robotic sensors
Recently Published Documents


TOTAL DOCUMENTS

50
(FIVE YEARS 4)

H-INDEX

10
(FIVE YEARS 0)

2021 ◽  
Vol 1 (1) ◽  
pp. 2-24
Author(s):  
Lorenzo Domarco Silva ◽  
Christiane M. Schweitzer ◽  
Elerson Gaetti Jardim Júnior


2021 ◽  
Vol 205 ◽  
pp. 107206
Author(s):  
Anatoly Lisnianski ◽  
Evgeniy Levit ◽  
Lina Teper


2021 ◽  
pp. 31-62
Author(s):  
Alexandre Escolà ◽  
Fernando Auat Cheein ◽  
Joan R. Rosell-Polo


Author(s):  
Thomas Georgethuruthel ◽  
Josie Hughes ◽  
Antonia Georgopoulou ◽  
Frank Clemens ◽  
Fumiya Iida


Robotica ◽  
2020 ◽  
pp. 1-23
Author(s):  
Linh Nguyen ◽  
Sarath Kodagoda ◽  
Ravindra Ranasinghe ◽  
Gamini Dissanayake

SUMMARY This paper addresses the issue of monitoring spatial environmental phenomena of interest utilizing information collected by a network of mobile, wireless, and noisy sensors that can take discrete measurements as they navigate through the environment. It is proposed to employ Gaussian Markov random field (GMRF) represented on an irregular discrete lattice by using the stochastic partial differential equations method to model the physical spatial field. It then derives a GMRF-based approach to effectively predict the field at unmeasured locations, given available observations, in both centralized and distributed manners. Furthermore, a novel but efficient optimality criterion is then proposed to design centralized and distributed adaptive sampling strategies for the mobile robotic sensors to find the most informative sampling paths in taking future measurements. By taking advantage of conditional independence property in the GMRF, the adaptive sampling optimization problem is proven to be resolved in a deterministic time. The effectiveness of the proposed approach is compared and demonstrated using pre-published data sets with appealing results.





2020 ◽  
Vol 67 ◽  
pp. 327-374 ◽  
Author(s):  
Jesse Thomason ◽  
Aishwarya Padmakumar ◽  
Jivko Sinapov ◽  
Nick Walker ◽  
Yuqian Jiang ◽  
...  

In this work, we present methods for using human-robot dialog to improve language understanding for a mobile robot agent. The agent parses natural language to underlying semantic meanings and uses robotic sensors to create multi-modal models of perceptual concepts like red and heavy. The agent can be used for showing navigation routes, delivering objects to people, and relocating objects from one location to another. We use dialog clari_cation questions both to understand commands and to generate additional parsing training data. The agent employs opportunistic active learning to select questions about how words relate to objects, improving its understanding of perceptual concepts. We evaluated this agent on Amazon Mechanical Turk. After training on data induced from conversations, the agent reduced the number of dialog questions it asked while receiving higher usability ratings. Additionally, we demonstrated the agent on a robotic platform, where it learned new perceptual concepts on the y while completing a real-world task.



Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1382 ◽  
Author(s):  
David Saucier ◽  
Samaneh Davarzani ◽  
Alana Turner ◽  
Tony Luczak ◽  
Phuoc Nguyen ◽  
...  

The purpose of this study was to use 3D motion capture and stretchable soft robotic sensors (SRS) to collect foot-ankle movement on participants performing walking gait cycles on flat and sloped surfaces. The primary aim was to assess differences between 3D motion capture and a new SRS-based wearable solution. Given the complex nature of using a linear solution to accurately quantify the movement of triaxial joints during a dynamic gait movement, 20 participants performing multiple walking trials were measured. The participant gait data was then upscaled (for the SRS), time-aligned (based on right heel strikes), and smoothed using filtering methods. A multivariate linear model was developed to assess goodness-of-fit based on mean absolute error (MAE; 1.54), root mean square error (RMSE; 1.96), and absolute R2 (R2; 0.854). Two and three SRS combinations were evaluated to determine if similar fit scores could be achieved using fewer sensors. Inversion (based on MAE and RMSE) and plantar flexion (based on R2) sensor removal provided second-best fit scores. Given that the scores indicate a high level of fit, with further development, an SRS-based wearable solution has the potential to measure motion during gait- based tasks with the accuracy of a 3D motion capture system.



2019 ◽  
Vol 6 (3) ◽  
pp. 604-611 ◽  
Author(s):  
Moran Amit ◽  
Rupesh K. Mishra ◽  
Quyen Hoang ◽  
Aida Martin Galan ◽  
Joseph Wang ◽  
...  

This work combined pressure and chemical sensors on disposable, retrofitting gloves to enable simultaneous tactile sensing and pesticide detection in a point-of-use robotic platform.



Sign in / Sign up

Export Citation Format

Share Document