Author name cluster

Yuqing He

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16 papers

2 author rows

IROS Conference 2025 Conference Paper

Real-Time Optimization-Based Quadrotor Trajectory Generation with Kinodynamic Constraints in Unknown Environments

Pinhui Zhao
Decai Li
Minjiang Wu
Yuyang Zhou
Yuqing He

Indoor disaster relief and rescue missions require quadrotors to fully exploit their maneuverability in real-time. However, the computational complexity induced by the underactuated kinodynamics conflicts with the rapid replanning requirement. For agile trajectory planning in cluttered and unknown environments, we propose a real-time optimization-based quadrotor trajectory generation method that integrates kinodynamic constraints in both trajectory search and trajectory optimization phases to fully exploit maneuverability. To further improve efficiency, we introduce a waypoints selection strategy to reduce the computational burden of kinodynamic trajectory optimization by transforming obstacle avoidance constraints into waypoint constraints, thereby enabling safe trajectory optimization in real-time. Specifically, kinodynamic trajectories are searched under kinodynamic constraints, providing reliable initial values for subsequent numerical optimization. Nextly, a waypoints selection algorithm, based on an estimation of trajectory variation during optimization, is introduced to preserve the obstacle-avoidance properties obtained during the search phase by limiting the variation with waypoint constraints. Finally, trajectory is segmented by waypoints with fixed time intervals each segment and then optimized under kinodynamic constraints, ensuring real-time optimization at the cost of time allocation optimality. We evaluated our method through simulation and experimentally validate its performance in cluttered and unknown environments. The competence of proposed method is also validated in real-world experiments.

IROS Conference 2024 Conference Paper

Small Multi-Rotor UAV Oriented Direct Thrust Sensor Based on Lightweight Barometers

Han Jiang
Yanchun Chang
Liying Yang 0002
Yuqing He

The multirotor unmanned aerial vehicle (UAV) requires precise control over thrust output when operating in wind-disturbed environments or executing intricate flight missions. Although current commercial force sensors offer high sensitivity and accuracy, they are often heavy and costly. These characteristics restrict their applicability in weight-sensitive and cost-sensitive scenarios, such as thrust measurement in UAVs. To overcome this difficulty, we have developed an embedded barometric force sensor (BFS) that mounts between the UAV’s airframe and motor, allowing direct measurement of the force exerted by the rotor on the UAV’s rigid body. The BFS is designed using low-cost MEMS barometers as tactile force sensors, encased in polyurethane rubber. Subsequently, we established its parameter model and devised a stability improvement strategy to reduce the impact of temperature. Additionally, we designed a structure suitable for mounting the BFS on the UAV to safeguard the rubber module from damage and reconstructed the thrust model to account for the impact of weight and friction on thrust measurement. Finally, we assembled testing platforms to validate the performance of the BFS. Experimental results demonstrate the BFS’s excellent linearity, wide range, adequate bandwidth to respond to UAV thrust variations, and confirm the feasibility of mounting the BFS on the UAV for thrust measurement and force feedback control.

IROS Conference 2023 Conference Paper

Aerial Manipulator Systems Gain a New Skill: Achieve Contact-based Landing on a Mobile Platform

Xiangdong Meng
Yuqing He
Haoyang Xi
Jianda Han
Aiguo Song

This paper studies a novel application of an aerial manipulator (AM)-the contact-based landing on a mobile platform. An AM is inherently unstable, under-actuated, and usually loses some DOFs while contacting environments. Meanwhile, the AM's flight state is susceptible to uncertain movements of the mobile platform, such as acceleration, sudden stopping, and reversing. To accomplish the contact-based landing mission, a robust controller is first designed to maintain a steady contact-based flight. Then a hierarchical control framework is applied, integrating the controllers in free-flight and restricted-flight stages. An AM and a mobile platform are developed for contact-based flight experiments. The proposed scheme is reliable and has good repeatability in experiments. To the best of our knowledge, this is the first time an AM has been implemented to conduct a contact-based landing, which is also an innovative landing approach for rotorcraft UAVs.

AAAI Conference 2022 Conference Paper

Random Mapping Method for Large-Scale Terrain Modeling

Xu Liu
Decai Li
Yuqing He

The vast amount of data captured by robots in large-scale environments brings the computing and storage bottlenecks to the typical methods of modeling the spaces the robots travel in. In order to efficiently construct a compact terrain model from uncertain, incomplete point cloud data of large-scale environments, in this paper, we first propose a novel feature mapping method, named random mapping, based on the fast random construction of base functions, which can efficiently project the messy points in the low-dimensional space into the high-dimensional space where the points are approximately linearly distributed. Then, in this mapped space, we propose to learn a continuous linear regression model to represent the terrain. We show that this method can model the environments in much less computation time, memory consumption, and access time, with high accuracy. Furthermore, the models possess the generalization capabilities comparable to the performances on the training set, and its inference accuracy gradually increases as the random mapping dimension increases. To better solve the large-scale environmental modeling problem, we adopt the idea of parallel computing to train the models. This strategy greatly reduces the wall-clock time of calculation without losing much accuracy. Experiments show the effectiveness of the random mapping method and the effects of some important parameters on its performance. Moreover, we evaluate the proposed terrain modeling method based on the random mapping method and compare its performances with popular typical methods and state-of-art methods.

IROS Conference 2021 Conference Paper

An Efficient and Continuous Representation for Occupancy Mapping with Random Mapping

Xu Liu 0026
Decai Li
Yuqing He

Generating meaningful spatial models of physical environments is a crucial ability for autonomous navigation of mobile robots. This paper considers the problem of building continuous occupancy maps from sparse and noisy sensor data. To this end, we propose a new method named random mapping maps that advances the popular methods in two aspects. Firstly, it can represent environment models in a memory-saving and time-saving manner by randomly mapping a low-dimensional feature space to a high-dimensional one where a linear model is learnt. Secondly, it can rapidly obtain accurate inferences of the occupancy states of the spatial locations. This technique is based on the random mapping that projects the measurement data into a random feature space in which a discriminative model is learnt by the available data. It can asymptotically represent the complexity of the real world as the mapping dimension increases. Evaluations of the proposed method were conducted on various environments to verify its availability to environment modeling. Its performances in terms of time and memory consumptions were evaluated quantitatively. Finally, as a practical application, experiments about path planning were conducted based on the gradients of the proposed representation of environment model.

ICRA Conference 2021 Conference Paper

Multiresolution Representations for Large-Scale Terrain with Local Gaussian Process Regression

Xu Liu 0026
Decai Li
Yuqing He

To address the problem of building accurate and coherent models for large-scale terrains from incomplete and noisy sensor data, this paper proposes a novel framework that can efficiently infer terrain structures by divisionally providing the best linear unbiased estimates for the elevation values. To avoid data ambiguity caused by the uncertainty of sensor data, the proposed method introduces elevation filtering to extract the terrain surfaces, which reduces the amount of data greatly while the contained terrain information is basically unchanged. Then, for the large-scale terrains, the Gaussian mixture model is used to divide the interested regions, which remarkably improves the prediction accuracy and speed. Finally, for each subregion, a gaussian process regression model based on the static kernel is used to create a multiresolution terrain representation, which can deal with incompleteness of sensor data by considering the spatial correlations of the terrain. Evaluations of the proposed technique were conducted on diverse large-scale field terrains, including the quarry, planetary emulation terrain and highland, showing that the proposed method outperforms the state-of-art terrain modeling techniques in terms of the prediction accuracy, computation speed and memory consumption. As a practical application, the path planning problem was explored based on this terrain modeling technique to produce a better path.

IROS Conference 2021 Conference Paper

Simultaneous Prediction of Pedestrian Trajectory and Actions based on Context Information Iterative Reasoning

Bo Chen
Decai Li
Yuqing He

Pedestrian trajectories and actions prediction in complex environment is challenging due to the complexity of human behavior and a variety of internal and external stimuli. Much works has gone towards predicting trajectories and actions separately without mining the coupling relationships between them, which is an important information for our humans to reason and predict. Inspired by this, we propose an end-to-end joint context information iterative reasoning network (CIR-Net). Specifically, a novel heterogeneous spatiotemporal graph module (HST-Graph) is proposed to encode and aggregate multiple types of context information of the motion pattern and the scene. And an action-trajectory hybrid guidance module is proposed to enhance the ability of long-time prediction by utilizing the internal coupling between actions and trajectory. Moreover, an iterative reasoning structure is designed to iteratively correcting the trajectory and actions prediction error. Experimental results on the ETH&UCY and VIRAT datasets demonstrate the favorable performance of the framework.

ICRA Conference 2019 Conference Paper

A Multi-Domain Feature Learning Method for Visual Place Recognition

Peng Yin 0001
Lingyun Xu
Xueqian Li
Chen Yin
Yingli Li
Rangaprasad Arun Srivatsan
Lu Li
Jianmin Ji

Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of environmental conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the environmental factors, leading to decreased accuracy decreases when environmental conditions change significantly, such as day versus night. To this end, we propose an end-to-end conditional visual place recognition method. Specifically, we introduce the multi-domain feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a feature detaching module to separate the environmental condition-related features from those that are not. The only label required within this feature learning pipeline is the environmental condition. Evaluation of the proposed method is conducted on the multi-season NORDLAND dataset, and the multi-weather GTAV dataset. Experimental results show that our method improves the feature robustness against variant environmental conditions.

IROS Conference 2019 Conference Paper

Design and Implementation of a Contact Aerial Manipulator System for Glass-Wall Inspection Tasks

Xiangdong Meng
Yuqing He
Jianda Han

Glass curtain walls have been widely used in modern architecture. This makes it urgent to inspect and clean these glasses at regular intervals. Up to now, most of these work is performed by workers, which is expensive and inefficient. Therefore, a novel robot—the contact aerial manipulator system—is developed. The new designed system presents priorities in the aspects of high flexibility and easy operation. In this paper, the system mechanical structure is first introduced. Subsequently, the hybrid force/motion control framework is utilized to realize the precise and steady motion on the two-dimensional plane and maintain a certain sustained contact force, simultaneously. Finally, two flight experiments (including continuous square-wave trajectory tracking and aerial drawing task) are performed and the results indicate that the developed contact aerial manipulator works and presents good performance.

IROS Conference 2019 Conference Paper

Hybrid Force/Motion Control and Implementation of an Aerial Manipulator towards Sustained Contact Operations

Xiangdong Meng
Yuqing He
Jianda Han

Contact-based operation in moving process is a challenging problem for aerial manipulators. It requires the whole system to maintain steady contact with external environment, to track some predefined trajectories on surfaces, and simultaneously to present some fixed contact force. Aiming at this problem, a hybrid force/motion control framework is proposed in this paper. In this framework, contact force control and position control are performed separately in two orthogonal subspaces: constrained space and free-flight space. To control the contact force, the closed-loop unmanned aerial vehicle is first theoretically shown to behave dynamically as a spring-mass-damper system. Further, an inverse-dynamics-based controller is proposed. To control the moving along the contact surface, trajectory planning and position controller are combined to achieve the steady behavior in a free-flight subspace. In the end, an aerial manipulator system with a roller-type end-effector was designed, and practical flight experiments was performed. The results indicate that the proposed framework is effective and validity.

ICRA Conference 2019 Conference Paper

MRS-VPR: a multi-resolution sampling based global visual place recognition method

Peng Yin 0001
Rangaprasad Arun Srivatsan
Yin Chen
Xueqian Li
Hongda Zhang
Lingyun Xu
Lu Li
Zhenzhong Jia

Place recognition and loop closure detection are challenging for long-term visual navigation tasks. SeqSLAM is considered to be one of the most successful approaches to achieve long-term localization under varying environmental conditions and changing viewpoints. SeqSLAM uses a brute-force sequential matching method, which is computationally intensive. In this work, we introduce a multi-resolution sampling-based global visual place recognition method (MRS-VPR), which can significantly improve the matching efficiency and accuracy in sequential matching. The novelty of this method lies in the coarse-to-fine searching pipeline and a particle filter-based global sampling scheme, that can balance the matching efficiency and accuracy in the long-term navigation task. Moreover, our model works much better than SeqSLAM when the testing sequence is over a much smaller time scale than the reference sequence. Our experiments demonstrate that MRSVPR is efficient in locating short temporary trajectories within long-term reference ones without compromising on the accuracy compared to SeqSLAM.

IROS Conference 2018 Conference Paper

Contact Force Control of an Aerial Manipulator in Pressing an Emergency Switch Process

Xiangdong Meng
Yuqing He
Qi Li
Feng Gu 0004
Liying Yang 0002
Tengfei Yan
Jianda Han

The dangerous work situation in industrial leakage accidents urgently needs a flexible and small robot to help workers perform operations and to protect them from being injured. An aerial manipulator system consisting of a hexa-rotor UAV and a one-DOF manipulator is developed, and is used to press an emergency switch to shut off machinery in an emergency. In practical application, an aerial manipulator usually performs contact operations as the UAV platform is in hover flight. The hovering UAV acting as a spring-mass-damper system is firstly proved. Then, based on the derived spring-mass-damper system model and the impedance control algorithm, the force-sensorless contact force control method is presented. That is, the force is indirectly controlled through controlling the UAV's position error and pitch angle simultaneously. The practical operation experiment of pressing an emergency button shows that the proposed method is able to control the contact force as the aerial manipulator interacts with the external environment.

ICRA Conference 2018 Conference Paper

Grasp a Moving Target from the Air: System & Control of an Aerial Manipulator

Guangyu Zhang 0003
Yuqing He
Bo Dai 0004
Feng Gu 0004
Liying Yang 0002
Jianda Han
Guangjun Liu
Juntong Qi

Grasping a moving target has been investigated extensively for fixed-base manipulator. However, such a task becomes much more challenging when the manipulator is free flying in the air with an UAV. Towards moving target grasping, this paper presents an aerial manipulator system composed of a hex-rotor and a 7-DoF (Degree of Freedom) manipulator. An independent control structure is used in the aerial manipulator control system, i. e. , the hex-rotor and the manipulator are controlled separately. In the hex-rotor's controller, the system CoM (Center of Mass) offset motion is used to compensate disturbance of the robotic arm. In the manipulator's controller, the relative kinematics between the target and the aerial vehicle is taken into consideration to grasp the target. At last aerial grasping experiments are conducted to validate the feasibility of the proposed control scheme and the reliability of our aerial manipulator system.

IROS Conference 2018 Conference Paper

Stabilize an Unsupervised Feature Learning for LiDAR-based Place Recognition

Peng Yin 0001
Lingyun Xu
Zhe Liu 0022
Lu Li
Hadi Salman
Yuqing He
Weiliang Xu 0001
Hesheng Wang 0001

Place recognition is one of the major challenges for the LiDAR-based effective localization and mapping task. Traditional methods are usually relying on geometry matching to achieve place recognition, where a global geometry map need to be restored. In this paper, we accomplish the place recognition task based on an end-to-end feature learning framework with the LiDAR inputs. This method consists of two core modules, a dynamic octree mapping module that generates local 2D maps with the consideration of the robot's motion; and an unsupervised place feature learning module which is an improved adversarial feature learning network with additional assistance for the long-term place recognition requirement. More specially, in place feature learning, we present an additional Generative Adversarial Network with a designed Conditional Entropy Reduction module to stabilize the feature learning process in an unsupervised manner. We evaluate the proposed method on the Kitti dataset and North Campus Long-Term LiDAR dataset. Experimental results show that the proposed method outperforms state-of-the-art in place recognition tasks under long-term applications. What's more, the feature size and inference efficiency in the proposed method are applicable in real-time performance on practical robotic platforms.

IROS Conference 2015 Conference Paper

A Real-time relative probabilistic mapping algorithm for high-speed off-road autonomous driving

Cheng Chen
Yuqing He
Feng Gu 0004
Chunguang Bu
Jianda Han

Reliable mapping and hazard detection are prerequisites for autonomous navigation for unmanned ground vehicles. Because of the uncertainty and vibration induced by high-speed navigation and rugged terrain, the problem of mapping for high-speed off-road autonomous navigation has not been completely solved yet. A relative probabilistic mapping (RPM) algorithm is introduced to address the problem. Firstly, the relative probabilistic map is updated by Kalman filter and Gaussian Mixture algorithm based on the probabilistic exteroceptive measurements model. Then, terrain traversability is evaluated to identify obstacles in the map. Experiments on off-road high-speed autonomous vehicle, which suffers from severe vibration, with different sensor configurations are carried out to demonstrate the capability of the RPM algorithm.

ICRA Conference 2014 Conference Paper

Quartic Bézier curve based trajectory generation for autonomous vehicles with curvature and velocity constraints

Cheng Chen
Yuqing He
Chunguang Bu
Jianda Han
Xuebo Zhang

To generate local trajectory between initial states and target states for autonomous vehicles, a feasible trajectory generation algorithm based on quartic Bézier curve is proposed. The problem of trajectory generation is firstly separated into generating continuous and bounded curvature profile to shape the trajectory and generating linear velocity profile to execute the trajectory. The curvature profile generation is further converted to an optimization problem with only 3 parameters owing to the specific properties of quartic Bézier curve. Sequential quadratic programming is employed to find optimal solution with respect to specific objective function. To avoid sideslip and ensure velocity-continuity and acceleration limits, the framework of linear velocity profile generation is also proposed. A simple profile with constant acceleration is also provided as an example. Simulation results on lane keeping and changing and path following demonstrate the capability and the real-time performance of the proposed algorithm.