Collision-free Path Planning for Indoor Mobile Robots Based on Rapidly-exploring Random Trees and Piecewise Cubic Hermite Interpolating Polynomial
This paper deals with the off-line path planning problem for wheeled indoor mobile robots. In the proposed approach, the robot exploits depth information acquired by a two Kinect cameras vision system to perfectly model its surroundings environment. Then, the Rapidly-exploring Random Tree algorithm is used to generate a collision-free path linking the initial configuration (Source) to the final configuration (Target). This feasible path consists of a set of n nodes in form of vectors. Next, an algorithm is proposed to reduce the number of nodes and edges of the generated path. Finally, the found path is smoothed using Piecewise Cubic Hermite Interpolating Polynomial technique. The proposed RRT-PCHIP solution is validated on the differential mobile robot, RobuTER, evolving in an indoor environment cluttered with static obstacles. Obtained results are compared with another similar work using a Genetic Algorithm in terms of (i) generation time, (ii) path length, (iii) number of segments constituting the path and (iv) transfer time.
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