Time Optimal Trajectory Planing Based on Improved Sparrow Search Algorithm

Zhang, Xiaofeng; Xiao, Fan; Tong, Xiliang; Yun, Juntong; Liu, Ying; Sun, Ying; Tao, Bo; Kong, Jianyi; Xu, Manman; Chen, Baojia

doi:10.3389/fbioe.2022.852408

Cited by 49 publications

(32 citation statements)

References 96 publications

(94 reference statements)

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“…In addition, the traditional target detection algorithm generates multiple candidate regions through traversal, which costs a lot of time. In addition, the traditional target detection algorithm classification training detector may produce the problem of feature vector “dimension disaster” (Zhao et al, 2019 ; Liao et al, 2020 ; Hao et al, 2021 ; Tao et al, 2022a ; Zhang et al, 2022 ).…”

Section: Related Workmentioning

confidence: 99%

Multi-Scale Feature Fusion Convolutional Neural Network for Indoor Small Target Detection

et al. 2022

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The development of object detection technology makes it possible for robots to interact with people and the environment, but the changeable application scenarios make the detection accuracy of small and medium objects in the practical application of object detection technology low. In this paper, based on multi-scale feature fusion of indoor small target detection method, using the device to collect different indoor images with angle, light, and shade conditions, and use the image enhancement technology to set up and amplify a date set, with indoor scenarios and the SSD algorithm in target detection layer and its adjacent features fusion. The Faster R-CNN, YOLOv5, SSD, and SSD target detection models based on multi-scale feature fusion were trained on an indoor scene data set based on transfer learning. The experimental results show that multi-scale feature fusion can improve the detection accuracy of all kinds of objects, especially for objects with a relatively small scale. In addition, although the detection speed of the improved SSD algorithm decreases, it is faster than the Faster R-CNN, which better achieves the balance between target detection accuracy and speed.

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Section: Related Workmentioning

confidence: 99%

Multi-Scale Feature Fusion Convolutional Neural Network for Indoor Small Target Detection

et al. 2022

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“…While based on the spinor theory only needs to establish a world coordinate system, which not only optimizes the modeling process, but also has good geometric meaning. Therefore, in this paper, we choose to establish the joint coordinate system based on the Lie group and spinor theory ( Wang et al, 2018 ; Liao et al, 2020 ; Sun and Zhao, 2022 ; Zhang et al, 2022 ).…”

Section: Model and Kinematic Analysismentioning

confidence: 99%

A Tandem Robotic Arm Inverse Kinematic Solution Based on an Improved Particle Swarm Algorithm

Zhao

Liu

et al. 2022

Front. Bioeng. Biotechnol.

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The analysis of robot inverse kinematic solutions is the basis of robot control and path planning, and is of great importance for research. Due to the limitations of the analytical and geometric methods, intelligent algorithms are more advantageous because they can obtain approximate solutions directly from the robot’s positive kinematic equations, saving a large number of computational steps. Particle Swarm Algorithm (PSO), as one of the intelligent algorithms, is widely used due to its simple principle and excellent performance. In this paper, we propose an improved particle swarm algorithm for robot inverse kinematics solving. Since the setting of weights affects the global and local search ability of the algorithm, this paper proposes an adaptive weight adjustment strategy for improving the search ability. Considering the running time of the algorithm, this paper proposes a condition setting based on the limit joints, and introduces the position coefficient k in the velocity factor. Meanwhile, an exponential product form modeling method (POE) based on spinor theory is chosen. Compared with the traditional DH modeling method, the spinor approach describes the motion of a rigid body as a whole and avoids the singularities that arise when described by a local coordinate system. In order to illustrate the advantages of the algorithm in terms of accuracy, time, convergence and adaptability, three experiments were conducted with a general six-degree-of-freedom industrial robotic arm, a PUMA560 robotic arm and a seven-degree-of-freedom robotic arm as the research objects. In all three experiments, the parameters of the robot arm, the range of joint angles, and the initial attitude and position of the end-effector of the robot arm are given, and the attitude and position of the impact point of the end-effector are set to verify whether the joint angles found by the algorithm can reach the specified positions. In Experiments 2 and 3, the algorithm proposed in this paper is compared with the traditional particle swarm algorithm (PSO) and quantum particle swarm algorithm (QPSO) in terms of position and direction solving accuracy, operation time, and algorithm convergence. The results show that compared with the other two algorithms, the algorithm proposed in this paper can ensure higher position accuracy and orientation accuracy of the robotic arm end-effector. the position error of the algorithm proposed in this paper is 0 and the maximum orientation error is 1.29 × 10–8. while the minimum position error of the other two algorithms is −1.64 × 10–5 and the minimum orientation error is −4.03 × 10–6. In terms of operation time, the proposed algorithm in this paper has shorter operation time compared with the other two algorithms. In the last two experiments, the computing time of the proposed algorithm is 0.31851 and 0.30004s respectively, while the shortest computing time of the other two algorithms is 0.33359 and 0.30521s respectively. In terms of algorithm convergence, the proposed algorithm can achieve faster and more stable convergence than the other two algorithms. After changing the experimental subjects, the proposed algorithm still maintains its advantages in terms of accuracy, time and convergence, which indicates that the proposed algorithm is more applicable and has certain potential in solving the multi-arm inverse kinematics solution. This paper provides a new way of thinking for solving the multi-arm inverse kinematics solution problem.

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“…An endless stream of attacks makes researchers pay attention to the research of vulnerabilities in electronic products and smart phones ( Niu et al, 2008 ; Zhou et al, 2016 ; Hur and Shamsi, 2017 ; Delgado-Santos et al, 2021 ). In order to get the optimal solution, bionic algorithms derived from nature often provide a novel research idea ( Liu et al, 2021a ; Xu et al, 2022 ; Zhang et al, 2022 ). In recent years, deep learning models have provided new solutions to traditional problems ( Jiang et al, 2021a ; Jiang et al, 2021b ; Tao et al, 2022 ; Yun et al, 2022 ).…”

Section: Related Workmentioning

confidence: 99%

Spider-Inspired HCCapture: Beware That What You Are Writing on Mobile Devices Is Becoming Prey for Spiders

Zhu

Chen

et al. 2022

Front. Bioeng. Biotechnol.

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On mobile devices, the most important input interface is touchscreen, which can transmit a large amount of sensitive information. Many researchers have proven that sensors can be used as side channels to leak touchscreen interactive information. The research of information leakage in the restricted area has been relatively mature, but in the unrestricted area, still there are two issues to be solved urgently: chirography difference and posture variation. We learn from the way spiders perceive prey through the subtle vibrations of their webs; an unrestricted-area handwriting information speculation framework, called spider-inspired handwriting character capture (spider-inspired HCCapture), is designed. Spider-inspired HCCapture exploits the motion sensor as the side-channel and uses the neural network algorithm to train the recognition model. To alleviate the impact of different handwriting habits, we utilize the generality patterns of characters rather than the patterns of raw sensor signals. Furthermore, each character is disassembled into basic strokes, which are used as recognition features. We also proposed a user-independent posture-aware approach to detect the user’s handwriting posture to select a suitable one from some pretrained models for speculation. In addition, the Markov model is introduced into spider-inspired HCCapture, which is used as an enhancement feature when there is a correlation between adjacent characters. In conclusion, spider-inspired HCCapture completes the handwritten character speculation attack without obtaining the victim’s information in advance. The experimental results show that the accuracy of spider-inspired HCCapture reaches 96.1%.

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Time Optimal Trajectory Planing Based on Improved Sparrow Search Algorithm

Cited by 49 publications

References 96 publications

Multi-Scale Feature Fusion Convolutional Neural Network for Indoor Small Target Detection

Multi-Scale Feature Fusion Convolutional Neural Network for Indoor Small Target Detection

A Tandem Robotic Arm Inverse Kinematic Solution Based on an Improved Particle Swarm Algorithm

Spider-Inspired HCCapture: Beware That What You Are Writing on Mobile Devices Is Becoming Prey for Spiders

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