Several position identification methods are being used for mobile robots. Dead reckoning is a popular method but due to the error accumulation from wheel slippage, its reliability is low for measurement of long distances especially on uneven surfaces. Another popular method is the landmark method, which estimates current position relative to known landmarks, but the landmark method's limitation is that it cannot be used in an uncharted environment. Thus, this paper proposes a new method called "Cooperative Positioning System (CPS)" that is able to overcome these shortcomings. The main concept of CPS is to divide the robots into two groups, A and B where group A remains stationary and acts as a landmark while group B moves and then group B stops and acts as a landmark for group A. This process is repeated until the target position is reached. Compared with dead reckoning, CPS has a far lower accumulation of positioning errors, and can also work in three dimensions. Furthermore, CPS employs inherent landmarks and therefore can be used in uncharted environments unlike the landmark method. In this paper, we introduce the basic concept of CPS and its positioning principle. Next, we outline a second prototype CPS machine model (CPS-II) and discuss the method of position estimation using the variance of positioning error and weighted least squares method. Position identification experiments using the CPS-II model give a positioning accuracy of 0.12% for position and 0.32 degree for attitude after the robots traveled a distance of 21.5 m.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence