An active sensing strategy to solve the problem of uncertainty identification in robotic contact

• Main Author: Chua, Alvin Y.
• Format: text
• Language: English
• Published: Animo Repository 2000
• Subjects: Robotics; Detectors; Robots > Error detection and recovery; Uncertainty (Information theory); Remote sensing; Remote Sensing
• Online Access: https://animorepository.dlsu.edu.ph/etd_doctoral/883
• Institution: De La Salle University

This study addresses the problem of solving the uncertainties present in a robotic contact situation. The uncertainties are errors, in terms of angles and displacements that inhibit the smooth presentation of a robotic task. A force sensor is used together with Kalman Filters to solve the problem of identifying these uncertainties. However, the straightforward use of a force sensor and the Kalman Filters is found to be effective in finding only some of the uncertainties. There are uncertainties that form dependencies and therefore could not be estimated in a direct manner. It is also observed that the relationship between the uncertainties and the forces is non-linear and therefore, an Extended Kalman Filter (EKF) has been used to find the uncertainties. This dependency brings about the problem of observability. To solve the observable uncertainties in contact situations four new active sensing strategies were tested, namely: random contact strategy, multiple-excitation strategy, combination-excitation strategy, and the diagonalization strategy. The active sensing strategy introduces a matrix into the Kalman filter algorithm to solve for the unobservable robotic contact uncertainties. The transformation matrix is derived through the relationship of a new contact situation and the previous contact situation. The error covariance matrix of the Kalman filter is used to indicate the directions of dependency and accuracy of the values estimated. Among the strategies, it was concluded based on this study that the combination-excitation is the best strategy because it solved all the uncertainties with the least number of contacts. The combination-excitation strategy also mimics the actual situation wherein the peg makes contact with the environment, exciting the uncertainties in different directions (combination of single or multiple excitation), until all uncertainties are identified. A two dimensional contact situation is used to demonstrate the validity of the strategies described above. Experimental results are also presented to prove the validity of the procedure.