Human Following for Outdoor Mobile Robots Based on Point‐Cloud's Appearance Model

Linxi, Gong; Cai, Yi

doi:10.1049/cje.2021.07.017

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…Gong et al proposed a point cloud-based algorithm, employing a particle filter to continuously track the target's position. This enables the robot to detect and track the target individual in outdoor environments [32]. Tsai et al achieved humanfollowing in outdoor scenes using depth sensors to determine the distance between the tracking target and obstacles [33].…”

Section: Related Workmentioning

confidence: 99%

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Human-Following Strategy for Orchard Mobile Robot Based on the KCF-YOLO Algorithm

Huang,

Ou,

Guo

et al. 2024

Horticulturae

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Autonomous mobile robots play a vital role in the mechanized production of orchards, where human-following is a crucial collaborative function. In unstructured orchard environments, obstacles often obscure the path, and personnel may overlap, leading to significant disruptions to human-following. This paper introduces the KCF-YOLO fusion visual tracking method to ensure stable tracking in interference environments. The YOLO algorithm provides the main framework, and the KCF algorithm intervenes in assistant tracking. A three-dimensional binocular-vision reconstruction method was used to acquire personnel positions, achieving stabilized visual tracking in disturbed environments. The robot was guided by fitting the personnel’s trajectory using an unscented Kalman filter algorithm. The experimental results show that, with 30 trials in multi-person scenarios, the average tracking success rate is 96.66%, with an average frame rate of 8 FPS. Additionally, the mobile robot is capable of maintaining a stable following speed with the target individuals. Across three human-following experiments, the horizontal offset Error Y does not exceed 1.03 m. The proposed KCF-YOLO tracking method significantly bolsters the stability and robustness of the mobile robot for human-following in intricate orchard scenarios, offering an effective solution for tracking tasks.

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Section: Related Workmentioning

confidence: 99%

“…and track the target individual in outdoor environments [32]. Tsai et al achieved humanfollowing in outdoor scenes using depth sensors to determine the distance between the tracking target and obstacles [33].…”

Section: Algorithmmentioning

confidence: 99%

Human-Following Strategy for Orchard Mobile Robot Based on the KCF-YOLO Algorithm

Huang,

Ou,

Guo

et al. 2024

Horticulturae

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“…For the application development problem of rehabilitation robots, Liu Y et al proposed to use a control method based on surface EMG signals and combined with principal component analysis to improve the recognition accuracy, which in turn improves the effect of skeletal rehabilitation training [14]. Linxi et al proposed a design feature space based on sparse point clouds to distinguish target characters for the tracking problem of outdoor mobile robots, and combined with motion planning algorithms to verify target detection and tracking performance, thereby increasing the robustness of robots to complex outdoor environments [15]. Naya Varela et al proposed to combine biological morphological development and controllers for the bipedal robot walking problem, and then use neural evolution algorithms to verify the feasibility and practicality of bipedal robot walking [16].…”

Section: Related Workmentioning

confidence: 99%

Tendon-Driven Robotic Arm Control Method Based on Radial Basis Function Adaptive Tracking Algorithm

Fang

2024

ijacsa

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With the rapid development of intelligent technology, robotic arms are widely used in different fields. The study combines the tendon drive theory and radial basis function neural network to construct the robotic arm model, and then combines the back-stepping method and non-singular fast terminal sliding mode to improve the controller and system optimization of the tendon drive robotic arm model. Simulation tests on commercial mathematical software platforms yielded that joint 2 achieves stable overlap of position trajectory and velocity trajectory after 0.2s and 0.5s with errors of 1° and 1°/s, respectively. Radial basis function neural network approximation of robotic arm error converged to the true value at 14s. The optimized joint achieved the accuracy of trajectory tracking after 0.2s. Also the control torque of joint 2 changes at 1.5s, 4.5s and 8s and its change is small. The tendon tension curve was smoother and more stable in the range of -0.05N~0.0.5N to show that the robotic arm model has superiority after the optimization of the controller, and the interference observer had accurate estimation of the tracking trajectory of the tendon-driven robotic arm. Therefore, the radial basis function-based adaptive tracking algorithm had higher accuracy for the tendon-driven robotic arm model and provided technical reference for the control system of the intelligent robotic arm.

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“…The initial position of AMR is taken as I = (12,350) and the end point of AMR, E =(650,7). The performance of the proposed ABCDLR model has been compared with other existing path planning approaches such as VFH [40], FLC [41], A* [42], and ASGDLR [3]. Simulation environment has been created on MATLAB 2022a, considering all real time scenarios.…”

Section: Avoidance Of Single Obstaclementioning

confidence: 99%

Machine Learning based Intelligent Model for Path Planning Obstacle Avoidance in Dense Environments for Autonomous Mobile Robot

Thakur¹,

Das²,

Kumari

et al. 2023

Preprint

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In this paper, a unique Machine Learning (ML) model namely, Adaptive Block Coordinate Descent Logistic Regression (ABCDLR), is proposed for segregating the movement of an Autonomous Mobile Robot (AMR) by framing it as three class problem, i.e., no, left, and right turn. The velocities of the left and right wheels, as well as the distance of the obstacle from AMR, are collected in real time by two Infrared (IR) and one Ultrasonic (US) sensors, respectively. The performance of the proposed algorithm is compared with three other state-of-the-art ML algorithms, such as, K-Nearest Neighbour (KNN), Naïve Baiyes, and Gradient Boosting, for obstacle avoidance by AMR; considering the accuracy, sensitivity, specificity, precision values for three different speed conditions, i.e., low, medium, and high. Various Logistic Regression (LR) model parameters, such as, pseudo R-squared (R2), Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), LL-null, and Log-Likelihood Ratio (LLR) are considered to investigate the performance of the proposed ABCDLR model. Furthermore, the proposed model has been applied for path planning in three different types of dense environments, and its performance is compared with four other competitive path planning approaches, such as, A*, Fuzzy Logic Controller(FLC), Vector Field Histogram(VFH) and ASGDLR.

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Human Following for Outdoor Mobile Robots Based on Point‐Cloud's Appearance Model

Cited by 8 publications

References 14 publications

Human-Following Strategy for Orchard Mobile Robot Based on the KCF-YOLO Algorithm

Human-Following Strategy for Orchard Mobile Robot Based on the KCF-YOLO Algorithm

Tendon-Driven Robotic Arm Control Method Based on Radial Basis Function Adaptive Tracking Algorithm

Machine Learning based Intelligent Model for Path Planning Obstacle Avoidance in Dense Environments for Autonomous Mobile Robot

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