and accurate navigation for rescue robots in some human inaccessible sites is very necessary. This paper proposes a method of vision-based navigation with high accuracy and real-time capability. Firstly, the system overview and the experimental platform are presented. Then an image processing algorithm using classic theories, which resulted in less calculated amount, is introduced to detect the guidance line. Lastly, the control model based on the results of image processing and motion information of the robot which makes the control more precise is developed. The results of the experiments show the feasibility of this method.

Symmetric Kullback-Leibler Metric Based Tracking Behaviors for Bioinspired Robotic Eyes

A symmetric Kullback-Leibler metric based tracking system, capable of tracking moving targets, is presented for a bionic spherical parallel mechanism to minimize a tracking error function to simulate smooth pursuit of human eyes. More specifically, we propose a real-time moving target tracking algorithm which utilizes spatial histograms taking into account symmetric Kullback-Leibler metric. In the proposed algorithm, the key spatial histograms are extracted and taken into particle filtering framework. Once the target is identified, an image-based control scheme is implemented to drive bionic spherical parallel mechanism such that the identified target is to be tracked at the center of the captured images. Meanwhile, the robot motion information is fed forward to develop an adaptive smooth tracking controller inspired by the Vestibuloocular Reflex mechanism. The proposed tracking system is designed to make the robot track dynamic objects when the robot travels through transmittable terrains, especially bumpy environment. To perform bumpy-resist capability under the condition of violent attitude variation when the robot works in the bumpy environment mentioned, experimental results demonstrate the effectiveness and robustness of our bioinspired tracking system using bionic spherical parallel mechanism inspired by head-eye coordination.

Development of an image-based visual servoing for moving target tracking based on bionic spherical parallel mechanism

An Image-based visual servo system is presented for a bionic spherical parallel mechanism to minimize a tracking error function to analog smooth pursuit of human eye, which is also called eye-in-hand visual servoing, capable of tracking moving target. More specially, we propose a real-time moving target tracking algorithms which utilizes perceptive image hash based on Discrete Cousin Transform, collaborative in particle filtering framework to achieve automatic moving target detection. In the proposed algorithm, the key geometry position features of the target are extracted to detect and identify the target. Once the target is identified, an image-based control scheme is implemented to drive bionic spherical parallel mechanism such that the identified target is to be tracked at the center of the captured images. Experimental results demonstrate the effectiveness and robustness of our visual servo system for bionic spherical parallel mechanism.

System design and study of bionic eye based on spherical ultrasonic motor using closed-loop control

We present a novel bionic eye based on spherical ultrasonic motor (SUSM). SUSM is a compact mechanism occupying little space but good responsiveness, high positioning accuracy, high torque at low speed and strong magnetic field compatibility. With those advantages, the bionic eye easily implements some human eye movements. It is constructed from an eyeball, three driving stators, three preloaded apparatuses and some supports, and has three degrees of freedom (3-DOF). The eyeball is driven by frictional forces from three same annular stators attached with several piezoelectric elements. A velocity closed-loop control by piezoelectric element is utilized to improve the eyeballs rotational velocity driven by each stator. An attitude sensor is mounted in the eyeball to improve its integrated position accuracy by a position closed-loop control method. The experimental results indicate the availability of our bionic eye and the validity of the closed-loop control methods.