The paper presents a robotics-based model for choice reaching experiments on visual attention. In these experiments participants were asked to make rapid reach movements toward a target in an odd-color search task, i.e., reaching for a green square among red squares and vice versa (e.g., Song and Nakayama, 2008). Interestingly these studies found that in a high number of trials movements were initially directed toward a distractor and only later were adjusted toward the target. These "curved" trajectories occurred particularly frequently when the target in the directly preceding trial had a different color (priming effect). Our model is embedded in a closed-loop control of a LEGO robot arm aiming to mimic these reach movements. The model is based on our earlier work which suggests that target selection in visual search is implemented through parallel interactions between competitive and cooperative processes in the brain (Heinke and Humphreys, 2003; Heinke and Backhaus, 2011). To link this model with the control of the robot arm we implemented a topological representation of movement parameters following the dynamic field theory (Erlhagen and Schoener, 2002). The robot arm is able to mimic the results of the odd-color search task including the priming effect and also generates human-like trajectories with a bell-shaped velocity profile. Theoretical implications and predictions are discussed in the paper.
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http://dx.doi.org/10.3389/fpsyg.2012.00105 | DOI Listing |
Front Robot AI
January 2025
School of Metallurgy and Materials, University of Birmingham, Birmingham, United Kingdom.
Introduction: The transition to electric vehicles (EVs) has highlighted the need for efficient diagnostic methods to assess the state of health (SoH) of lithium-ion batteries (LIBs) at the end of their life cycle. Electrochemical Impedance Spectroscopy (EIS) offers a non-invasive technique for determining battery degradation. However, automating this process in industrial settings remains a challenge.
View Article and Find Full Text PDFMed Phys
January 2025
Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Background: Histotripsy is a non-invasive, non-ionizing, non-thermal focused ultrasound technique. High amplitude short acoustic pulses converge to create high negative pressures that cavitate endogenous gas into a bubble cloud leading to mechanical tissue destruction. In the United States, histotripsy is approved to treat liver tumors under diagnostic ultrasound guidance but in initial clinical cases, some areas of the liver have not been treated due to bone or gas obstructing the acoustic window for targeting.
View Article and Find Full Text PDFSci Rep
January 2025
Zhongyuan University of Technology, Zhengzhou, 450007, China.
This paper studies the practical prescribed-time control problem for dual-arm robots handling an object with output constraints. Firstly, by utilizing the property that the sum of internal forces in the grasping space is zero, the system model is obtained and decomposed into the contact force model and free motion model, which are orthogonal to each other. Furthermore, by combining the performance function and constraint function, the original system tracking error is transformed to a new one, whose boundedness can ensure that the original system variable converges to the predetermined range within the specified time.
View Article and Find Full Text PDFJ Shoulder Elbow Surg
January 2025
Roth | McFarlane Hand & Upper Limb Center, St Joseph's Health Care London, London, ON, Canada.
Background: Precise and accurate glenoid preparation is important for the success of shoulder arthroplasty. Despite advancements in preoperative planning software and enabling technologies, most surgeons execute the procedure manually. Patient-specific instrumentation (PSI) facilitates accurate glenoid guide pin placement for cannulated reaming; however, few commercially available systems offer depth of reaming control.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing Institute of Agricultural Mechanization, Nanjing 210014, China.
To address several challenges, including low efficiency, significant damage, and high costs, associated with the manual harvesting of , in this study, a machine vision-based intelligent harvesting device was designed according to its agronomic characteristics and morphological features. This device mainly comprised a frame, camera, truss-type robotic arm, flexible manipulator, and control system. The FES-YOLOv5s deep learning target detection model was used to accurately identify and locate .
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