Determining optimum assembly zone for modular reconfigurable robots using multi-objective genetic algorithm by Ravikiran Pasumarthi, S. M. Bhagya P. Samarakoon, Mohan Rajesh Elara, Bing J. Sheu

“…Utilizing the A* algorithm for path planning ensures efficient navigation. A generic kinematic model enabling holonomic locomotion with any reconfiguration and a new modular robot design are also introduced. …”
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Visual–Inertial Autonomous UAV Navigation in Complex Illumination and Highly Cluttered Under-Canopy Environments by Leyang Zhao, Weixi Wang, Qizhi He, Li Yan, Xiaoming Li

“…Furthermore, proposes a boundary sampling autonomous exploration algorithm and an advanced Rapidly exploring Random Tree (RRT) path planning algorithm. The objective is to enhance the reliability and safety of UAV operations beneath the forest canopy, thereby establishing a technical foundation for surveying vertically stratified natural resources.…”
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Five-Axis Tool Path Generation of Injection Mold Represented by T-Spline Surface by Ce Shang, Hongyao Shen, Jianzhong Fu, Yangfan Sun, Shuhua Yue, Jianfeng Zhang

“…And, its patch layout information can be utilized for tool path planning. We propose an algorithm to determine the patch processing order and generate nonretraction tool path for T-spline surface models. …”
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Research on Collision Avoidance Methods for Unmanned Surface Vehicles Based on Boundary Potential Field by Yongzheng Li, Panpan Hou, Chen Cheng, Biwei Wang

“…Artificial potential field (APF) methods, with their strong adaptability and simplicity of implementation, are widely used in USV path planning tasks. However, the naive APF method struggles in static complex environments, due to the local minima problem. …”
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A Brain-Computer Interface for Teleoperation of a Semiautonomous Mobile Robotic Assistive System Using SLAM by Vidya Nandikolla, Bryan Ghoslin, Kevin Matsuno, Daniel A. Medina Portilla

“…The mobile base’s SLAM algorithm has obstacle avoidance capability and path planning to assist the robot maneuver safely. The robot arm calculates and deploys the necessary joint movement to pick up or drop off a desired object selected by the user via a brainwave controlled cursor on a camera feed. …”
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Global and Local Awareness: Combine Reinforcement Learning and Model-Based Control for Collision Avoidance by Luman Zhao, Guoyuan Li, Houxiang Zhang

“…The proposed approach combines global path planning based on deep reinforcement learning (DRL) and local motion control to improve computational efficiency and alleviate the sensitivity to heading angle changes. …”
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Robust reinforcement learning algorithm based on pigeon-inspired optimization by Mingying ZHANG, Bing HUA, Yuguang ZHANG, Haidong LI, Mohong ZHENG

“…Reinforcement learning(RL) is an artificial intelligence algorithm with the advantages of clear calculation logic and easy expansion of the model.Through interacting with the environment and maximizing value functions on the premise of obtaining little or no prior information, RL can optimize the performance of strategies and effectively reduce the complexity caused by physical models .The RL algorithm based on strategy gradient has been successfully applied in many fields such as intelligent image recognition, robot control and path planning for automatic driving.However, the highly sampling-dependent characteristics of RL determine that the training process needs a large number of samples to converge, and the accuracy of decision making is easily affected by slight interference that does not match with the simulation environment.Especially when RL is applied to the control field, it is difficult to prove the stability of the algorithm because the convergence of the algorithm cannot be guaranteed.Considering that swarm intelligence algorithm can solve complex problems through group cooperation and has the characteristics of self-organization and strong stability, it is an effective way to be used for improving the stability of RL model.The pigeon-inspired optimization algorithm in swarm intelligence was combined to improve RL based on strategy gradient.A RL algorithm based on pigeon-inspired optimization was proposed to solve the strategy gradient in order to maximize long-term future rewards.Adaptive function of pigeon-inspired optimization algorithm and RL were combined to estimate the advantages and disadvantages of strategies, avoid solving into an infinite loop, and improve the stability of the algorithm.A nonlinear two-wheel inverted pendulum robot control system was selected for simulation verification.The simulation results show that the RL algorithm based on pigeon-inspired optimization can improve the robustness of the system, reduce the computational cost, and reduce the algorithm’s dependence on the sample database.…”
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Modelling and Simulation of Distributed UAV Swarm Cooperative Planning and Perception by Haifeng Ling, Hongchuan Luo, Haisong Chen, Linyuan Bai, Tao Zhu, Yanjun Wang

“…To address the mentioned issues, this paper models the problem and proposes a simulation platform for distributed swarm cooperative perception, which addresses software engineering concerns and provides a set of extendable functionalities of a cooperative swarm, including communication, estimation, perception fusion, and path planning. The applicability of the proposed platform is verified by simulations with the real-world application. …”
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Modelling and Analysis of Autonomous Airport Surface Movement Operations based on Multi-Agent Planning by Malte von der Burg, Alexei Sharpanskykh

“…To compute conflict-free trajectories for all taxiing agents, we tailored and extended state-of-the-art multi-agent motion planning algorithms: the two-level routing algorithm combines Priority-Based Search (PBS) with Safe Interval Path Planning (SIPP). We defined different sizes of aircraft, accounted for a minimal safety distance between them, and calibrated their speed limits in curves with historic ADS-B data. …”
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Data Fusion Applied to the Leader-Based Bat Algorithm to Improve the Localization of Mobile Robots by Wolmar Araujo-Neto, Leonardo Rocha Olivi, Daniel Khede Dourado Villa, Mário Sarcinelli-Filho

“…The increasing demand for autonomous mobile robots in complex environments calls for efficient path-planning algorithms. Bio-inspired algorithms effectively address intricate optimization challenges, but their computational cost increases with the number of particles, which is great when implementing algorithms of high accuracy. …”
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Deep Reinforcement Learning Algorithm with Long Short-Term Memory Network for Optimizing Unmanned Aerial Vehicle Information Transmission by Yufei He, Ruiqi Hu, Kewei Liang, Yonghong Liu, Zhiyuan Zhou

“…Our approach accounts for the specific flight constraints of fixed-wing UAVs and employs a continuous policy network to facilitate real-time flight path planning. A non-sparse reward function is designed to maximize data collection from internet of things (IoT) devices, thus guiding the UAV to optimize its operational efficiency. …”
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The Prediction Method and Application of Off-Road Mobility for Ground Vehicles: A Review by Chen Hua, Wencheng Zhang, Hanghao Fu, Yuhao Zhang, Biao Yu, Chunmao Jiang, Yuliang Wei, Ziyu Chen, Xinkai Kuang

“…Finally, we address several existing challenges in current mobility prediction methods and propose exploratory research directions focusing on key technologies and applications, such as real-time mobility prediction, terrain perception, path planning on deformable terrain, and autonomous mobility prediction for unmanned systems. …”
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Mobile robot navigation path algorithm in 3d industrial internet of thing (iot) environment based on 5g mobile communication by Pei Ping, Yu. N. Petrenko

“…In this paper, the research focus on 3-D mobile robot tracking in 5G wireless communication combine to sensor network and mobile robot path planning. The mobile robot can move fast in the three-dimensional (3-D) indoor industrial environment for many tasks such as the monitoring of possible damages on industrial plants [3], data gathering and transmission from wireless communication [4], transportation [5].…”
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Driver Route Planning Method Based on Accident Risk Cost Prediction by Xiaoleng Liao, Tong Zhou, Xu Wang, Rongjian Dai, Xuehui Chen, Xiangmin Zhu

“…Three candidate paths were calculated by using the path planning algorithm proposed in this study; the total risk cost is 6.19, 6.26, and 6.39, respectively; and the total travel time is 29, 29, and 31, respectively. …”
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Single-View Depth Estimation: Advancing 3D Scene Interpretation With One Lens by Kavitha Dhanushkodi, Akila Bala, Neelam Chaplot

“…Its versatility makes it particularly valuable for real-world applications like autonomous navigation, where accurate depth perception is essential for tasks such as obstacle avoidance and path planning, and augmented reality, where it enhances the interaction between virtual and physical objects. …”
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Inverse kinematics solution and control method of 6-degree-of-freedom manipulator based on deep reinforcement learning by Chengyi Zhao, Yimin Wei, Junfeng Xiao, Yong Sun, Dongxing Zhang, Qiuquan Guo, Jun Yang

“…This algorithm also has the advantages of path planning, intelligent obstacle avoidance, and other advantages in dynamically processing complex environmental scenes.…”
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UAV Collision Avoidance in Unknown Scenarios with Causal Representation Disentanglement by Zhun Fan, Zihao Xia, Che Lin, Gaofei Han, Wenji Li, Dongliang Wang, Yindong Chen, Zhifeng Hao, Ruichu Cai, Jiafan Zhuang

“…Deep reinforcement learning (DRL) has significantly advanced online path planning for unmanned aerial vehicles (UAVs). Nonetheless, DRL-based methods often encounter reduced performance when dealing with unfamiliar scenarios. …”
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Trajectory Tracking for 3-Wheeled Independent Drive and Steering Mobile Robot Based on Dynamic Model Predictive Control by Chaobin Xu, Xingyu Zhou, Rupeng Chen, Bazhou Li, Wenhao He, Yang Li, Fangping Ye

“…The A* algorithm with a non-point mass model is used for path planning, enabling the robot to navigate quickly in narrow and constrained environments. …”
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A spatiotemporal distribution prediction model for electric vehicles charging load in transportation power coupled network by Xiaolong Yang, Jingwen Yun, Shuai Zhou, Tek Tjing Lie, Jieping Han, Xiaomin Xu, Qian Wang, Zeqi Ge

“…The present study proposes a spatio-temporal distribution prediction model for EV charging loads in transportation-power coupled network (TPCN). First, path planning is performed separately using the Dijkstra algorithm and refined origin-destination (OD) probability matrix based on the travel characteristics of various vehicle types. …”
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Obstacle avoidance and formation control of multiple unmanned vehicles in complex environments based on artificial potential field method by Yilin MEI, Likun CUI, Xueyan HU, Guangqi HU, Hao WANG

“…The method also incorporated the velocity repulsive potential field for dynamic obstacles and an attraction potential field for sparse obstacles, enhancing the success rate of obstacle avoidance and path planning in complex environments. Compared to traditional artificial potential field methods and the improved algorithms, the simulation results show that the proposed method effectively maintains formation stability and exhibits high anti-interference capabilities in complex environments. …”
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