Results 171 to 180 of about 993,417 (294)

Multi-robot collision avoidance method in sweet potato fields. [PDF]

Front Plant Sci
Xu K, Xing J, Sun W, Xu P, Yang R.
europepmc   +1 more source

Extending Battery Usage Time of a Heavy‐Duty Mecanum‐Wheeled Autonomous Electric Vehicle Used in Iron–Steel Industry by Considering Wheel Slippage

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
It is a fact that slippage causes tracking errors in both longitudinal and lateral directions which results to have less travel distance in tracking a reference trajectory. Less travel distance means having energy loss of the battery and carrying loads less than planned.
Gokhan Bayar, Goktug Hambarci, Ali Sezai Demirel   +2 more
wiley   +1 more source

Correction: Novel deep reinforcement learning based collision avoidance approach for path planning of robots in unknown environment. [PDF]

PLoS One
Alharthi R, Noreen I, Khan A, Aljrees T, Ali Z, Innab N.   +5 more
europepmc   +1 more source

Bubbles Acting as Micro End‐Effectors for Dexterous Manipulation and Sensing in Aqueous Environment

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Inspired by bubbles, this article proposes a low‐cost method for multifunctional manipulation and sensing using microbubbles in aqueous environments. Bubbles are easily generated in situ, enabling the safe and adaptive handling of microobjects and sensing of microforces and surface textures.
Zichen Xu, Qingsong Xu
wiley   +1 more source

Analysis of collision avoidance in honeybee flight. [PDF]

J R Soc Interface
Singh S, Garratt M, Srinivasan M, Ravi S.   +3 more
europepmc   +1 more source

Collision Avoidance and Routing based on Location Access (CARLA) of mobile robots

, 2023
El-sayyad S, Saleh A, Ali H, Rabie A, Saraya M, Abdelsalam M.   +5 more
europepmc   +1 more source

Predicting Performance of Hall Effect Ion Source Using Machine Learning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source

Model predictive path integral for decentralized multi-agent collision avoidance. [PDF]

PeerJ Comput Sci
Dergachev S, Yakovlev K.
europepmc   +1 more source

Roadmap on Artificial Intelligence‐Augmented Additive Manufacturing

Advanced Intelligent Systems, EarlyView.
This Roadmap outlines the transformative role of artificial intelligence‐augmented additive manufacturing, highlighting advances in design, monitoring, and product development. By integrating tools such as generative design, computer vision, digital twins, and closed‐loop control, it presents pathways toward smart, scalable, and autonomous additive ...
Ali Zolfagharian, Liuchao Jin, Qi Ge, Wei‐Hsin Liao, Andrés Díaz Lantada, Francisco Franco Martínez, Tianyu Zhang, Tao Liu, Charlie C. L. Wang, Mohammad Hossein Mosallanejad, Reza Ghanavati, Abdollah Saboori, Alejandro De Blas De Miguel, William Solórzano‐Requejo, Yi Cai, Xiangyang Dong, Huangyi Qu, Najmeh Samadiani, Guangyan Huang, Austin Downey, Yanzhou Fu, Lang Yuan, Tsz‐Kwan (Glory) Lee, Arbind Agrahari Baniya, Eisha Waseem, Abdul Rahman Sani, Abbas Z. Kouzani, Yijia Wu, Markus P. Nemitz, Masoud Shirzad, Dageon Oh, Seung Yun Nam, Amedeo Franco Bonatti, Irene Chiesa, Gabriele Maria Fortunato, Giovanni Vozzi, Carmelo De Maria, Mahdi Bodaghi   +37 more
wiley   +1 more source

Collision Avoidance Path Planning for Automated Vehicles Using Prediction Information and Artificial Potential Field. [PDF]

Sensors (Basel)
Ahn S, Oh T, Yoo J.
europepmc   +1 more source