ABSTRACT
An optical tactile sensor technique with 3-dimension (3-D) surface reconstruction is proposed for robotic fingers. The hardware of the tactile sensor consists of a surface deformation sensing layer, an image sensor and four individually controlled flashing light emitting diodes (LEDs). The image sensor records the deformation images when the robotic finger touches an object. For each object, four deformation images are taken with the LEDs providing different illumination directions. Before the 3-D reconstruction, the look-up tables are built to map the intensity distribution to the image gradient data. The possible image shadow will be detected and amended. Then the 3-D depth distribution of the object surface can be reconstructed from the 2-D gradient obtained using the look-up tables. The architecture of the tactile sensor and the proposed signal processing flow have been presented in details. A prototype tactile sensor has been built. Both the simulation and experimental results have validated the effectiveness of the proposed 3-D surface reconstruction method for the optical tactile sensors. The proposed 3-D surface reconstruction method has the unique feature of image shadow detection and compensation, which differentiates itself from those in the literature.
ABSTRACT
Land use change and fossil fuel combustion due to urbanization have a significant effect on global carbon cycle and climate change. It's important to have an explicit understanding of the spatial distribution of CO2 to recognize and control GHG emission, which is helpful to reduce human-induced contribution to global climate change. The study area of this project was set in the city of Shanghai with intensive human activity and rapid urbanization. The monitoring of near surface CO2 concentration along 3 transects was conducted across an urban-rural gradient by means of near infrared gas analyzer Li-840A in spring, 2014. Remote sensing data were also used to derive underlying surface information. Further quantitative analysis of the mechanism of CO2 concentration's response to the characteristics of underlying surface was presented in this paper. The results showed that the average near surface CO2 concentration was (443.4±22.0) µmol . mol-1. CO2 concentration in city center was in average 12.5% (52.5 µLmol . mol-1) higher than that in the suburban area. Also, CO2 concentration showed a significant spatial differentiation, with the highest CO2 concentration in the northwest, the second highest in the southwest, and the lowest in the southeast, which was in accordance with the urbanization level of the underlying surface. The results revealed that the vegetation coverage rate (CVeg) was an important indicator to describe near surface CO2 concentration with a negative correlation, and the impervious surface area coverage rate (CISA) had lower explanatory power with a positive correlation. The study also found that the determination coefficient (R2) between CO2 concentration (CCO2) and CISA or CVeg achieved its highest value when the buffer distance was 5 km, and their quantitative relationships be described by a stepwise regression equation: CCO2=0.32CISA-0.89CVeg+445.13 (R2 =0.66, P<0.01).
