Localization and Navigation for Autonomous Mobile Robots using Petri Nets in Indoor Environments
; Rocha, M.
Rodrigues, J. R.
; Albuquerque, V.
; Alexandria, A.
IEEE Access Vol. 6, Nº 1, pp. 31665 - 31676, December, 2018.
ISSN (print): 2169-3536
Journal Impact Factor: (in )
Digital Object Identifier: 10.1109/ACCESS.2018.2846554
In this paper, mobile robotics and present tools used in localization, mapping, and navigation of a mobile robot are discussed. The main purpose of this paper is, given a map represented by the incidence matrix of a petri net (PN), to evaluate the use of radio-frequency identification (RFID) technology to recognize the position of a robot in this map as well as the use of the PN dynamics as the cognition system of this robot. Thus, cards with RFID technology were placed at each intersection of structured environment (labyrinth) ways. A robot equipped with an RFID reader at its bottom moves until it passes over these cards. When this happens, the vehicle performs actions, such as turning right or left according to the map defined in its algorithm. Once the above-mentioned actions are performed, it goes straight to the next card. To ensure that this happens, there is a black line connecting each card to its neighbor cards. The robot is equipped with three infrared sensors, so it can detect and follow these lines. The results show that the robot can get out of one RFID card and reach the next one since they are connected by a black line. Without these lines, due to the limitations in the structure of the robot, it loses its way and cannot return. Still, the robot is able to execute the necessary navigation movements, in the case of stopping, moving forward, and turning right and left. These movements are correctly coordinated by the PN dynamics. The robot knows which card it is on and goes to the next card according to the previously established map. Each path of the robot is mathematically modeled by the incidence matrix of a petri net. Therefore, it managed to reach the destination in each of the four proposed paths. The PN that represents path 1 has the same number of places and transitions and has curves only to the left. On path 2, a right turn was added. In path 3, besides the two-way curves, RP has more places than transitions. Lastly, in path 4, there is a crossing in the path, that is, the robot goes through the same card twice, making the right and different decisions in each case according to the respective maps. Experimental results show that this approach has feasibility and effectiveness.