Sonar sensor interpretation for ectogeneous robots

We have developed four generations of sonar scanning systems to automatically interpret surrounding environment. The first two are stationary 3D air-coupled ultrasound scanning systems and the last two are packaged as sensor heads for mobile robots. Template matching analysis is applied to distinguish simple indoor objects. It is conducted by comparing the tested echo with the reference echoes. Important features are then extracted and drawn in the phase plane. The computer then analyzes them and gives the best choices of the tested echoes automatically. For cylindrical objects outside, an algorithm has been presented to distinguish trees from smooth circular poles based on analysis of backscattered sonar echoes. The echo data is acquired by a mobile robot which has a 3D air-coupled ultrasound scanning system packaged as the sensor head. Four major steps are conducted. The final Average Asymmetry-Average Squared Euclidean Distance phase plane is segmented to tell a tree from a pole by the location of the data points for the objects interested. For extended objects outside, we successfully distinguished seven objects in the campus by taking a sequence scans along each object, obtaining the corresponding backscatter vs. scan angle plots, forming deformable template matching, extracting interesting feature vectors and then categorizing them in a hyper-plane. We have also successfully taught the robot to distinguish three pairs of objects outside. Multiple scans are conducted at different distances. A two-step feature extraction is conducted based on the amplitude vs. scan angle plots. The final Slope1 vs. Slope2 phase plane not only separates the rectangular objects from the corresponding cylindrical

Morphological surface profile extraction with multiple range sensors

A novel method is described for surface profile extraction based on morphological processing of multiple range sensor data. The approach taken is extremely flexible and robust, in addition to being simple and straightforward. It can deal with arbitrary numbers and configurations of sensors as well as synthetic arrays. The method has the intrinsic ability to suppress spurious readings, crosstalk, and higher-order reflections, and process multiple reflections informatively. The performance of the method is investigated by analyzing its dependence on surface structure and distance, sensor beamwidth, and noise on the time-of-flight measurements