Improved dynamic windows approach based on energy consumption management and fuzzy logic control for local path planning of mobile robots

Yao, Ming; Deng, Haigang; Feng, Xianying; Li, Peigang; Li, Yanfei; Liu, Haiyang

doi:10.1016/j.cie.2023.109767

Cited by 7 publications

(2 citation statements)

References 44 publications

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“…The path planning problem can be defined as the task of discovering a collision-free path from the starting point to the ending point (Aggarwal & Kumar, 2020). In the context of path planning, there are three types of planning: global planning (Tsardoulias, Iliakopoulou, Kargakos, & Petrou, 2016), local planning (Yao et al, 2023), and a combination of both (Meng et al, 2023).…”

Section: Background and Related Workmentioning

confidence: 99%

Smart Library: A Multi-Agent Path Planning Logistics Operation using Reinforcement Learning

Batista,

Carvalho,

Oliveira

et al. 2024

Preprint

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Robotics navigation holds significant importance in the transportation, manufacturing, and exploration sectors. The prioritization of path planning is essential for efficient navigation in environments where multiple robots coexist. In the realm of logistic libraries, characterized by repetitive tasks with a need to mitigate human errors, this study concentrates on achieving feasible and optimal path planning for multiple agents tasked with transporting books within a two-floor building. The proposed algorithm, based on Q-learning, incorporates transfer and curriculum learning, enabling cooperative and decentralized behavior among the agents. Numerical experiments conducted through a software-in-the-loop approach validate the effectiveness of the method. The results indicate a success rate of 94% with 9 agents, accompanied by a 73.36% reduction in task completion steps compared to a scenario with a single agent.This research serves to showcase the algorithm's capability in enhancing navigation and task efficiency in multi-agent logistics settings, particularly within smart library environments.

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Section: Background and Related Workmentioning

confidence: 99%

Smart Library: A Multi-Agent Path Planning Logistics Operation using Reinforcement Learning

Batista,

Carvalho,

Oliveira

et al. 2024

Preprint

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show abstract

“…These measures aim to improve algorithm performance and mitigate the problem of local optima. Therefore, many experts and scholars, both domestically and internationally, mainly focus on improving the original algorithms and algorithm fusion orientation as the primary research directions in path planning [32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Vehicle Path Planning Based on Optimized A* Algorithm

Chu,

Wang,

et al. 2024

Sensors

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With the rapid development of the intelligent driving technology, achieving accurate path planning for unmanned vehicles has become increasingly crucial. However, path planning algorithms face challenges when dealing with complex and ever-changing road conditions. In this paper, aiming at improving the accuracy and robustness of the generated path, a global programming algorithm based on optimization is proposed, while maintaining the efficiency of the traditional A* algorithm. Firstly, turning penalty function and obstacle raster coefficient are integrated into the search cost function to increase the adaptability and directionality of the search path to the map. Secondly, an efficient search strategy is proposed to solve the problem that trajectories will pass through sparse obstacles while reducing spatial complexity. Thirdly, a redundant node elimination strategy based on discrete smoothing optimization effectively reduces the total length of control points and paths, and greatly reduces the difficulty of subsequent trajectory optimization. Finally, the simulation results, based on real map rasterization, highlight the advanced performance of the path planning and the comparison among the baselines and the proposed strategy showcases that the optimized A* algorithm significantly enhances the security and rationality of the planned path. Notably, it reduces the number of traversed nodes by 84%, the total turning angle by 39%, and shortens the overall path length to a certain extent.

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A human-like collision avoidance method for USVs based on deep reinforcement learning and velocity obstacle

Yang,

Lou,

et al. 2024

Expert Systems with Applications

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Improved dynamic windows approach based on energy consumption management and fuzzy logic control for local path planning of mobile robots

Cited by 7 publications

References 44 publications

Smart Library: A Multi-Agent Path Planning Logistics Operation using Reinforcement Learning

Smart Library: A Multi-Agent Path Planning Logistics Operation using Reinforcement Learning

Intelligent Vehicle Path Planning Based on Optimized A* Algorithm

A human-like collision avoidance method for USVs based on deep reinforcement learning and velocity obstacle

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