Ze Ji

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Verified

Ze verified their affiliation via an institutional email.

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• PhD
• Reader at Cardiff University

The piled-up status of bulk material in a haul truck body determines the load balance, hence affects the mining operations’ efficiency. Prediction of Piled-up Status and Payload Distribution (PSPD) of bulk material contributes to providing optimal dumping positions to improve the vehicle’s stress state and service life. This work introduces a novel...

Multistep tasks, such as block stacking or parts (dis)assembly, are complex for autonomous robotic manipulation. A robotic system for such tasks would need to hierarchically combine motion control at a lower level and symbolic planning at a higher level. Recently, reinforcement learning (RL)-based methods have been shown to handle robotic motion co...

In the past decade, there is an increasing interest in the deployment of unmanned surface vehicles (USVs) for undertaking ocean missions in dynamic, complex maritime environments. The success of these missions largely relies on motion planning algorithms that can generate optimal navigational trajectories to guide a USV. Apart from minimising the d...

In cutter suction dredger (CSD) operations, direct visual assessment of underwater soil composition and terrain is unavailable to operators. This limitation necessitates reliance on indirect indicators, such as concentration meter readings, interpreted through the operator’s accumulated experience. However, concentration meters are typically instal...

Robotic manipulation of volumetric elastoplastic deformable materials, from foods such as dough to construction materials like clay, is in its infancy, largely due to the difficulty of modelling and perception in a high-dimensional space. Simulating the dynamics of such materials is computationally expensive. It tends to suffer from inaccurately es...

Current models for detecting defects on steel surfaces struggle to fully utilize potential positional and semantic information. Usually, these models merely combine high-level and low-level features in a straightforward manner, leading to an increase in redundant information. To address this challenge, this study presents an aggregated multi-level...

Skeletonization is a powerful tool for shape analysis, rooted in the inherent instinct to understand an object's morphology. It has found applications across various domains, including robotics. Although skeletonization algorithms have been studied in recent years, their performance is rarely quantified with detailed numerical evaluations. This wor...

There has been a growth of collaborative robots in Industry 5.0 due to the research in automation involving human-centric workplace design. It has had a substantial impact on industrial processes; however, physical exertion in human workers is still an issue, requiring solutions that combine technological innovation with human-centric development....

Tracking a moving unmanned ground vehicle (UGV) with an autonomous Unmanned Aerial Vehicle (UAV) is challenging, particularly in GNSS-denied indoor environments where reacquiring the UGV after losing track poses a significant obstacle. This paper presents a novel learning framework designed to address these challenges, enabling a quadrotor UAV to e...

Defect detection plays a crucial role in industrial production, and the implementation of this technology has significant implications for improving both product quality and processing efficiency. However, the limited availability of defect samples for training deep-learning-based object detection models within industrial processes poses challenges...

Featured Application This work is motivated by practical applications, such as smart factories and warehouses, where unmanned ground vehicles (UGVs) are required to efficiently navigate to desired destinations without accurate environmental maps, and they may encounter unfamiliar obstacles with various sizes and shapes. Reinforcement learning (RL)-...

Autonomous unmanned aerial vehicle (UAV) landing on a moving unmanned ground vehicle (UGV) remains a challenge as it is difficult for the UAV to track the real-time state of the UGV and adjust its landing policy accordingly. This paper proposes a learning framework for a quadrotor UAV to land on a moving UGV without knowing its motion dynamics. Spe...

Robotic manipulation of volumetric elastoplastic deformable materials, from foods such as dough to construction materials like clay, is in its infancy, largely due to the difficulty of modelling and perception in a high-dimensional space. Simulating the dynamics of such materials is computationally expensive. It tends to suffer from inaccurately es...

Bottle packaging is extensively used in manufacturing, and inspecting aluminum foil sealing during filling is crucial for ensuring product quality. Traditional Machine vision methods based on supervised learning require extensive annotated data, but the scarcity of defective samples hampers the effectiveness of these methods. To address this challe...

Techniques for detecting mirrors from static images have witnessed rapid growth in recent years. However, these methods detect mirrors from single input images. Detecting mirrors from video requires further consideration of temporal consistency between frames. We observe that humans can recognize mirror candidates, from just one or two frames, base...

Due to the complex physical properties of granular materials, research on robot learning for manipulating such materials predominantly either disregards the consideration of their physical characteristics or uses surrogate models to approximate their physical properties. Learning to manipulate granular materials based on physical information obtain...

The curve skeleton is known to geometric modelling and computer graphics communities as one of the shape descriptors which intuitively indicates the topological properties of the objects. In recent years, studies have also suggested the potential of applying curve skeletons to assist robotic reasoning and planning. However, the raw scanned point cl...

Bagging is an essential skill that humans perform in their daily activities. However, deformable objects, such as bags, are complex for robots to manipulate. A learning‐based framework that enables robots to learn bagging is presented. The novelty of this framework is its ability to learn and perform bagging without relying on simulations. The lear...

Human-robot collaboration is a vital approach in manufacturing, integrating the capabilities of both humans and robots effectively. In recent years, the well-being of manufacturing workers has received increasing attention with the development of manufacturing systems. However, the perception of human characteristics, such as physical fatigue, and...

Exploring large-scale environments autonomously poses a significant challenge. As the size of environments increases, the computational cost becomes a hindrance to real-time operation. Additionally, while frontier-based exploration planning provides convenient access to environment frontiers, it suffers from slow global exploration speed. On the ot...

Visual simultaneous localisation and mapping (vSLAM) finds applications for indoor and outdoor navigation that routinely subjects it to visual complexities, particularly mirror reflections. The effect of mirror presence (time visible and its average size in the frame) was hypothesised to impact localisation and mapping performance, with systems usi...

Object Goal Navigation (ObjectNav) refers to an agent navigating to an object in an unseen environment, which is an ability often required in the accomplishment of complex tasks. Though it has drawn increasing attention from researchers in the Embodied AI community, there has not been a contemporary and comprehensive survey of ObjectNav. In this su...

Collaborative robots, or cobots, are one of the Industry 4.0 technologies that have and continue to change many industrial procedures. However, amid this technological advancement, the persisting physical strain on human workers remains a significant concern. Even with the advent of cobots aimed at alleviating burdensome tasks, certain physical job...

Mapless navigation for Automated Guided Vehicles (AGV) via Deep Reinforcement Learning (DRL) algorithms has attracted significantly rising attention in recent years. Collision avoidance from dynamic obstacles in unstructured environments, such as pedestrians and other vehicles, is one of the key challenges for mapless navigation. Autonomous navigat...

Though reinforcement learning (RL) has shown an outstanding capability for solving complex computational problems, most RL algorithms lack an explicit method that would allow learning from contextual information. On the other hand, humans often use context to identify patterns and relations among elements in the environment, along with how to avoid...

Cooperative human-robot interaction often requires successful handovers of objects between the two entities. However, the assumption that a human can reliably grasp an object from a robot is not always valid. To address this issue, we propose a vision-based tactile sensor for object handover framework that utilises a low-cost sensor with variable s...

Mapless navigation for mobile Unmanned Ground Vehicles (UGVs) using Deep Reinforcement Learning (DRL) has attracted significantly rising attention in robotic and related research communities. Collision avoidance from dynamic obstacles in unstructured environments, such as pedestrians and other vehicles, is one of the key challenges for mapless navi...

As a popular concept proposed in the field of psychology, affordance has been regarded as one of the important abilities that enable humans to understand and interact with the environment. Briefly, it captures the possibilities and effects of the actions of an agent applied to a specific object or, more generally, a part of the environment. This pa...

Recent interest in additive manufacturing (AM) technologies (also known as 3D printing) has led to embedding multi-material and electronic components into 3D-printed structures. However, current 3D printing technologies fail to provide all the required materials to fabricate complex devices. Besides, the process of inserting individual building blo...

To better address the difficulties in designing green fruit recognition techniques in machine vision systems, a new fruit detection model is proposed. This model is an optimization of the FCOS (full convolution one-stage object detection) algorithm, incorporating LSC (level scales, spaces, channels) attention blocks in the network structure, and na...

In order to enable intelligent orchard management and the application of harvesting robots, it is necessary to improve the accuracy of computer vision technology for green fruit segmentation in complex orchard environments. However, existing segmentation algorithms are unable to generate precise fruit masks in such environments. This paper proposes...

As a popular concept proposed in the field of psychology, affordance has been regarded as one of the important abilities that enable humans to understand and interact with the environment. Briefly, it captures the possibilities and effects of the actions of an agent applied to a specific object or, more generally, a part of the environment. This pa...

Planning precise manipulation in robotics to perform grasp and release-related operations, while interacting with humans is a challenging problem. Reinforcement learning (RL) has the potential to make robots attain this capability. In this paper, we propose an affordance-based human-robot interaction (HRI) framework, aiming to reduce the action spa...

Solving reinforcement learning (RL)-based mapless navigation tasks is challenging due to their sparse reward and long decision horizon nature. Hierarchical reinforcement learning (HRL) has the ability to leverage knowledge at different abstract levels and is thus preferred in complex mapless navigation tasks. However, it is computationally expensiv...

Despite of significant achievements made in the detection of target fruits, small fruit detection remains a great challenge, especially for immature small green fruits with a few pixels. The closeness of color between the fruit skin and the background greatly increases the difficulty of locating small target fruits in the natural orchard environmen...

Industry application of additive manufacturing demands strict in-process quality control procedures and high product quality. Feedback loop control is a reasonable solution and a necessary tool. This paper demonstrated our preliminary work on the laser powder-bed fusion feedback loop: predict local porosity through in-process monitoring images and...

Reinforcement learning (RL) has become an interesting topic in robotics applications as it can solve complex problems in specific scenarios. The small amount of RL-tools focused on robotics, plus the lack of features such as easy transfer of simulated environments to real hardware, are obstacles to the widespread use of RL in robotic applications....

Although Deep Reinforcement Learning (DRL) has been popular in many disciplines including robotics, state-of-the-art DRL algorithms still struggle to learn long-horizon, multi-step and sparse reward tasks, such as stacking several blocks given only a task-completion reward signal. To improve learning efficiency for such tasks, this paper proposes a...

It is a complex problem to adjust the motion mechanism of the coordinator. Manual operation will bring the problems of quality consistency and reliability. In this paper, automatic method is adopted to solve the principle problem of force adjustment in the assembly process of motion mechanism with the cooperation of two KUKA lbr-iwa robots. Firstly...

Fruit detection and segmentation will be essential for future agronomic management, with applications in yield estimation, growth monitoring, intelligent picking, disease detection and etc. In order to more accurately and efficiently realize the recognition and segmentation of apples in natural orchards, a robust segmentation net framework speciall...

The Cutter Suction Dredger (CSD) is one of the key equipment dedicated to the construction and maintenance projects of harbours, ports and navigational channels. Among the dredging manipulations, the swing process is the most tedious and recurring work for human operators, which often leads to accidents because of carelessness or fatigue of the ope...

Digital twins (DT), aiming to improve the performance of physical entities by leveraging the virtual replica, have gained significant growth in recent years. Meanwhile, DT technology has been explored in different industrial sectors and on a variety of topics, e.g., predictive maintenance (PdM). In order to understand the state-of-the-art of DT in...

Over the years, advancement in automation technology is allowed the increased integration of humans and machines in a manufacturing environment, these days fewer humans. The use of Knowledge-based Systems in improving and converting human overall performance has been restrained in truth because of a lack of expertise of the way an individual’s over...

Digital twins (DT), aiming to improve the performance of physical entities by leveraging the virtual replica, have gained significant growth in recent years. Meanwhile, DT technology has been explored in different industrial sectors and on a variety of topics, e.g., predictive maintenance (PdM). In order to understand the state-of-the-art of DT in...

In the process of agricultural automation production, efficient and accurate segmentation of target fruit is the basis and guarantee for numerous applications including crop growth monitoring, yield prognosis, and machine picking. Green fruit is apt to affect by complicated scenes such as occlusions and overlaps, as well as the homo-chromatic backg...

Cranes are widely deployed for lifting and moving heavy objects in dynamic environments with human coexistence. Suddenly appeared workers, vehicles, and robots can affect the safety of the cranes. To avoid possible collisions, the cranes must have prediction ability to know how dangerous the situation is. In this paper, we address the safety issues...

Mapless navigation is the capability of a robot to navigate without knowing the map. Previous works assume the availability of accurate self-localisation, which is, however, usually unrealistic. In our work, we deploy simultaneous localisation and mapping (SLAM)-based self-localisation for mapless navigation. SLAM performance is prone to the qualit...

This work re-implements the OpenAI Gym multi-goal robotic manipulation environment, originally based on the commercial Mujoco engine, onto the open-source Pybullet engine. By comparing the performances of the Hindsight Experience Replay-aided Deep Deterministic Policy Gradient agent on both environments, we demonstrate our successful re-implementat...

Snakes possess multi-locomotion abilities to best suit different environments. This work explores the design of a robot to replicate three types of snake motions: rectilinear, serpentine and sidewinding. The design featured identical modular housing units containing all the components for movement, a biomimetic skin to replicate the anisotropic fri...

To achieve more accurate recognition and segmentation of obscured fruit in natural orchard environments, DLNet model is proposed. The model is improved for the more challenging problem of segmenting overlapping fruit from homochromatic backgrounds without considering various damages. This approach is tantamount to construct the detection network RS...

Efficient and accurate recognition of apples in the visible spectrum is the key part of realizing orchard production prediction and automatic harvesting. The color of the green target is similar to the background of the branches and leaves.Thus the recognition of green apples becomes a new challenge. In the natural orchard, many complicated environ...

This paper introduces a novel deep learning approach to semantic segmentation of the shoreline environments with a high frames-per-second (fps) performance, making the approach readily applicable to autonomous navigation for Unmanned Surface Vehicles (USV). The proposed ShorelineNet is an efficient deep neural network of high performance relying on...

Online action recognition is an important task for human-centered intelligent services. However, it remains a highly challenging problem due to the high varieties and uncertainties of spatial and temporal scales of human actions. In this paper, the following core ideas are proposed to deal with the online action recognition problem. First, we combi...

The posture of target fruit is ever-changing in the complex orchard environment. Some target fruits are homochromatic with background, and the limited number of samples have brought great challenges to accurately detect the target, due mainly to the difficulty of collecting some environmental data. Therefore, the detection needs to meet the high re...

In this work, a new separate-style trapezoidal wave generator (TWG) is designed to alleviate the over deviation of residual waves in trapezoidal shock waveform generation processes. Firstly, a hypothesis is proposed that this over deviation is caused by the impact between the piston and the end cover of the TWG by analyzing the dynamic behavior of...

This work re-implements the OpenAI Gym multi-goal robotic manipulation environment, originally based on the commercial Mujoco engine, onto the open-source Pybullet engine. By comparing the performances of the Hindsight Experience Replay-aided Deep Deterministic Policy Gradient agent on both environments, we demonstrate our successful re-implementat...

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 dis...