publications

2021

1. Int.J.Intell.Syst.

   Online human action recognition with spatial and temporal skeleton features using a distributed camera network

   Liu, Guoliang, Zhang, Qinghui, Cao, Yichao, Tian, Guohui, and Ji, Ze

   International Journal of Intelligent Systems 2021
2. TAROS

   Reinforcement Learning-based Mapless Navigation with Fail-safe Localisation

   Lin, F., Ji, Z., Wei, C., and Niu, H.

   In Towards Autonomous Robotic Systems Conference (TAROS) 2021
3. TAROS

   Designing a Multi-Locomotion Modular Snake Robot

   Djoudi, S., Frampton, J.M., Murphy, A., Scammell, T., Wright, T., and Ji, Z

   In Towards Autonomous Robotic Systems Conference (TAROS) 2021
4. IROS

   ShorelineNet: An Efficient Deep Learning Approach for Shoreline Semantic Segmentation for Unmanned Surface Vehicles

   Yao, L., Dimitrios, K., Ji, Z., and Liu, Y.

   In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems 2021
5. TAROS

   An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with Pybullet

   Yang, Xintong, Ji, Ze, Wu, Jing, and Lai, Yu-Kun

   In Towards Autonomous Robotic Systems Conference (TAROS) 2021
6. TNNLS

   Hierarchical reinforcement learning with universal policies for multi-step robotic manipulation

   Yang, Xintong., Ji, Z., Wu, J., Lai, Y.K., Wei, C., Liu, G., and Setch, R.

   IEEE Transaction on Neural Networks and Learning Systems 2021
7. OE

   Model predictive control for slurry pipeline transportation of a cutter suction dredger

   Wei, Changyun, Wei, Yi, and Ji, Ze

   Ocean Engineering 2021

   Abs

   Cutter Suction Dredgers (CSDs) are a special type of ships designed for construction and maintenance projects of ocean and offshore engineering. During the dredging operation, CSDs can excavate nearly all kinds of soil on the sea bed, and then the dredged materials with coarse particles need to be sucked up by a slurry pump and transported to a disposal area through a long-distance pipeline. In order to avoid sedimentation of slurry in pipeline transportation, the flow rate must be maintained within a reasonable range. Otherwise, the pipeline can be blocked when the slurry density is too high. In this paper, we present a Model Predictive Control (MPC) approach to manipulate the flow rate of slurry in pipeline transportation for a CSD. To demonstrate the advantages of our proposed approach, we also implement three Proportional Integral Derivative (PID) controllers (i.e., conventional PID, Fuzzy-PID, and LQR-PID) to make a direct comparison. Moreover, in order to evaluate the effectiveness of our proposed approach in real scenarios, we have, in particular, built a slurry pipeline transportation platform. Both the simulation and experimental results show that our proposed MPC approach is more effective than other PID controllers in controlling the flow rate in the slurry pipeline transportation problem. The proposed approach can provide a guideline for the automated control of the slurry pump for a CSD.

8. Sens. Actuator

   Self-validating high-g accelerometers through data-driven methods

   Wen, Jingjing, Yao, Houpu, Ji, Ze, Wu, Bin, and Xu, Feng

   Sensors and Actuators A: Physical 2021

   Abs

   In the aerospace industry, pyroshock testing is an indispensable step in designing space electronics. Yet, damages in high-g accelerometers, the core measuring instruments in pyroshock test systems, could result in various failures of pyroshock tests. To ensure the reliability of pyroschock tests for space electronics, a machine learning system is proposed to perform self-validation for high-g accelerometers. In this work, self-validation refers to the capability of identifying five key parameters, namely the validated shock signal, the validated uncertainty, the measurement status, the raw shock signal and the fault type, synchronously during measuring shock signals in pyroshock tests. To achieve the highest performance, we accomplish these tasks through combining an ensemble learning model and a deep neural network (DNN). The ensemble learning model, which integrates several k-nearest neighbors with different k values, is used to identify the sensorsâ health conditions from their measurements and diagnose their fault types synchronously if damaged. The DNN, a deep autoencoder-based neural network, is designed to correct corrupted measurements through constructing the mapping between faulty signals and their corresponding reference counterparts. Experimental results show that the proposed machine learning system is capable of not only accurately identifying the health conditions and fault types of the damaged high-g accelerometers from their measurements, but also recovering the corrupted shock signals to a large extent, and, meanwhile, outputting the five self-validation parameters.

9. SII

   Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human Player

   Niu, H., Ji, Z., Arvin, F., Lennox, B., and Carrasco, J.

   In 2021 IEEE/SICE International Symposium on System Integration 2021
10. SII

    Model Checking for Decision Making System of Long Endurance Unmanned Surface Vehicle

    Niu, H., Ji, Z., Savvaris, A., Tsourdos, A., and Carrasco, J.

    In 2021 IEEE/SICE International Symposium on System Integration 2021
11. Automat Constr

    Deep learning-based prediction of piled-up status and payload distribution of bulk material

    Yao, ZongWei, Huang, Qiuping, Ji, Ze, Li, XueFei, and Bi, Qiushi

    Automation in Construction 2021

2020

1. JAMT

   A novel image feature descriptor for SLM spattering pattern classification using a consumable camera

   Ji, Ze, and Han, Quanquan

   Int J Adv Manuf Technol 2020
2. OE

   Energy efficient path planning for Unmanned Surface Vehicle in spatially-temporally variant environment

   Niu, Hanlin, Ji, Ze, Savvaris, Al, and Tsourdos, Antonios

   Ocean Engineering 2020

   Abs

   Unmanned Surface Vehicles (USVs) are increasingly used for ocean missions, which typically require long duration of operations under strict energy constraints. Consequently, there is an increased interest in energy efficient path planning for USVs. This work proposes a novel energy efficient path planning algorithm to address the challenges with the presence of spatially-temporally variant sea current and complex geographic map data, by integrating the following algorithms, namely Voronoi roadmap, Dijkstra’s searching, coastline expanding and genetic algorithm (GA). The selection, crossover and mutation operators are employed as part of the GA algorithm. The dividing, smoothing and exchanging operators are proposed to improve the quality of the path and adapt to the Voronoi-Visibility roadmap. The Global Self-Consistent Hierarchical High-Resolution Shorelines dataset and historical sea current dataset are applied to demonstrate the flexibility and practicability of the proposed algorithm. To evaluate the performance, the Voronoi-GA energy efficient algorithm and Voronoi-Visibility energy efficient path re-planning algorithm are also implemented to provide the baseline for comparison. The proposed algorithm generates the most energy efficient paths in ten USV missions, while keeping a configurable clearance from the coastlines. The practicability and scalability of this algorithm is also demonstrated by analysing the computational time in these ten missions.

3. IEEE Sensors

   On Fault Diagnosis for High-g Accelerometers via Data-Driven Models

   Wen, J., Yao, H., Ji, Z., Wu, B., and Xia, M.

   IEEE Sensors Journal 2020
4. RA-L

   Particle Swarm Optimization for Cooperative Multi-Robot Task Allocation: A Multi-Objective Approach

   Wei, C., Ji, Z., and Cai, B.

   IEEE Robotics and Automation Letters 2020
5. IEEE Sensors

   Efficient Multi-View Multi-Target Tracking Using a Distributed Camera Network

   He, L., Liu, G., Tian, G., Zhang, J., and Ji, Z.

   IEEE Sensors Journal 2020
6. IEEE Sensors

   A Deep Learning Approach to Recover High-g Shock Signals From the Faulty Accelerometer

   Wen, J., Yao, H., Wu, B., Ren, Y., and Ji, Z.

   IEEE Sensors Journal 2020
7. AIM

   Online Collision Avoidance for Human-Robot Collaborative Interaction Concerning Safety and Efficiency

   Liu, G., He, H., Tian, G., Zhang, J., and Ji, Z.

   In 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) 2020

2019

1. IEEE Robio

   Visual SLAM Based on Dynamic Object Removal

   Liu, H., Liu, G., Tian, G., Xin, S., and Ji, Z.

   In 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO) 2019
2. IEEE Robio

   Distributed Human 3D Pose Estimation and Action Recognition

   Liu, G., Liu, T., Tian, G., and Ji, Z.

   In 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO) 2019
3. IEEE Robio

   Simultaneous Monocular Visual Odometry and Depth Reconstruction with Scale Recovery

   Luo, Y., Liu, G., Liu, H., Liu, T., Tian, G., and Ji, Z.

   In 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO) 2019
4. taros

   An Auto-Correction Teleoperation Method for a Mobile Manipulator Using Gaze Tracking and Hand Motion Detection

   Chen, Junshen, Ji, Ze, Niu, Hanlin, Setchi, Rossitza, and Yang, Chenguang

   In Towards Autonomous Robotic Systems 2019

   Abs

   Situational awareness in remote environments is crucial for human operators to teleoperate mobile manipulators confidently and reliably. Visual feedback is the most common way for environment perception, providing rich information to human operators. This paper proposes an intuitive teleoperation method by combining gaze tracking and hand motion detection to teleoperate a mobile manipulator. A camera is fixed on the end-effector of the mobile robot’s arm to provide visual feedback, acting as the eye of the teleoperator. Rather than direct remote control of the robot, an adaptive auto-correction mechanism is introduced for helping human operators to achieve better hand-eye coordination of the teleoperation experiences. The mobile manipulator can adjust its behaviours, such as speed, while gaze and hand movements of the operator are in different states. The experiments carried out demonstrated the effectiveness of the proposed algorithm and the survey evaluation verified the practical application value of the system.

5. Voronoi-Visibility Roadmap-based Path Planning Algorithm for Unmanned Surface Vehicles

   Niu, Hanlin, Savvaris, Al, Tsourdos, Antonios, and Ji, Ze

   Journal of Navigation 2019
6. ICASSP

   Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration

   Yao, H., Wen, J., Ren, Y., Wu, B., and Ji, Z.

   In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2019
7. JNE

   Feature extraction of four-class motor imagery EEG signals based on functional brain network

   Ai, Qingsong, Chen, Anqi, Chen, Kun, Liu, Quan, Zhou, Tichao, Xin, Sijin, and Ji, Ze

   Journal of Neural Engineering 2019

   Abs

   Objective. A motor-imagery-based brain–computer interface (MI-BCI) provides an alternative way for people to interface with the outside world. However, the classification accuracy of MI signals remains challenging, especially with an increased number of classes and the presence of high variations with data from multiple individual people. This work investigates electroencephalogram (EEG) signal processing techniques, aiming to enhance the classification performance of multiple MI tasks in terms of tackling the challenges caused by the vast variety of subjects. Approach. This work introduces a novel method to extract discriminative features by combining the features of functional brain networks with two other feature extraction algorithms: common spatial pattern (CSP) and local characteristic-scale decomposition (LCD). After functional brain networks are established from the MI EEG signals of the subjects, the measures of degree in the binary networks are extracted as additional features and fused with features in the frequency and spatial domains extracted by the CSP and LCD algorithms. A real-time BCI robot control system is designed and implemented with the proposed method. Subjects can control the movement of the robot through four classes of MI tasks. Both the BCI competition IV dataset 2a and real-time data acquired in our designed system are used to validate the performance of the proposed method. Main results. As for the offline data experiment results, the average classification accuracy of the proposed method reaches 79.7%, outperforming the majority of popular algorithms. Experimental results with real-time data also prove the proposed method to be highly promising in its real-time performance. Significance. The experimental results show that our proposed method is robust in extracting discriminative brain activity features when performing different MI tasks, hence improving the classification accuracy in four-class MI tasks. The high classification accuracy and low computational demand show a considerable practicality for real-time rehabilitation systems.

2018

1. Autonomous 3D scene understanding and exploration in cluttered workspaces using point cloud data

   Ji, Z., Schruoffeneger, R., and Setchi, R.

   In 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC) 2018
2. taros

   Seeing the Unseen: Locating Objects from Reflections

   Wu, Jing, and Ji, Ze

   In Towards Autonomous Robotic Systems 2018

   Abs

   Inspired by the ubiquitous use of reflections in human vision system, in this paper, we present our first step exploration of using reflections to extend the FOV of cameras in computer vision applications. We make use of a stereo camera, and establish mathematical models for locating objects from their mirror reflections. We also propose a pipeline to track and locate moving objects from their reflections captured in videos. Experimental results demonstrated the efficiency and effectiveness of the proposed method, and verified the potential use of reflections in locating non-line of sight (NLOS) objects.

2017

1. Autonomous boat dynamics: How far away is simulation from the high sea?

   Huang, Y., and Ji, Z.

   In OCEANS 2017 - Aberdeen 2017