Addressing Local Minima in Path Planning for Drones with Reinforcement Learning-Based Vortex Artificial Potential Fields by Boyi Xiao, Lujun Wan, Xueyan Han, Zhilong Xi, Chenbo Ding, Qiang Li

“…In complex environments, autonomous navigation for quadrotor drones presents challenges in terms of obstacle avoidance and path planning. …”
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Research of UAV 3D path planning based on improved Dwarf mongoose algorithm with multiple strategies by Lixin Mu, Wenhui Liu, Haocheng Wang, Yu Zhang

“…To enhance UAV adaptability in such environments, improve rapid and efficient path planning capabilities, and reduce operational costs, this paper proposes a 3D UAV path planning algorithm based on an improved Dwarf Mongoose Optimization (DMO) algorithm enhanced with multiple strategies. …”
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DBO-AWOA: An Adaptive Whale Optimization Algorithm for Global Optimization and UAV 3D Path Planning by Tao Xu, Chaoyue Chen

“…The rapid expansion of unmanned aerial vehicle (UAV) applications in complex environments presents significant challenges in 3D path planning, particularly in overcoming the limitations of traditional methods for dynamic obstacle avoidance and computational efficiency. …”
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Navigating Intelligence: A Survey of Google OR‐Tools and Machine Learning for Global Path Planning in Autonomous Vehicles by Alexandre Benoit, Pedram Asef

“…We offer a new in‐depth investigation of global path planning (GPP) for unmanned ground vehicles, an autonomous mining sampling robot named ROMIE. …”
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Multi-UAV Path Planning for Air-Ground Relay Communication Based on Mix-Greedy MAPPO Algorithm by Yiquan Wang, Yan Cui, Yu Yang, Zhaodong Li, Xing Cui

“…The results show that the Mix-Greedy MAPPO algorithm significantly improves communication probability, reduces energy consumption, avoids no-fly zones, and facilitates exploration compared to other algorithms in the multi-UAV ground communication relay path planning task. After training with the same number of steps, the Mix-Greedy MAPPO algorithm has an average reward score that is 45.9% higher than the MAPPO algorithm and several times higher than the multi-agent soft actor-critic (MASAC) and multi-agent deep deterministic policy gradient (MADDPG) algorithms. …”
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Path planning of intelligent tennis ball picking robot integrating twin network target tracking algorithm by Zegang Wang

“…This method provides an effective solution for target tracking and path planning of intelligent tennis ball picking robots in complex environments and has important practical application value.…”
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An Unmanned Delivery Vehicle Path-Planning Method Based on Point-Graph Joint Embedding and Dual Decoders by Jiale Cheng, Zhiwei Ni, Wentao Liu, Qian Chen, Rui Yan

“…In this scenario, the output of one decoder serves as the label for another, overseeing its learning process to choose the most effective path. Experimental results demonstrate that PGDD reduces total costs by 8.73% on average compared to state-of-the-art algorithms in 100-node scenarios, with performance gains reaching 12.5% for larger-scale problems (400 nodes), validating its superiority in complex path-planning. …”
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Robotic Tack Welding Path and Trajectory Optimization Using an LF-IWOA by Bingqi Jia, Haihong Pan, Lei Zhang, Yifan Yang, Huaxin Chen, Lin Chen

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Enhancing Unmanned Aerial Vehicle Path Planning in Multi-Agent Reinforcement Learning through Adaptive Dimensionality Reduction by Haotian Shi, Zilin Zhao, Jiale Chen, Mengjie Zhou, Yang Liu

“…Unmanned Aerial Vehicles (UAVs) have become increasingly important in various applications, including environmental monitoring, disaster response, and surveillance, due to their flexibility, efficiency, and ability to access hard-to-reach areas. Effective path planning for multiple UAVs exploring a target area is crucial for maximizing coverage and operational efficiency. …”
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Multi-robot path planning in online dynamic obstacle environments based on parallel cooperative strategy optimization algorithm by Chia-Hung Wang, Kun Hu, Xiaojing Wu

“…Abstract The obstacle avoidance path planning for robots has become a critical research focus, especially in the context of addressing complex tasks in dynamic, unstructured environments where unpredictable obstacles and varying conditions present significant challenges. …”
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Path planning in radioactive environment of nuclear facilities based on modified A-star algorithm and search neighborhood optimization by Biao Zhang, Xingfu Cai, Guoqiang Li, Xiaomeng Li, Minjun Peng, Haowei Wang

“…For models of varying complexity, optimizing the number of search neighborhoods further enhances search efficiency without affecting path planning results. …”
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A Computational Framework for Automated Puncture Trajectory Planning in Hemorrhagic Stroke Surgery by Ziyue Ma, Feng Yan, Yongzhi Shan, Yaming Wang, Hong Wang

“…The average angle difference between automatically generated and manually designed paths was 16.36°. …”
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A Bionic Social Learning Strategy Pigeon-Inspired Optimization for Multi-Unmanned Aerial Vehicle Cooperative Path Planning by Yankai Shen, Xinan Liu, Xiao Ma, Hong Du, Long Xin

“…This paper proposes a bionic social learning strategy pigeon-inspired optimization (BSLSPIO) algorithm to tackle cooperative path planning for multiple unmanned aerial vehicles (UAVs) with cooperative detection. …”
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Real-Time 3D Vision-Based Robotic Path Planning for Automated Adhesive Spraying on Lasted Uppers in Footwear Manufacturing by Ya-Yung Huang, Jun-Ting Lai, Hsien-Huang Wu

“…A triangulation-based scanning setup reconstructs each upper into a high-resolution point cloud, enabling customized spraying path planning. A six-axis robotic arm executes the path using an adaptive transformation matrix that aligns with surface normals. …”
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Bi-directional virtual search algorithm for efficient and collision-free path planning in autonomous robots navigating static and dynamic environments by M.D. Yeshwanth Kumar, K. Rajchandar

“…Overall, the proposed framework ensures robust, real-time path planning with minimal computational overhead, making it highly suitable for complex autonomous navigation tasks in uncertain environments.…”
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MPN-RRT*: A New Method in 3D Urban Path Planning for UAV Integrating Deep Learning and Sampling Optimization by Yue Zheng, Ang Li, Zihan Chen, Yapeng Wang, Xu Yang, Sio-Kei Im

“…The increasing deployment of unmanned aerial vehicles (UAVs) in complex urban environments necessitates efficient and reliable path planning algorithms. …”
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UAV Path Planning: A Dual-Population Cooperative Honey Badger Algorithm for Staged Fusion of Multiple Differential Evolutionary Strategies by Xiaojie Tang, Chengfen Jia, Zhengyang He

“…Experimental evaluations on the CEC2017 benchmark suite demonstrate the superiority of LRMHBA over 11 comparison algorithms. In the UAV 3D path planning task, LRMHBA consistently generated the shortest average path across three obstacle simulation scenarios of varying complexity, achieving the highest rank in the Friedman test.…”
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A Hierarchical Path Planning Framework of Plant Protection UAV Based on the Improved D3QN Algorithm and Remote Sensing Image by Haitao Fu, Zheng Li, Jian Lu, Weijian Zhang, Yuxuan Feng, Li Zhu, He Liu, Jian Li

“…Additionally, a dynamic energy consumption model and a progressive composite reward function are incorporated to further optimize UAV path planning in complex farmland conditions. Simulation experiments reveal that in the two-level scenario, the MoE-D3QN algorithm achieves a coverage efficiency of 0.8378, representing an improvement of 37.84–63.38% over traditional algorithms and 19.19–63.38% over conventional reinforcement learning methods. …”
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Enhanced Unmanned Surface Vehicle Path Planning Based on the Pair Barracuda Swarm Optimization Algorithm: Implementation and Performance in Thousand Island Lake by Binghua Shi, Zeyu Liu, Zhou He, Chen Wang, Jia Guo

“…The path planning problem for unmanned surface vehicles (USVs) is related to multiobjective optimization, including shortest path, minimum energy consumption, and obstacle avoidance, making it particularly complex in multi-island and multiobstacle environments such as Thousand Island Lake. …”
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Three-Dimensional Unmanned Aerial Vehicle Path Planning in Simulated Rugged Mountainous Terrain Using Improved Enhanced Snake Optimizer (IESO) by Wuke Li, Kongwen Zhang, Qi Xiong, Xiaoxiao Chen

“…Experimental results show that compared with five commonly used algorithms, the IESO algorithm improves the quality of flight trajectory planning by nearly 30% on average. Particularly when compared to the original SO algorithm, IESO demonstrates performance enhancement exceeding 36%, proving its superiority in UAV path planning over complex terrain.…”
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