Results 171 to 180 of about 14,605 (264)

Visual and Visual‐Inertial SLAM Based on Enhanced Deep Learning Features and Motion Smoothness Constraints

open access: yesAdvanced Intelligent Systems, EarlyView.
A visual and visual‐inertial simultaneous localization and mapping (SLAM) algorithm, leveraging enhanced deep learning features and motion smoothness constraints, is proposed in this research work. This method retains the advantages of geometry‐based SLAM methods while effectively utilizing the powerful representational capabilities of data‐driven ...
Maosheng Jiang   +3 more
wiley   +1 more source

Towards Advanced Intelligent and Perceptive Soft Grippers

open access: yesAdvanced Intelligent Systems, EarlyView.
Implementing soft yet strong and intelligent soft grippers request innovative and creative solutions in designing soft bodies and seamlessly integrating actuated systems with hierarchical sensing. This review systematically analyses soft grippers with a deep understanding of core components, from fundamental design principles to actuation and sensing ...
Haneul Kim   +4 more
wiley   +1 more source

Pre‐Curved Everting Robots With Embedded Steering Intelligence Fabricated by CO2 Laser Welding

open access: yesAdvanced Intelligent Systems, EarlyView.
Design and experimental demonstration of a laser welded growing robot for anatomically guided navigation. The robot follows an aortic arch phantom entering the branchiocephalic branch through steering by design. The figure shows the physical phantom setup, CAD defined weld geometry and full robot eversion.
Brandon Saldarriaga   +5 more
wiley   +1 more source

Design‐for‐Benchmarking in Soft Robotics: Navigating Component‐System Dichotomy

open access: yesAdvanced Intelligent Systems, EarlyView.
Soft robotics faces a profound evaluation challenge: the Component‐System Dichotomy, where isolated component tests fail to predict integrated performance. This article presents a systematic survey of critical reporting gaps across actuation, sensing, and control.
Matteo Lo Preti   +4 more
wiley   +1 more source

Driver Behavior Modeling with Subjective Risk‐Driven Inverse Reinforcement Learning

open access: yesAdvanced Intelligent Systems, EarlyView.
A subjective risk‐driven inverse reinforcement learning framework is proposed to model driver decision‐making. It infers drivers' risk perception and risk tolerance from driving data. A learnable risk threshold is used to regulate decisions, enabling interpretable and human‐like driving behavior decisions.
Yang Liang   +6 more
wiley   +1 more source

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