Robot Control
Robot Control - Related Videos
09:00
Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect
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Cited by 18 •
2016
The capability to localize an odor source is necessary for insect survival and is expected to be applicable to artificial odor-tracking. The insect-controlled robot is driven by an actual silkmoth and enables us to evaluate the odor-tracking capability of insects through a robotic platform.
05:47
Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
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Cited by 1 •
2025
Here, we present a protocol for the simulation and monitoring of a scaled semi-automated assembly process, through the collaboration of a collaborative robot and verification via a computer vision system for quality control.
11:01
SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
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Cited by 12 •
2015
The overall goal of this method is to establish an SSVEP-based experimental procedure by integrating multiple software programs to enable the study of brain-robot interaction with humanoid robots, which is prospective in assisting the sick and elderly as well as performing unsanitary or dangerous jobs.
08:34
Robot-assisted Partial Splenectomy
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2026
This protocol describes a robot-assisted partial splenectomy for a benign splenic tumor. The robotic approach optimizes spleen preservation by enhancing surgical precision, minimizing blood loss, and improving postoperative outcomes. Key surgical steps include meticulous splenic hilum dissection, selective vascular ligation, intraoperative ultrasonography, and controlled parenchymal transection.
11:21
Robotic Ablation of Atrial Fibrillation
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Cited by 12 •
2015
Pulmonary vein isolation (PVI) with an ablation catheter is a curative treatment for atrial fibrillation (AF). Robotic catheter systems aim to improve catheter steerability. Here, a procedure with a new robotic catheter system is presented. The goal of the procedure is electrical block between pulmonary vein and left atrium.
FAQs
Feedback gives the controller updated information about the robot’s position and surroundings while a task is underway. The system can then adjust motor commands in real time instead of relying only on an initial instruction. This ongoing correction supports more accurate movement and contributes to safe, reliable performance when conditions change.
Neural activity can provide signals that guide artificial movement, allowing a robot or prosthetic system to respond to patterns associated with motor control. In neuroscience research, this connection helps investigators examine how brain activity relates to movement while also developing ways for users to interact with the physical world through assistive technologies.
Programmed goals direct a system through predefined task instructions, whereas neural signals can supply an input associated with the user’s brain activity. Robot control can therefore connect either external programming or neural information to movement. Comparing these inputs helps neuroscience researchers study motor control and explore different routes for controlling artificial limbs or devices.
A typical workflow begins by establishing a movement goal or obtaining a signal source, such as neural activity or another sensor. The controller converts that input into motor commands, directs the robot, and receives information about position and surroundings. It then adjusts the commands in real time, producing a feedback-based cycle for studying movement or assistance.
It is useful when researchers need to connect brain activity with movement produced by an external device. By linking neural patterns to artificial motion, a brain-machine interface can serve as a platform for investigating motor control and interaction with the physical world. The same framework also supports research into assistive technologies that may extend a person’s physical capabilities.
In prosthetic-limb research, robot control helps relate neural activity or sensor information to artificial movement. In neurorehabilitation, it provides a way to study or support movement through controlled interaction with a robotic system. These applications connect engineering with neuroscience by testing how artificial motion can represent, assist, or extend motor behavior.
Researchers can examine how patterns of brain activity correspond to artificial movement, how accurately a system follows a task goal, and how feedback changes its commands. These observations provide information about motor control while also helping evaluate assistive approaches. The resulting systems can support studies of prosthetic movement, brain-machine interfaces, and neurorehabilitation.