Results 221 to 230 of about 79,894 (308)

USV Trajectory Tracking Control Based on Receding Horizon Reinforcement Learning. [PDF]

Sensors (Basel)
Wen Y, Chen Y, Guo X.
europepmc   +1 more source

Consensus based SoC trajectory tracking control design for economic-dispatched distributed battery energy storage system. [PDF]

PLoS One, 2020
Ullah S, Khan L, Badar R, Ullah A, Karam FW, Khan ZA, Rehman AU.   +6 more
europepmc   +1 more source

Improving the Robustness of Visual Teach‐and‐Repeat Navigation Using Drift Error Correction and Event‐Based Vision for Low‐Light Environments

Advanced Robotics Research, EarlyView.
Visual teach‐and‐repeat (VTR) navigation allows robots to learn and follow routes without building a full metric map. We show that navigation accuracy for VTR can be improved by integrating a topological map with error‐drift correction based on stereo vision.
Fuhai Ling, Ze Huang, Tony J. Prescott
wiley   +1 more source

Trajectory Tracking Control of Variable Sweep Aircraft Based on Reinforcement Learning. [PDF]

Biomimetics (Basel)
Cao R, Lu K.
europepmc   +1 more source

Trajectory tracking control for autonomous vehicles

Trajectory tracking control is an essential technology for achieving intelligent driving in autonomous vehicles, involving both longitudinal speed tracking and lateral path tracking.
Shou, Hanpu
core  

Soft Actuators Integrated with Control and Power Units: Approaching Wireless Autonomous Soft Robots

Advanced Robotics Research, EarlyView.
Soft robots exhibit significant development potential in various applications. However, there are still key technical challenges regarding material improvement, structure design and components integration. This review focuses on the development and challenge of soft actuators, power components, and control components in untethered intelligent soft ...
Renwu Shi, Feifei Pan, Xiaobin Ji
wiley   +1 more source

Identifying Physical Interactions in Contact‐Based Robot Manipulation for Learning from Demonstration

Advanced Robotics Research, EarlyView.
Robots can learn manipulation tasks from human demonstrations. This work proposes a versatile method to identify the physical interactions that occur in a demonstration, such as sequences of different contacts and interactions with mechanical constraints.
Alex Harm Gert‐Jan Overbeek, Herman van der Kooij, Douwe Dresscher, Mark Vlutters   +3 more
wiley   +1 more source

Motion/force coordinated trajectory tracking control of nonholonomic wheeled mobile robot via LMPC-AISMC strategy. [PDF]

Sci Rep
Tang M, Tang K, Zhang Y, Qiu J, Chen X.
europepmc   +1 more source

Multimodal Locomotion in Insect‐Inspired Microrobots: A Review of Strategies for Aerial, Surface, Aquatic, and Interfacial Motion

Advanced Robotics Research, EarlyView.
This review identifies key design considerations for insect‐inspired microrobots capable of multimodal locomotion. To draw inspiration, biological and robotic strategies for moving in air, on water surfaces, and underwater are examined, along with approaches for crossing the air–water interface.
Mija Jovchevska, Kaushik Jayaram, Nicole Xu   +2 more
wiley   +1 more source

Asymmetry in Skipping Enhances Viability Against Control Input Noise

Advanced Robotics Research, EarlyView.
Quadruped animals use asymmetric galloping gaits at high speeds, yet the functional role of this asymmetry remains unclear. This study shows that left–right asymmetry in touchdown angles enhances robustness to control noise. Using a simple two‐legged locomotion model and viability theory, it demonstrates that asymmetric skipping substantially enlarges ...
Yuichi Ambe, Alvin So, Shinya Aoi
wiley   +1 more source