In mobile robot navigation, the landmarks that provide the relative relationship between the environment and the mobile robot are useful in correcting the accumulated error of dead-reckoning system, and this necessitates the use of various landmarks. Although artificial landmarks provide efficiency and stability in this regard, they seem to be more restrictive than natural landmarks in the light of autonomy of a mobile robot. In recognizing natural landmarks visual sensors have been used mostly and not much work has been done on utilizing ultrasonic sensors which have some desirable characteristics beside its low cost. The capability to measure distances directly and low power consumption are few of them. However, straightforward application of the ultrasonic sensors has been hampered by the two notorious obstacles of wide beamwidth and specular reflection. Because of these two effects ambiguity arises in interpreting a single ultrasonic measurement. Numerous approaches have been proposed to overcome this difficulty by integrating many data either obtained from multiple measurements or multiple sensors.
In this thesis, a new approach is proposed to recognize multiple acoustic features with relatively less constraints on their locations and with fewer measurements than previous works. The work of this thesis is composed of two parts: the recognition of acoustic landmarks and its application to mobile robot navigation through self-localization using the acoustic landmarks.
In this work, we propose a method to extract multiple acoustic landmarks for the indoor navigation of a mobile robot. The environment is modeled by specular vertical walls. An ultrasonic sensor mounted on the mobile robot scans around the surrounding walls and detects multiple echo pulses. On the horizontal plane at the height of the ultrasonic sensor from the floor, the two dimensional environment is described by the distance as a function of angle in the scan. Due to the specularity of the walls, the distance function has manageable number of extremum points which represent the retroreflective parts(RPs) that can be used as landmarks. By virtue of the constancy of the relative positions between the ultrasonic sensor and the walls in scanning, the echo pulses reflected from a certain RP have the same times-of-flight in ultrasonic scan data. The position of an RP is determined from the collection of the echo pulses that have the same times-of-flight. The direction to an RP is estimated from the orientation of the ultrasonic sensor at which the maximum magnitude is obtained for the group of echo pulses that correspond to the RP. The mean time-of-flight of the group of echo pulses provides the distance to the RP. Some experimental results show that the multiple RPs are recognized and located with fair accuracy.
As the positions of some RPs are invariable with the location of ultrasonic sensor, they can be used as acoustic landmarks(ALMs) to localize a mobile robot. Within the region in which the ALMs can be recognized, the localization is performed in two steps. Firstly the RPs are located with respect to the unknown current position. Secondly the current location is estimated by maximally matching the locational pattern of the ALMs and the newly acquired RPs.
The path of a mobile robot is defined as a network of this localizable regions(LRs). If the distance between any two adjacent LRs is not too large, a mobile robot can navigate from one LR to the other LR based on dead-reckoning. As the mobile robot can find its current position relative to the reference frame of one LR, the position error made in dead-reckoning motion between the LRs can be corrected. Therefore long range navigation can be successfully accomplished without accumulated errors in following the given path. The experimental iterative run of our mobile robot system shows the validity of the proposed method.