Research on Mobile Robot Path Planning Based on Improved A* and DWA Algorithms

Qian, Wei; Peng, Jiansheng; Zhang, Hongyu

doi:10.1007/978-981-99-9239-3_10

Lecture Notes in Electrical Engineering

2024

DOI: 10.1007/978-981-99-9239-3_10

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Research on Mobile Robot Path Planning Based on Improved A* and DWA Algorithms

Wei Qian,

Jiansheng Peng,

Hongyu Zhang

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2024

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Cited by 2 publications

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An effective robot selection and recharge scheduling approach for improving robotic networks performance

ElSayyad,

Saleh,

Ali

et al. 2024

Sci Rep

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With the ability of servers to remotely control and manage a mobile robot, mobile robots are becoming more widespread as a form of remote communication and human-robot interaction. Controlling these robots, however, can be challenging because of their power consumption, delays, or the challenge of selecting the right robot for a certain task. This paper introduces a novel methodology for enhancing the efficacy of a mobile robotic network. The key two contributions of our suggested methodology are: I: A recommended strategy that eliminates the unwieldy robots before selecting the ideal robot to satisfy the task. II: A suggested procedure that uses a fuzzy algorithm to schedule the robots that need to be recharged. Since multiple robots may need to be recharged at once, this process aims to manage and control the recharging of robots in order to avoid conflicts or crowding. The suggested approach aims to preserve the charging capacity, physical resources (e.g. Hardware components), and battery life of the robots by loading the application onto a remote server node instead of individual robots. Furthermore, our solution makes use of fog servers to speed up data transfers between smart devices and the cloud, it is also used to move processing from remote cloud servers closer to the robots, improving on-site access to location-based services and real-time interaction. Simulation results showed that, our method achieved a 2.4% improvement in average accuracy and a 2.2% enhancement in average power usage over the most recent methods in the same comparable settings.

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An effective robot selection and recharge scheduling approach for improving robotic networks performance

ElSayyad,

Saleh,

Ali

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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Noisy Dueling Double Deep Q-Network algorithm for autonomous underwater vehicle path planning

Liao,

Li,

Huang

et al. 2024

Front. Neurorobot.

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How to improve the success rate of autonomous underwater vehicle (AUV) path planning and reduce travel time as much as possible is a very challenging and crucial problem in the practical applications of AUV in the complex ocean current environment. Traditional reinforcement learning algorithms lack exploration of the environment, and the strategies learned by the agent may not generalize well to other different environments. To address these challenges, we propose a novel AUV path planning algorithm named the Noisy Dueling Double Deep Q-Network (ND3QN) algorithm by modifying the reward function and introducing a noisy network, which generalizes the traditional D3QN algorithm. Compared with the classical algorithm [e.g., Rapidly-exploring Random Trees Star (RRT*), DQN, and D3QN], with simulation experiments conducted in realistic terrain and ocean currents, the proposed ND3QN algorithm demonstrates the outstanding characteristics of a higher success rate of AUV path planning, shorter travel time, and smoother paths.

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Research on Mobile Robot Path Planning Based on Improved A* and DWA Algorithms

Cited by 2 publications

References 10 publications

An effective robot selection and recharge scheduling approach for improving robotic networks performance

An effective robot selection and recharge scheduling approach for improving robotic networks performance

Noisy Dueling Double Deep Q-Network algorithm for autonomous underwater vehicle path planning

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