Abstract
The process of finding an optimum, smooth and feasible global path for mobile robot navigation usually involves determining the shortest polyline path, which will be subsequently smoothed to satisfy the requirements. Within this context, this paper deals with a novel roadmap algorithm for generating an optimal path in terms of Non-Uniform Rational B-Splines (NURBS) curves. The generated path is well constrained within the curvature limit by exploiting the influence of the weight parameter of NURBS and/or the control points' locations. The novelty of this paper lies in the fact that NURBS curves are not used only as a means of smoothing, but they are also involved in meeting the system's constraints via a suitable parameterization of the weights and locations of control points. The accurate parameterization of weights allows for a greater benefit to be derived from the influence and geometrical effect of this factor, which has not been well investigated in previous works. The effectiveness of the proposed algorithm is demonstrated through extensive MATLAB computer simulations.
Highlights
Since the 1980s, path planning has been a hot issue in robotic and automation fields
We report on extensive experiments to evaluate the effectiveness and performance of the proposed approach for global curvature-constrained path planning using NonUniform Rational B-Splines (NURBS) curves modelling
In Map I, after having stored the geometrical representation of the environment and after having calculated the corre‐ sponding extended skeleton, with the help of the Bellman Ford algorithm, a single-source shortest path, in the resulting graph, is determined
Summary
Since the 1980s, path planning has been a hot issue in robotic and automation fields. The solution should guarantee a collision-free path with minimum travelling distance, and provide a smooth and feasible path from the initial location to a goal location in an environment cluttered with obstacles. The path planning environment can be either static or dynamic. The whole solution can be found before starting execution. For dynamic or partially observable environ‐ ments, replanning is required frequently and more update time is needed. There are many studies on robot path planning using various approaches, which can be broadly classified into local and global methods
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