This paper addresses the problem of path plannrng using a dynamic vehicle model. Previous works which include a basic kinematic model generate paths that are only realistic at very low speed. By considering higher vehicle speed during navigation, the vehicle can significantly deviate from the planned trajectory. Consequently, the planned path becomes unusable for the mission achievement. So, to bridge a gap between planning and navigation, we propose a realistic path planner based on a dynamic vehicle model.
This paper addresses the problem of safe path planning in an uncertain-configuration space. We consider the case of a car-like robot moving in an indoor environment (three-dimensional space). The Extended Kalman Filter (EKF) is a popular way to localize such a robot and to estimate its configuration uncertainty during navigation. Consequently, we supply an EKF with simulated measurements in order to compute realistic uncertainties (in a four-dimensional space) during path planning. We show that our Safe-RRT algorithm, based upon Rapidly-exploring Random Trees (RRT), is an efficient way to find a path in the resulting seven-dimensional uncertainconfiguration space.
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