A new approach for mobile robot path planning based on RRT algorithm

Nguyen, Thanh-Hung; Nguyen, Xuan-Thuan; Pham, Duc-An; Tran, Ba-Long; Bui, Dinh-Ba

doi:10.1142/s0217984923400274

Cited by 7 publications

(3 citation statements)

References 2 publications

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“…Nguyen, T.H. et al, proposed a new path-planning method by inserting a universal pseudo-random number generator into RRT, which improved the convergence and effectiveness of path planning for the mobile robot [7]. Sabudin, E.N.…”

Section: Introductionmentioning

confidence: 99%

E-DQN-Based Path Planning Method for Drones in Airsim Simulator under Unknown Environment

Chao,

Dillmann,

Roennau

et al. 2024

Biomimetics

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To improve the rapidity of path planning for drones in unknown environments, a new bio-inspired path planning method using E-DQN (event-based deep Q-network), referring to introducing event stream to reinforcement learning network, is proposed. Firstly, event data are collected through an airsim simulator for environmental perception, and an auto-encoder is presented to extract data features and generate event weights. Then, event weights are input into DQN (deep Q-network) to choose the action of the next step. Finally, simulation and verification experiments are conducted in a virtual obstacle environment built with an unreal engine and airsim. The experiment results show that the proposed algorithm is adaptable for drones to find the goal in unknown environments and can improve the rapidity of path planning compared with that of commonly used methods.

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Section: Introductionmentioning

confidence: 99%

E-DQN-Based Path Planning Method for Drones in Airsim Simulator under Unknown Environment

Chao,

Dillmann,

Roennau

et al. 2024

Biomimetics

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show abstract

“…Therefore, local path planning and dynamic obstacle avoidance algorithms for mobile robots must be studied [10][11][12]. To execute real-time obstacle avoidance and online planning based on the local environment information, local path planning uses many sensors, which gather local environment information in real time while the robot is moving.…”

Section: Introductionmentioning

confidence: 99%

Local Path Planning for Mobile Robots Based on Fuzzy Dynamic Window Algorithm

Sun,

Wang,

et al. 2023

Sensors

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Due to the increased employment of robots in modern society, path planning methods based on human–robot collaborative mobile robots have been the subject of research in both academia and industry. The dynamic window approach used in the research of the robot local path planning problem involves a mixture of fixed weight coefficients, which makes it hard to deal with the changing dynamic environment and the issue of the sub-optimal global planning paths that arise after local obstacle avoidance. By dynamically modifying the combination of weight coefficients, we propose, in this research, the use of fuzzy control logic to optimize the evaluation function’s sub-functions and enhance the algorithm’s performance through the safe and dynamic avoidance of obstacles. The global path is introduced to enhance the dynamic window technique’s ability to plan globally, and important points on the global path are selected as key sub-target sites for the local motion planning phase of the dynamic window technique. The motion position changes after local obstacle avoidance to keep the mobile robot on the intended global path. According to the simulation results, the enhanced dynamic window algorithm cuts planning time and path length by 16% and 5%, respectively, while maintaining good obstacle avoidance and considering a better global path in the face of various dynamic environments. It is difficult to achieve a local optimum using this algorithm.

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“…In 1967, Japan Heavy Industries Company purchased the production license of robots from the United States, and Japan began the upsurge of manufacturing and developing robots. With the wide application of robots in industry, strengthening the experimental teaching of robot platform and improving the control ability of learners to robots have become the most important task of robot teaching, and thus the robot teaching platform has been derived [1][2].…”

Section: Introductionmentioning

confidence: 99%

Application of Mobile Robot Virtual Simulation Platform in Teaching Management

Xie

2023

Proceedings of the 4th International Conference on Modern Education and Information Management, ICMEIM 2023, September 8–10, 20

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In order to understand the application of mobile robot virtual simulation platform, this article proposes an application research of mobile robot virtual simulation platform in teaching management. This virtual simulation platform is suitable for course teaching and has multiple advantages: universal interface, easy to use, flexible configuration, intuitive interface, etc. Secondly, through the adaptation of communication interfaces, this virtual simulation platform has the ability to control physical robots, achieving an experimental teaching technology that combines virtual and real, and applying it to course teaching. In this case, quickly loading the scene into memory to determine the rendering accuracy is very important for system speed, so it is usually necessary to add some additional controls to optimize the data structure, such as organizing the scene into a tree structure according to space, and using its hierarchical structure to traverse some scenes that need to be displayed. Finally, this article relies on the virtual simulation platform, adopts a task driven teaching method, and combines the teaching content in and out of class to conduct theoretical and experimental teaching of intelligent robot courses. This teaching method enables students to participate more actively in learning, cultivating their hands-on ability and problem-solving ability. This virtual simulation experimental platform provides new methods and ideas for the experimental teaching of intelligent robot courses, better assisting classroom teaching and improving students' learning outcomes.

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A new approach for mobile robot path planning based on RRT algorithm

Cited by 7 publications

References 2 publications

E-DQN-Based Path Planning Method for Drones in Airsim Simulator under Unknown Environment

E-DQN-Based Path Planning Method for Drones in Airsim Simulator under Unknown Environment

Local Path Planning for Mobile Robots Based on Fuzzy Dynamic Window Algorithm

Application of Mobile Robot Virtual Simulation Platform in Teaching Management

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