Path planning and trajectory generation using multi-rate predictive Artificial Potential Fields

In this paper, a multi-rate predictive artificial potential field method for dynamic and uncertain environments is described. It is based on the combination of classical Artificial Potential Field methods (APF) with Multi-rate Kalman Filter estimations (MKF), which takes into account present and future obstacle locations within a temporal horizon. By doing that, position uncertainty of obstacles is considered in the avoidance algorithm. This implies anticipation to the movement of the obstacles and its consideration in the path planning strategy. In this paper, forces derived from the potential field are taken as control inputs for the system model as well as considered in the Kalman Filter estimation. This leads to the generation of a local trajectory that fully meets the restrictions imposed by the kinematic model of the robot.

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