Localization of a high-speed mobile robot using global features

Abstract

A new localization algorithm is proposed for a fast moving mobile robot, which utilizes only one beacon and the global features of the differential-driving mobile robot. It takes a relatively long time to localize a mobile robot with active beacon sensors, since the distance to the beacon is measured based on the traveling time of the ultrasonic signal. When the mobile robot is moving slowly, the measurement time does not yield a high error. At a higher speed, however, the localization error becomes too large for the mobile robot to be located accurately. Therefore, in high-speed mobile robot operations, instead of using two or more active beacons, only one active beacon and the global features of the mobile robot are used to localize the mobile robot. The two global features are the radius and center of the rotational motion for the differential-driving mobile robot, which generally describe the motion of, and are used for the trace prediction of, a mobile robot. In high-speed operations, the localizer finds the intersection point of this predicted trace and the circle, which is centered at the beacon whose radius is the distance between the mobile robot and the beacon. This new approach overcomes the large localization error caused by the high speed of the mobile robot. The performance of the new localization algorithm is verified through experiments with a high-speed mobile robot.

Sinusoidal input-based visual control for nonholonomic vehicles

“…The velocities defined in Eqs. (3) and (4) have been designed in such a way that the rotational velocity is zero at the points where the linear velocity is either maximum (in absolute value) or zero. Besides, the greatest rotational speeds are associated in our method to intermediate linear velocity values.…”

“…In this section we obtain analytical expressions for the evolution of state variables in a motion planning scenario under the sinusoidal-varying velocity commands (3) and (4). By integrating these sinusoidal inputs over time, we can derive the equations for the evolution of the three state variables.…”

“…Autonomous navigation and control of mobile robots using vision sensors is a widely researched topic. [1][2][3] Although other sensors can be used for control or localization purposes, 4 vision is a highly regarded sensing modality due to its relatively low cost and the rich information it provides. In particular, significant interest has been put in recent years in omnidirectional cameras, 5 which, due to their wide field of view, facilitate navigational tasks.…”

Database based Global Positioning System Correction

“…GPS, IMU, Pseudo-lite와 TLS(Terrestrial Laser Scanning)를 자동차에 모두 장착 하여 위치를 보정하는 연구도 진행되어왔지만 [2] , GPS 수신기만을 장착하는 방법보다 비용도 많이 들고 비효 율적이다. 이 외의 방법으로는 이미지 센서나 LiDAR 센서, Radar 센서 등을 이용하여 위치를 인식하는 방 법도 연구되어왔다 [4][5][6]8] . GPS의 잡음을 제거하는 대표적인 방법은 칼만 필 터 알고리즘을 사용하는 것이다 [7] .…”

Combined visual odometry and visual compass for off-road mobile robots localization

“…These techniques determine the position of the robot with respect to a global reference frame, for instance, using beacons or landmarks. [4][5][6] The most popular technique is Global Positioning System (GPS), which is based on satellite signals to determine the absolute position of an object on the Earth (longitude, latitude, and altitude). 7 The main drawbacks of absolute techniques are as follows: (i) It requires a costly installation of beacons/markers on the area where the robot operates, and (ii) the mobile robot can only navigate over the area in which landmarks are located.…”