Applied Sciences

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24 pages, 4712 KiB

Open AccessArticle

Balancing Efficiency and Accuracy: Enhanced Visual Simultaneous Localization and Mapping Incorporating Principal Direction Features

by Yuelin Yuan, Fei Li, Xiaohui Liu and Jialiang Chen

Appl. Sci. 2024, 14(19), 9124; https://doi.org/10.3390/app14199124 - 9 Oct 2024

Viewed by 1464

Abstract

In visual Simultaneous Localization and Mapping (SLAM), operational efficiency and localization accuracy are equally crucial evaluation metrics. We propose an enhanced visual SLAM method to ensure stable localization accuracy while improving system efficiency. It can maintain localization accuracy even after reducing the number [...] Read more.

In visual Simultaneous Localization and Mapping (SLAM), operational efficiency and localization accuracy are equally crucial evaluation metrics. We propose an enhanced visual SLAM method to ensure stable localization accuracy while improving system efficiency. It can maintain localization accuracy even after reducing the number of feature pyramid levels by 50%. Firstly, we innovatively incorporate the principal direction error, which represents the global geometric features of feature points, into the error function for pose estimation, utilizing Pareto optimal solutions to improve the localization accuracy. Secondly, for loop-closure detection, we construct a feature matrix by integrating the grayscale and gradient direction of an image. This matrix is then dimensionally reduced through aggregation, and a multi-layer detection approach is employed to ensure both efficiency and accuracy. Finally, we optimize the feature extraction levels and integrate our method into the visual system to speed up the extraction process and mitigate the impact of the reduced levels. We comprehensively evaluate the proposed method on local and public datasets. Experiments show that the SLAM method maintained high localization accuracy after reducing the tracking time by 24% compared with ORB SLAM3. Additionally, the proposed loop-closure-detection method demonstrated superior computational efficiency and detection accuracy compared to the existing methods. Full article

(This article belongs to the Special Issue Mobile Robotics and Autonomous Intelligent Systems)

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14 pages, 3259 KiB

Open AccessArticle

Design and Implementation of a Two-Wheeled Self-Balancing Car Using a Fuzzy Kalman Filter

by Yimin Ma, Fanhao Meng and Shuangshuang Xiong

Appl. Sci. 2024, 14(12), 5296; https://doi.org/10.3390/app14125296 - 19 Jun 2024

Viewed by 1404

Abstract

To improve the upright balancing performance of the two-wheeled self-balancing car, this paper proposes an attitude estimation algorithm based on fuzzy Kalman filtering. Fuzzy logic is used to correct the inclination angle and angular velocity of the two-wheeled self-balancing car, thereby optimizing the [...] Read more.

To improve the upright balancing performance of the two-wheeled self-balancing car, this paper proposes an attitude estimation algorithm based on fuzzy Kalman filtering. Fuzzy logic is used to correct the inclination angle and angular velocity of the two-wheeled self-balancing car, thereby optimizing the state of the Kalman filter and ultimately improving the balancing performance of the car. This paper combines dual closed-loop PID control with the complementary filtering algorithm, Kalman filtering algorithm, and fuzzy Kalman filtering algorithm to conduct experiments on a physical two-wheeled self-balancing car. The experimental results validate the superiority of the fuzzy Kalman filtering algorithm proposed in this paper for improving the upright balancing performance of the two-wheeled self-balancing car. Full article

(This article belongs to the Special Issue Mobile Robotics and Autonomous Intelligent Systems)

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25 pages, 7224 KiB

Open AccessArticle

Enhanced Berth Mapping and Clothoid Trajectory Prediction Aided Intelligent Underground Localization

by Fei Li, Jialiang Chen, Yuelin Yuan, Zhaozheng Hu and Xiaohui Liu

Appl. Sci. 2024, 14(12), 5032; https://doi.org/10.3390/app14125032 - 9 Jun 2024

Cited by 1 | Viewed by 1297

Abstract

In response to the widespread absence of global navigation satellite system (GNSS) signals in underground parking scenes, we propose a multimodal localization method that integrates enhanced berth mapping with Clothoid trajectory prediction, enabling high-precision localization for intelligent vehicles in underground parking environments. This [...] Read more.

In response to the widespread absence of global navigation satellite system (GNSS) signals in underground parking scenes, we propose a multimodal localization method that integrates enhanced berth mapping with Clothoid trajectory prediction, enabling high-precision localization for intelligent vehicles in underground parking environments. This method began by constructing a lightweight map based on the key berths. The map consisted of a series of discrete nodes, each encompassing three elements: holistic and local scene features extracted from an around-view image, and the global pose of the mapping vehicle calculated using the positions of the key berth’s corner points. An adaptive localization strategy was employed during the localization phase based on the trajectory prediction result. A progressive localization strategy, relying on multi-scale feature matching, was applied to the nodes within the map coverage range. Additionally, a compensation localization strategy that combined odometry with the prior pose was utilized for the nodes outside the map coverage range. The experiments conducted in two typical underground parking scenes demonstrated that the proposed method achieved a trajectory prediction accuracy of 40 cm, a nearest map search accuracy exceeding 92%, and a metric localization accuracy meeting the 30 cm standard. These results indicate that the proposed approach satisfies the high-precision, robust, real-time localization requirements for intelligent vehicles in underground parking scenes, while effectively reducing the map memory requirements. Full article

(This article belongs to the Special Issue Mobile Robotics and Autonomous Intelligent Systems)

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22 pages, 2513 KiB

Open AccessArticle

Comprehensive Performance Evaluation of Earthquake Search and Rescue Robots Based on Improved FAHP and Radar Chart

by Liming Li and Zeang Zhao

Appl. Sci. 2024, 14(7), 3099; https://doi.org/10.3390/app14073099 - 7 Apr 2024

Cited by 1 | Viewed by 2216

Abstract

To effectively enhance the adaptability of earthquake rescue robots in dynamic environments and complex tasks, there is an urgent need for an evaluation method that quantifies their performance and facilitates the selection of rescue robots with optimal overall capabilities. In this paper, twenty-two [...] Read more.

To effectively enhance the adaptability of earthquake rescue robots in dynamic environments and complex tasks, there is an urgent need for an evaluation method that quantifies their performance and facilitates the selection of rescue robots with optimal overall capabilities. In this paper, twenty-two evaluation criteria are proposed based on a comprehensive review of existing evaluation criteria for rescue robots across various domains. The evaluation criteria are tested using the test modules developed by the National Earthquake Response support service, obtaining the corresponding values for each criterion. Then, the weights of the criterion layer and comprehensive evaluation index are determined based on the analytical hierarch process and trapezoidal fuzzy number complementary judgment matrix, and a new consistency test method is proposed. The qualitative evaluation and quantitative analysis are effectively combined to overcome the subjective influence of expert decision-making. Additionally, the performance of three earthquake search and rescue robots is comprehensively evaluated and ranked using the improved radar chart method as an empirical example. Finally, the robustness of the ranking results is examined using a weight sensitivity analysis. The results of the sensitivity analysis demonstrate the effectiveness and feasibility of the proposed method, thereby providing valuable insights for developing multi-objective optimization control strategies and structural designs for earthquake search and rescue robots. Full article

(This article belongs to the Special Issue Mobile Robotics and Autonomous Intelligent Systems)

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25 pages, 12147 KiB

Open AccessArticle

Development of Portable Magnetic Adsorption Amphibious Robot

by Fushen Ren, Jiaxiang Zhu, Jun Liu, Baojin Wang and Kekuan Wang

Appl. Sci. 2024, 14(7), 2820; https://doi.org/10.3390/app14072820 - 27 Mar 2024

Cited by 2 | Viewed by 1521

Abstract

In this study, a portable magnetic adsorption amphibious robot which can operate on and below the waterline is developed for special curved environments, such as the pile legs of offshore platforms and the outer walls of ships. An open robot integrated control system [...] Read more.

In this study, a portable magnetic adsorption amphibious robot which can operate on and below the waterline is developed for special curved environments, such as the pile legs of offshore platforms and the outer walls of ships. An open robot integrated control system based on a domestic chip is developed, and two operating modes of local control operation and remote wireless operation are realized. A permanent magnet adsorption scheme combining a magnetic adsorption track and a synchronous belt wheel is designed, static and dynamic analysis of the wall-climbing operation of the robot is carried out, and a kinematic model of the underwater robot is established. The experimental results show that the robot can effectively complete amphibious tasks and can realize the accurate control of attitude in water, proving it to be an effective tool for amphibious tasks, such as operating on the pile legs of offshore platforms and the outer walls of ships. Full article

(This article belongs to the Special Issue Mobile Robotics and Autonomous Intelligent Systems)

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16 pages, 3705 KiB

Open AccessArticle

A Tube Linear Model Predictive Control Approach for Autonomous Vehicles Subjected to Disturbances

by Jianqiao Chen and Guofu Tian

Appl. Sci. 2024, 14(7), 2793; https://doi.org/10.3390/app14072793 - 27 Mar 2024

Cited by 1 | Viewed by 1385

Abstract

The path tracking performance of autonomous vehicles is degraded by common disturbances, especially those that affect the safety of autonomous vehicles (AVs) in obstacle avoidance conditions. To improve autonomous vehicle tracking performances and their computational efficiency when subjected to common disturbances, this paper [...] Read more.

The path tracking performance of autonomous vehicles is degraded by common disturbances, especially those that affect the safety of autonomous vehicles (AVs) in obstacle avoidance conditions. To improve autonomous vehicle tracking performances and their computational efficiency when subjected to common disturbances, this paper proposes a tube linear model predictive controller (MPC) framework for autonomous vehicles. A bicycle vehicle dynamics model is developed and employed in the tube MPC control design in the proposed framework. A robust invariant set is calculated with an efficient linear programming (LP) method, and it is used to guarantee that the constraints are satisfied under common disturbance conditions. The results show that the computational cost of robust positively invariant sets that are constructed by the LP method is much less than that obtained by the traditional method. In addition, all the trajectories of the tube linear MPC successfully avoided obstacles when under disturbance conditions, but only about 80% of the trajectories obtained with the traditional MPC successfully avoided obstacles under disturbance conditions. The proposed framework is effective. Full article

(This article belongs to the Special Issue Mobile Robotics and Autonomous Intelligent Systems)

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13 pages, 6352 KiB

Open AccessArticle

A Real-Time Sinkage Detection Method for the Planetary Robotic Wheel-on-Limb System via a Monocular Camera

by Baochang Liu, Lihang Feng and Dong Wang

Appl. Sci. 2024, 14(6), 2319; https://doi.org/10.3390/app14062319 - 9 Mar 2024

Cited by 1 | Viewed by 1353

Abstract

When traversing soft and rugged terrain, a planetary rover is susceptible to slipping and sinking, which impedes its movement. The real-time detection of wheel sinkage in the planetary wheel-on-limb system is crucial for enhancing motion safety and passability on such terrain. Initially, this [...] Read more.

When traversing soft and rugged terrain, a planetary rover is susceptible to slipping and sinking, which impedes its movement. The real-time detection of wheel sinkage in the planetary wheel-on-limb system is crucial for enhancing motion safety and passability on such terrain. Initially, this study establishes a measurement of wheel sinkage under complex terrain conditions. Subsequently, a monocular vision-based wheel sinkage detection method is presented by combining the wheel–terrain boundary with the wheel center position (WTB-WCP). The method enables the efficient and accurate detection of wheel sinkage through two-stage parallel computation of the wheel–terrain boundary fitting and wheel center localization. Finally, this study establishes an experimental platform based on a monocular camera and the planetary rover wheel-on-limb system to experimentally validate and comparatively analyze the proposed method. The experimental results demonstrate that the method effectively provides information on the wheel sinkage of the planetary rover wheel-on-limb system, and the relative errors of the method do not exceed 4%. The method has high accuracy and reliability and is greatly significant for the safety and passability of planetary rovers in soft and rugged terrain. Full article

(This article belongs to the Special Issue Mobile Robotics and Autonomous Intelligent Systems)

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14 pages, 5456 KiB

Open AccessArticle

Path Planning of Obstacle-Crossing Robot Based on Golden Sine Grey Wolf Optimizer

by Di Zhao, Guangrui Cai, Yuxing Wang and Xixing Li

Appl. Sci. 2024, 14(3), 1129; https://doi.org/10.3390/app14031129 - 29 Jan 2024

Cited by 7 | Viewed by 1427

Abstract

This paper proposes a golden sine grey wolf optimizer (GSGWO) that can be adapted to the obstacle-crossing function to solve the path planning problem of obstacle-crossable robot. GSGWO has been improved from the gray wolf optimizer (GWO), which provide slow convergence speed and [...] Read more.

This paper proposes a golden sine grey wolf optimizer (GSGWO) that can be adapted to the obstacle-crossing function to solve the path planning problem of obstacle-crossable robot. GSGWO has been improved from the gray wolf optimizer (GWO), which provide slow convergence speed and easy to fall into local optimum, especially without obstacle-crossing function. Firstly, aiming at the defects of GWO, the chaotic map is introduced to enrich the initial population and improve the convergence factor curve. Then, the convergence strategy of the golden sine optimizer is introduced to improve the shortcomings of GWO, such as insufficient convergence speed in the later stage and the ease with which it falls into the local optimum. Finally, by adjusting the working environment model, path generation method and fitness function, the path-planning problem of the obstacle-crossing robot is adapted. In order to verify the feasibility of the algorithm, four standard test functions and three different scale environment models are selected for simulation experiments. The results show that in the performance test of the algorithm, the GSGWO has higher convergence speed and accuracy than the GWO under different test functions. In the path-planning experiment, the length, number and size of inflection points and stability of the path planned by the GSGWO are better than those of the GWO. The feasibility of the GSGWO is verified. Full article

(This article belongs to the Special Issue Mobile Robotics and Autonomous Intelligent Systems)

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18 pages, 4783 KiB

Open AccessArticle

Formation Control of Nonlinear Multi-Agent Systems with Nested Input Saturation

by Panagiotis S. Trakas, Andreas Tantoulas and Charalampos P. Bechlioulis

Appl. Sci. 2024, 14(1), 213; https://doi.org/10.3390/app14010213 - 26 Dec 2023

Cited by 2 | Viewed by 1571

Abstract

A decentralized robust control protocol addressing leader-follower formation control of unknown nonlinear input-constrained multi-agent systems with adaptive performance specifications is proposed in this paper. The performance characteristics predefined by the user are adaptively modified in order to comply with the actuation constraints of [...] Read more.

A decentralized robust control protocol addressing leader-follower formation control of unknown nonlinear input-constrained multi-agent systems with adaptive performance specifications is proposed in this paper. The performance characteristics predefined by the user are adaptively modified in order to comply with the actuation constraints of the agents regarding both the magnitude and the rate of the control signals, ensuring closed-loop stability. The proposed control protocol is characterized by easy gain tuning and low structural complexity which simplifies the integration to real systems. A thorough experiment involving a system of multiple quadrotors was conducted to clarify and verify the theoretical findings. Full article

(This article belongs to the Special Issue Mobile Robotics and Autonomous Intelligent Systems)

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18 pages, 10943 KiB

Open AccessArticle

Path Planning of Rail-Mounted Logistics Robots Based on the Improved Dijkstra Algorithm

by Xiwei Zhou, Jingwen Yan, Mei Yan, Kaihao Mao, Ruizhe Yang and Weiyu Liu

Appl. Sci. 2023, 13(17), 9955; https://doi.org/10.3390/app13179955 - 3 Sep 2023

Cited by 24 | Viewed by 3380

Abstract

With the upgrading of manufacturing production lines and innovations in information technology, logistics robot technology applied in factories is maturing. Rail-mounted logistics robots are suitable for precise material distribution in large production workshops with fixed routes and over long distances. However, designing an [...] Read more.

With the upgrading of manufacturing production lines and innovations in information technology, logistics robot technology applied in factories is maturing. Rail-mounted logistics robots are suitable for precise material distribution in large production workshops with fixed routes and over long distances. However, designing an efficient path-planning algorithm is the key to realizing high efficiency in multi-robot system operations with rail logistics. Therefore, this paper proposes an improved Dijkstra algorithm that introduces real-time node occupancy and a time window conflict judgment model for global path planning and conflict coordination in multi-robot systems. More specifically, the introduction of real-time node occupancy can determine the shortest feasible routes for each task, and the introduction of the time window conflict judgment model can avoid the route conflict problem in the execution of multiple tasks, planning the shortest route without conflict. For the robot UBW positioning module, a Chan algorithm based on TDOA is proposed to realize the accurate positioning of rail-mounted logistics robots during their operation. Compared with the traditional Dijkstra algorithm, the results show that the algorithm proposed herein can plan a conflict-free and better path and dynamically adjust the on-orbit conflict in real time to avoid track congestion and efficiently complete multiple distribution tasks. Full article

(This article belongs to the Special Issue Mobile Robotics and Autonomous Intelligent Systems)

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13 pages, 4394 KiB

Open AccessArticle

Study on Head Stabilization Control Strategy of Non-Wheeled Snake Robot Based on Inertial Sensor

by Liming Bao, Yongjun Sun, Qiang Wang and Zongwu Xie

Appl. Sci. 2023, 13(7), 4477; https://doi.org/10.3390/app13074477 - 31 Mar 2023

Cited by 2 | Viewed by 1778

Abstract

In this paper, the head stabilization problem of the snake robot in planar motion is studied. When the snake robot performs a planar movement with an inchworm locomotion gait, the head controller of the snake robot swings up and down due to a [...] Read more.

In this paper, the head stabilization problem of the snake robot in planar motion is studied. When the snake robot performs a planar movement with an inchworm locomotion gait, the head controller of the snake robot swings up and down due to a fluctuation in the joint angle of the neck joint. However, the snake robot usually has a laser radar and other visual instruments on the head, and the swing of the head causes the visual instrument to fail to obtain external visual information normally, which affects the navigation and detection of the snake robot. In this paper, a head stabilization method for a snake robot in planar motion is proposed. The inertial sensor is used to obtain the direction parameters to control the swing of the head when the snake robot moves, and the effectiveness of the method is verified by a simulation and an experiment of the real robot. Full article

(This article belongs to the Special Issue Mobile Robotics and Autonomous Intelligent Systems)

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12 pages, 968 KiB

Open AccessArticle

Learning Form Closure Grasping with a Four-Pin Parallel Gripper

by Rui Li, Shimin Liu and Xiaojie Su

Appl. Sci. 2023, 13(4), 2506; https://doi.org/10.3390/app13042506 - 15 Feb 2023

Cited by 1 | Viewed by 2126

Abstract

Being able to stably grasp with generalization is one of the distinguished capabilities for building a generic grasping system for robots. In this work, we propose a stable grasping method for four-pin parallel grippers within a reinforcement learning framework. First, a reinforcement learning [...] Read more.

Being able to stably grasp with generalization is one of the distinguished capabilities for building a generic grasping system for robots. In this work, we propose a stable grasping method for four-pin parallel grippers within a reinforcement learning framework. First, a reinforcement learning problem is constructed on the basis of the improved four-pin gripper. Then, the learning policy and the reward function are constructed in consideration of the knowledge of environmental constraint and form closure. Finally, the effectiveness of the designed grasping method is validated in a simulated environment, and the results demonstrate that a safe and stable grasp can be planned for given 2.5D objects. Full article

(This article belongs to the Special Issue Mobile Robotics and Autonomous Intelligent Systems)

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12 pages, 1886 KiB

Open AccessArticle

Underground Garage Patrol Based on Road Marking Recognition by Keras and Tensorflow

by Jianwen Gan, Longqing Zhang, Hongming Chen, Liping Bai, Xinwei Zhang, Lei Yang and Yanghong Zhang

Appl. Sci. 2023, 13(4), 2385; https://doi.org/10.3390/app13042385 - 13 Feb 2023

Cited by 1 | Viewed by 2226

Abstract

The purpose of this study was to design an unmanned patrol service in combination with artificial intelligence technology to solve the problem of underground vehicle patrol. This design used the Raspberry Pi development board, L298N driver chip, Raspberry Pi camera, and other major [...] Read more.

The purpose of this study was to design an unmanned patrol service in combination with artificial intelligence technology to solve the problem of underground vehicle patrol. This design used the Raspberry Pi development board, L298N driver chip, Raspberry Pi camera, and other major hardware equipment to transform the remote control car. This design used Python as the programming language. By writing Python code, the car could be driven under the control of the computer keyboard and the camera was turned on for data collection. The Keras neural network library was used to quickly build a neural network model, the collected data was used to train the model, and the model was finally generated. The model was placed in the TensorFlow system for processing, and the car could travel in a preset track for unmanned driving. Full article

(This article belongs to the Special Issue Mobile Robotics and Autonomous Intelligent Systems)

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17 pages, 3693 KiB

Open AccessArticle

Autonomous Visual Navigation System Based on a Single Camera for Floor-Sweeping Robot

by Jinjun Rao, Haoran Bian, Xiaoqiang Xu and Jinbo Chen

Appl. Sci. 2023, 13(3), 1562; https://doi.org/10.3390/app13031562 - 25 Jan 2023

Cited by 6 | Viewed by 4532

Abstract

The indoor sweeping robot industry has developed rapidly in recent years. The current sweeping robot environment perception sensor configuration is more diverse and generally does not have active garbage detection capabilities. Advances in computer vision technology, artificial intelligence, and cloud computing technology have [...] Read more.

The indoor sweeping robot industry has developed rapidly in recent years. The current sweeping robot environment perception sensor configuration is more diverse and generally does not have active garbage detection capabilities. Advances in computer vision technology, artificial intelligence, and cloud computing technology have provided new possibilities for the development of sweeping robot technology. This paper conceptualizes a new autonomous visual navigation system based on a single-camera sensor for floor-sweeping robots. It investigates critical technologies such as floor litter recognition, environmental perception, and dynamic local path planning based on depth maps. The system is applied to the TurtleBot robot for experiments, and the results show that the mAP accuracy of the autonomous visual navigation system for fine trash recognition is 91.28%; it reduces the average relative error of depth perception by 10.4% compared to conventional methods. Moreover, it has greatly improved the dynamics and immediacy of path planning. Full article

(This article belongs to the Special Issue Mobile Robotics and Autonomous Intelligent Systems)

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14 pages, 1003 KiB

Open AccessArticle

Leader-Following Formation Tracking Control of Nonholonomic Mobile Robots Considering Collision Avoidance: A System Transformation Approach

by Baoyu Wen and Jiangshuai Huang

Appl. Sci. 2022, 12(24), 12579; https://doi.org/10.3390/app122412579 - 8 Dec 2022

Cited by 3 | Viewed by 1912

Abstract

In this paper, the obstacle avoidance problem-based leader–following formation tracking of nonholonomic wheeled mobile robots with unknown parameters of desired trajectory is investigated. First, the under-actuated system is transformed into a fully-actuated system by obtaining an auxiliary control variable using the transverse function. [...] Read more.

In this paper, the obstacle avoidance problem-based leader–following formation tracking of nonholonomic wheeled mobile robots with unknown parameters of desired trajectory is investigated. First, the under-actuated system is transformed into a fully-actuated system by obtaining an auxiliary control variable using the transverse function. Second, by introducing a potential function for each obstacle, the influence of obstacles is considered in trajectory tracking, and the effect of the potential field on mobile robots is taken into account in the system tracking error. Third, the adaptive laws are designed to estimate the unknown parameters of the desired trajectory. Fourth, the results show that the formation error with respect to the actual position and orientation can be arbitrarily small by selecting appropriate design parameters. Finally, simulation examples are used to demonstrate that the proposed control scheme is effective. Full article

(This article belongs to the Special Issue Mobile Robotics and Autonomous Intelligent Systems)

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14 pages, 2349 KiB

Open AccessArticle

Temporal Logic Planning and Receding Horizon Control for Signal Source Localization

by Xingtong Chen, Qiang Lu, Dilong Chen and Boyuan Geng

Appl. Sci. 2022, 12(21), 10984; https://doi.org/10.3390/app122110984 - 30 Oct 2022

Viewed by 1314

Abstract

This article copes with signal source localization by employing a receding horizon control approach with temporal logic planning in the light of a single mobile robot. First, a temporal logic planning approach is proposed such that the task requirements from the temporal logic [...] Read more.

This article copes with signal source localization by employing a receding horizon control approach with temporal logic planning in the light of a single mobile robot. First, a temporal logic planning approach is proposed such that the task requirements from the temporal logic specifications can be effectively dealt with based on the product automaton in an offline fashion. Second, in order to label the key nodes of the product automaton, a particle filter is utilized to predict the source positions as the key nodes. Third, on the basis of the product automaton, a receding horizon control approach with temporal logic planning is developed to produce the robot’s trajectory that satisfies a given linear temporal logic specification. Finally, the effectiveness of the proposed control approach is illustrated for signal source localization. Full article

(This article belongs to the Special Issue Mobile Robotics and Autonomous Intelligent Systems)

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19 pages, 7441 KiB

Open AccessArticle

Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor

by Junhong Zhao, Bin Li, Xinyu Wei, Huazhong Lu, Enli Lü and Xingxing Zhou

Appl. Sci. 2022, 12(20), 10331; https://doi.org/10.3390/app122010331 - 13 Oct 2022

Cited by 5 | Viewed by 4211

Abstract

(1) Background: Forklifts are used widely in factories, but it shows the problem of large uncertainties when using an RGB-D sensor to recognize and locate pallets in warehouse environments. To enhance the flexibility of current autonomous forklifts in unstructured environments, the improved labeled [...] Read more.

(1) Background: Forklifts are used widely in factories, but it shows the problem of large uncertainties when using an RGB-D sensor to recognize and locate pallets in warehouse environments. To enhance the flexibility of current autonomous forklifts in unstructured environments, the improved labeled template matching algorithm was proposed to recognize pallets. (2) Methods: The algorithm comprises four steps: (i) classifying each pixel of a color image with the color feature and obtaining the category matrix; (ii) building a labeled template containing the goods, pallet, and ground category information; (iii) compressing and matching the category matrix and template to determine the region of the pallet; and (iv) extracting the pallet pose from information in respect of the pallet feet. (3) Results: The results show that the proposed algorithm is robust against environmental influences and obstacles and that it can precisely recognize and segment multiple pallets in a warehouse with a 92.6% detection rate. The time consumptions were 72.44, 85.45, 117.63, and 182.84 ms for detection distances of 1000, 2000, 3000, and 4000 mm, respectively. (4) Conclusions: Both static and dynamic experiments were conducted, and the results demonstrate that the detection accuracy is directly related to the detection angle and distance. Full article

(This article belongs to the Special Issue Mobile Robotics and Autonomous Intelligent Systems)

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