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18 pages, 10943 KiB

Open AccessArticle

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

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Appl. Sci. 2023, 13(17), 9955; https://doi.org/10.3390/app13179955 - 03 Sep 2023

Cited by 1 | Viewed by 643

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

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

Viewed by 818

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

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Rui Li

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Shimin Liu

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Xiaojie Su

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

Cited by 1 | Viewed by 820

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

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

Viewed by 1137

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

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

Cited by 1 | Viewed by 1809

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

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Baoyu Wen

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Jiangshuai Huang

Appl. Sci. 2022, 12(24), 12579; https://doi.org/10.3390/app122412579 - 08 Dec 2022

Viewed by 853

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

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

Viewed by 727

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

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

Cited by 1 | Viewed by 1773

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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