Abstract
This paper proposed an algorithm to track the obstacle position and avoid the moving objects for differential driving Automatic Guided Vehicles (AGV) system in industrial environment. This algorithm has several abilities such as: to detect the moving objects, to predict the velocity and direction of moving objects, to predict the collision possibility and to plan the avoidance maneuver. For sensing the local environment and positioning, the laser measurement system LMS-151 and laser navigation system NAV-200 are applied. Based on the measurement results of the sensors, the stationary and moving obstacles are detected and the collision possibility is calculated. The velocity and direction of the obstacle are predicted using Kalman filter algorithm. Collision possibility, time, and position can be calculated by comparing the AGV movement and obstacle prediction result obtained by Kalman filter. Finally the avoidance maneuver using the well known tangent Bug algorithm is decided based on the calculation data. The effectiveness of proposed algorithm is verified using simulation and experiment. Several examples of experiment conditions are presented using stationary obstacle, and moving obstacles. The simulation and experiment results show that the AGV can detect and avoid the obstacles successfully in all experimental condition. [Keywords — Obstacle avoidance, AGV, differential drive, laser measurement system, laser navigation system ] .
Highlights
Automatic Guided Vehicles (AGV) is common equipment to transport the materials in production process
The second one is to forecast the relative motion of object to AGV, and adapt reasonable avoiding policy according to the anticipated collision time and position such as in [5,6,7], which is more effective for quick motion of II
This paper proposed an algorithm to detect the environment quickly and reasonably to avoid the stationary and moving obstacles in factory environment such as human moving for differential AGV system
Summary
Automatic Guided Vehicles (AGV) is common equipment to transport the materials in production process. There are two ways to deal with moving object in the obstacle avoiding process, the first one does not consider the motion of object, but improve the motion planning ability and responding ability of AGV such as Artificial Potential Field [1,2] and grid algorithm [3,4]. Those algorithms need computation time to replan the trajectory and only work in known environment.
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