Results 181 to 190 of about 7,724,255 (252)
This work presents a robotic control method for human–robot collaborative assembly based on a biomechanics‐constrained digital human model. Reinforcement learning is used to generate physiologically plausible human motion trajectories, which are integrated into a virtual environment for robot control learning.
Bitao Yao +4 more
wiley +1 more source
A Q-Learning-Enhanced Cuckoo Catfish Optimizer (CCO-RL): A Comparative Study of Nine Metaheuristics Applied to CEC2017, CEC2022 and Engineering Design Problems. [PDF]
Tawil AA +3 more
europepmc +1 more source
Cross‐Scale Hierarchical Targeted Delivery System Based on Small‐Scale Magnetic Robots
This article reviews a cross‐scale hierarchical targeted delivery system that integrates magnetic continuum robots and magnetic microrobots. By combining rapid long‐range navigation with precise microscale targeting, the system overcomes key limitations of single‐scale approaches.
Junjian Zhou +4 more
wiley +1 more source
An energy aware Q-learning framework for comprehensive coverage path planning in unknown complex environments. [PDF]
Xue Y, Tan CK, Wong WP.
europepmc +1 more source
DRIVE‐SAFE evaluates learning‐based, black‐box autonomous driving policies against evolving temporal safety requirements using Signal Temporal Logic robustness metrics. It aggregates distributional robustness measures with domain‐informed weights to guide iterative retraining.
Kristy Sakano +3 more
wiley +1 more source
A Q-learning-based hybrid search algorithm integrating PRM and ACO for 3D UAV path planning. [PDF]
Liu S, Cui Y, Yu Y, Hu W.
europepmc +1 more source
Intelligent Sky Guardians (InSkyGuard) is introduced as a four‐drone swarm that autonomously detects, tracks, and safely captures rogue drones using a coordinated net system. Computer vision and leader–follower control architecture enable synchronized enclosure, while integrated failsafes enhance system reliability. Validated through closed‐environment
Joshua Hastings +6 more
wiley +1 more source
Comparative analysis of deep Q-learning algorithms for object throwing using a robot manipulator. [PDF]
Al Homsi M +3 more
europepmc +1 more source
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao +6 more
wiley +1 more source
A Q-Learning Based Scheme for Neighbor Discovery and Power Control in Marine Opportunistic Networks. [PDF]
Zhang J, Jiang S, Duan J.
europepmc +1 more source

