Research on dead-reckoning based localization for cleaning robot

Abstract

The cleaning robot can perform cleaning tasks autonomously and intelligently, thus saving people from boring and heavy physical labour. Autonomous positioning is one of the indicators for intelligent level of cleaning robots. In this paper, dead-reckoning method based on encoder is adopted. The kinematics model of the robot is established to optimize the existing cleaning robot positioning method by using arc model. The positioning effect of the cleaning robot is tested by experiment. Through experiments, it is found that there are two main problems in the estimation of the track. One is that during the turning process, the calculation of the angle is affected by the slip, which causes a large deviation. Another is that the displacement deviation of linear motion gradually increases with the increase of the driving distance due to system error. Based on the above analysis, a positioning scheme is proposed. Gyro sensor and RFID tag are introduced to correct the angular deviation and assist positioning and improve the positioning accuracy of the cleaning robot.

Time-space consistency strategy for live-virtual-constructive (LVC) integrated simulation environment

“…In addition, due to the network transmission delay [2] and data packet loss [3], the result message may occur before the cause message, causing adverse effects such as inversion of causality and ambiguity of understanding, which will seriously damage the normal operation of the LVC system. In response to the above, many strategies or methods have been proposed to ensure the time-space consistency of simulation systems, such as interest-domain based time synchronization strategy [4], support vector machine based space coordinate transformation method [5], lifetime limited causal order control method [6], and various dead reckoning (DR) algorithms for state prediction [7][8][9]. These…”