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International Journal of Advanced Robotic Systems
Journal Prestige (SJR): 0.327

Citation Impact (citeScore): 1
Number of Followers: 1

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ISSN (Print) 1729-8814 - ISSN (Online) 1729-8814
Published by Sage Publications

[1176 journals]

Fast loop closure detection using probabilistic integration of pose and
appearance similarity
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  Authors: Qinghua Yu, Kaihong Huang, Huimin Lu, Junhao Xiao, Zhiwen Zeng
  Abstract: International Journal of Advanced Robotic Systems, Volume 21, Issue 3, May-June 2024.
  Loop closure detection is a key technique for robots to minimize the accumulated localization and mapping errors after long-time explorations of simultaneous localization and mapping. However, the requirement for efficiency and accuracy performance for mobile robot applications is not well satisfied. In this article, we propose a fast and accurate loop closure detection method by exploiting both pose-based and appearance-based information in a probabilistic manner, inspired by the complementarity between the pose-based and the appearance-based information. Our approach formulates a probability framework combing the pose-based loop closure detection probability and the appearance-based loop closure detection probability. In the proposed framework, the pose-based loop closure detection model is firstly derived from the nonlinear optimization model of odometry. Then the appearance-based loop similarity and the pose-based loop similarity are combined into a joint framework to improve the loop closure detection performance. We implemented our approach using C++ and ROS and thoroughly tested it on the publicly available datasets. The experiments presented in this article suggest that the proposed method can achieve high efficiency and accuracy performance on loop closure detection.
  Citation: International Journal of Advanced Robotic Systems
  PubDate: 2024-06-11T09:05:18Z
  DOI: 10.1177/17298806241255461
  Issue No: Vol. 21, No. 3 (2024)
Gas source localization using Dueling Deep Q-Network with an olfactory
quadruped robot
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  Authors: Yu He, Lei Cheng, YaDuo Pan, DuanChu Wang, YuAo Li, Han Zheng
  Abstract: International Journal of Advanced Robotic Systems, Volume 21, Issue 3, May-June 2024.
  Gas source localization is one of the most common applications of a gas-sensitive mobile robot. However, most of the existing work focuses on rule-based algorithms for wheeled robots, which are difficult to apply in complex terrain with obstacles. In this article, we propose an olfactory quadruped robot to perform the gas source localization task using the Dueling Deep Q-Network (Dueling DQN) algorithm. For training, we designed a set of environments and imported gas dispersion data from computational fluid dynamics (CFD) software to construct a simulator. The olfactory quadruped robot was trained in this simulator using the Dueling DQN algorithm to learn how to find the gas source. The trained neural network was then deployed on the olfactory quadruped robot. Our method has been tested in both simulation and real environments. The olfactory quadruped robot can traverse rugged terrain in real experiments and efficiently find gas sources, demonstrating that our method is highly robust and has important practical value.
  Citation: International Journal of Advanced Robotic Systems
  PubDate: 2024-06-03T07:53:56Z
  DOI: 10.1177/17298806241255797
  Issue No: Vol. 21, No. 3 (2024)
Human–robot mechanics model using hip torque identification with a
dual-arm nursing-care transfer robot
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  Authors: Zhiqiang Yang, Hao Lu, Mengqian Chen, Qifei Guan, Qiming Liu, Shijie Guo
  Abstract: International Journal of Advanced Robotic Systems, Volume 21, Issue 3, May-June 2024.
  In response to the growing demand for robotic interventions to mitigate the profound physical strain experienced by caregivers during transfers, dual-arm robotic systems have emerged as a focal point of interest in the caregiving community due to their adaptability and versatility. However, the accuracy of existing human–robot mechanics model is insufficient, impacting the execution of transfer tasks. To enhance the model’s precision, an improved model is proposed, integrating hip torque identification. This proposed methodology involves the simplification of the human structure based on the kinematic attributes associated with the embrace of individuals by robotic arms and the subsequent computation of its inertial parameters. Accordingly, the application of D’Alembert’s principle is employed to analyze the impact of embracing motion, human posture, and the position of human–robot contact on the forces exerted on the human. This culminates in the establishment of a model. Considering the static indeterminacy predicament inherent in the model, a biomechanical data set is curated for the dual-arm transfer scenario. Leveraging this data set, a deep neural network utilizing multilayer perceptron is trained to accurately identify hip torque, thereby improving the model. Accordingly, a robotic transfer platform featuring dual arms is developed and trailed on six subjects with varying anatomical profiles. The results show that the constructed model has high accuracy. This study provides critical mechanics insights for dual-arm transfer tasks, offering potential application value in the field of nursing and rehabilitation.
  Citation: International Journal of Advanced Robotic Systems
  PubDate: 2024-05-29T05:58:28Z
  DOI: 10.1177/17298806241249848
  Issue No: Vol. 21, No. 3 (2024)
Hierarchical gain scheduling based tilt angle guided robust control during
mode transition for tilt-rotor unmanned aircraft vehicle
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  Authors: Hongyu Nie, Feng Gu, Yuqing He
  Abstract: International Journal of Advanced Robotic Systems, Volume 21, Issue 3, May-June 2024.
  Tilt-rotor unmanned aircraft vehicle has the potential to combine vertical take-off and landing capability with efficient, high-speed cruise flight. However, the mode transition process is risky owing to the internal continuous time change of aerodynamic and external uncertain wind disturbances. Gain scheduling between two modes is commonly used to achieve mode transition control. However, the optimal scheduling parameters to allocate the manipulated variables for the stable transition have not been determined to data. Focusing on this problem, a gain scheduling-based tilt angle guided robust control method for mode transition is proposed. The dynamic model is first built and analyzed based on a newly developed 360-kg tilt-rotor unmanned aircraft vehicle. Based on this model, the transition guided curve is mapped with respect to velocity and tilt angle, and it is introduced to the gain scheduling method to optimally achieve the allocation of the manipulated variables. Finally, the feasibility and validity are verified in a simulation experiment. Furthermore, the robustness is verified in simulated wind disturbance.
  Citation: International Journal of Advanced Robotic Systems
  PubDate: 2024-05-23T09:02:49Z
  DOI: 10.1177/17298806241246334
  Issue No: Vol. 21, No. 3 (2024)
Deep reinforcement learning and decoupling
proportional-integral-derivative control of a humanoid cable-driven hybrid
robot
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  Authors: Yong Liu, Zhisheng Luo, Sen Qian, Shuaikang Wang, Zhe Wu
  Abstract: International Journal of Advanced Robotic Systems, Volume 21, Issue 3, May-June 2024.
  Aiming at the application of robots in service, medical treatment, rehabilitation, and other fields, a humanoid cable-driven hybrid robot by imitating the structure of human arm is designed in this article. The robot is composed of a cable–spool–pulley system, series mechanism, and coaxial spherical parallel mechanism, which can achieve six degrees of freedom movement in space. A challenge with cable driving is that the movement of the rear end joints (such as pitch and roll) can alter the length or tension of the cable driven by the frontend joints, resulting in joint coupling. This interference can lead to a decrease in the motion accuracy of the robotic arm. In addition, it also affects the durability of cables or mechanical components such as bearings and pulleys. Considering the joint motion coupling phenomenon caused by the cable-driven, the decoupling method is proposed, and the kinematic model of the robot is established. To solve the nonlinear coupling characteristics and the uncertainty of the dynamics parameters, a controller is proposed for the humanoid cable-driven hybrid robot, combining proportional-integral-derivative (PID) control based on decoupling method (DC-PID) and the double delay deep deterministic policy gradient (TD3) deep reinforcement learning algorithm. The trajectory tracking of the end-effector position and orientation are controlled by using DC-PID and TD3. And the simulation results show that the proposed control method has good trajectory tracking and convergence performance according to trajectory tracking error and training reward. Finally, the humanoid cable-driven hybrid robot prototype is developed. The experimental results of coupling compensation show that the average maximum error of the joint is reduced by 77.58% after considering the coupling compensation of the joint. The results of the controller validation experiments show that the DC-PID controller reduces the maximum error in each axis by 11.54%, 35.29%, and 40.16%, respectively, compared to the open-loop experiments.
  Citation: International Journal of Advanced Robotic Systems
  PubDate: 2024-05-23T09:02:48Z
  DOI: 10.1177/17298806241254336
  Issue No: Vol. 21, No. 3 (2024)
A study on the dynamics of a novel seven degrees of freedom spray-painting
robot with a telescopic forearm
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  Authors: Yutian Wang, Jiahao Qiu, Jun Wu, Jinsong Wang
  Abstract: International Journal of Advanced Robotic Systems, Volume 21, Issue 3, May-June 2024.
  In this article, the dynamics of a seven degrees of freedom robot with a telescopic forearm for the spray-painting of large workpiece is investigated. A novel two-step inverse kinematic solving method that combines numerical and analytical approaches is proposed to solve the inverse kinematics of the seven degrees of freedom robot with two redundant degrees of freedom for spray-painting applications. Based on the kinematic model, the dynamic model is derived using the Newton–Euler method. For the dynamic parameter identification, the dynamic model is written in a linear form with respect to dynamic parameters. Considering the effect of the telescopic forearm on the dynamics of the robot, two novel performance indices concerning the inertial load and gravitational load in the joint space are proposed to evaluate the effect of the telescopic forearm on the dynamic load of the robot. Monte Carlo method is used to solve these two indices. This work is essential for the motion planning and control of the seven degrees of freedom spray-painting robot with a telescopic forearm.
  Citation: International Journal of Advanced Robotic Systems
  PubDate: 2024-05-21T11:46:52Z
  DOI: 10.1177/17298806241243162
  Issue No: Vol. 21, No. 3 (2024)
Planar curved path following controller for a small fixed-wing unmanned
aerial vehicle with constrained parameters optimized by nonlinear model
predictive control
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  Authors: Yang Chen, Wei Zeng, Chaolei Wang, Yongliang Wu
  Abstract: International Journal of Advanced Robotic Systems, Volume 21, Issue 3, May-June 2024.
  Path following presents a pivotal challenge within the realm of small fixed-wing unmanned aerial vehicles. Firstly, a Lyapunov-stable path guidance law was formulated to follow specific planar curved paths. To ensure differentiability of the guidance law, a modified, smooth saturation function was derived. Secondly, an analysis was conducted to ascertain the interrelationship between control parameters and input constraints, thereby identifying the relevant parameter domains. Thirdly, the nonlinear model predictive control technique was harnessed to optimize both guidance law parameters, enhancing the unmanned aerial vehicle’s capacity to achieve optimal performance in both straight-line and circular path following, hereafter referred to as PFC_NMPC. By leveraging Lyapunov stability arguments for switched systems, the stability of the corresponding nonlinear switched system was guaranteed. In this study, square and circular paths were generated to assess the path-following control of a simulated fixed-wing unmanned aerial vehicle. The performance of various guidance laws, including those with fixed parameters (PFC), those with parameters tuned using fuzzy logic (PFC_FL), PFC_NMPC, vector field, and pure pursuit with line-of-sight, was compared. Notably, the proposed PFC_NMPC method exhibited the ability to expedite the unmanned aerial vehicle’s convergence to the desired path while maximizing the effective flight path length.
  Citation: International Journal of Advanced Robotic Systems
  PubDate: 2024-05-06T08:55:49Z
  DOI: 10.1177/17298806241249005
  Issue No: Vol. 21, No. 3 (2024)
Environmental-structure-perception-based adaptive pose fusion method for
LiDAR-visual-inertial odometry
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  Authors: Zixu Zhao, Chang Liu, Wenyao Yu, Jinglin Shi, Dalin Zhang
  Abstract: International Journal of Advanced Robotic Systems, Volume 21, Issue 3, May-June 2024.
  Light Detection and Ranging (LiDAR)-visual-inertial odometry can provide accurate poses for the localization of unmanned vehicles working in unknown environments in the absence of Global Positioning System (GPS). Since the quality of poses estimated by different sensors in environments with different structures fluctuates greatly, existing pose fusion models cannot guarantee stable performance of pose estimations in these environments, which brings great challenges to the pose fusion of LiDAR-visual-inertial odometry. This article proposes a novel environmental structure perception-based adaptive pose fusion method, which achieves the online optimization of the parameters in the pose fusion model of LiDAR-visual-inertial odometry by analyzing the complexity of environmental structure. Firstly, a novel quantitative perception method of environmental structure is proposed, and the visual bag-of-words vector and point cloud feature histogram are constructed to calculate the quantitative indicators describing the structural complexity of visual image and LiDAR point cloud of the surroundings, which can be used to predict and evaluate the pose quality from LiDAR/visual measurement models of poses. Then, based on the complexity of the environmental structure, two pose fusion strategies for two mainstream pose fusion models (Kalman filter and factor graph optimization) are proposed, which can adaptively fuse the poses estimated by LiDAR and vision online. Two state-of-the-art LiDAR-visual-inertial odometry systems are selected to deploy the proposed environmental structure perception-based adaptive pose fusion method, and extensive experiments are carried out on both open-source data sets and self-gathered data sets. The experimental results show that environmental structure perception-based adaptive pose fusion method can effectively perceive the changes in environmental structure and execute adaptive pose fusion, improving the accuracy of pose estimation of LiDAR-visual-inertial odometry in environments with changing structures.
  Citation: International Journal of Advanced Robotic Systems
  PubDate: 2024-05-06T08:55:48Z
  DOI: 10.1177/17298806241248955
  Issue No: Vol. 21, No. 3 (2024)

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