A New Path Generation Algorithm Based on Accurate NURBS Curves

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 NonUniform 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.

Hybrid Evolutionary Approach to Multi-objective Path Planning for UAVs

“…Oberge et al [12], Ganganath et al [15], and Ma et al [18] derive different energy consumption models and compute the energy needed to travel along a certain path in order to minimize it. A group of path planners [8]- [12], [16]- [18], [20] aims to maximize the clearance to static obstacles or defined danger zones. Other approaches [9], [12], [13], [16]- [18] consider various feasibility objectives like a path's smoothness, minimal curvature, or its maximum altitude.…”

“…A group of path planners [8]- [12], [16]- [18], [20] aims to maximize the clearance to static obstacles or defined danger zones. Other approaches [9], [12], [13], [16]- [18] consider various feasibility objectives like a path's smoothness, minimal curvature, or its maximum altitude. Furthermore, some works take a safety objective into account, which can be seen from two different perspectives: Ahmed et al [11] ensure safety through maximization of the robot's range of vision in its environment, whereas Babel's [13] approach minimizes the agent's chance to be detected by other agents.…”

“…Babel [13] is choosing one of the several proposed objectives to optimize it separately and thus can not provide any Pareto front. Jalel et al [16] are applying several single-objective optimization stages, which has the effect that solutions of a latter stage can get worse with respect to objectives of a previous stage. The other approaches [8]- [11], [14], [15], [19], [20] handle two objectives in a multi-objective manner.…”

“…3) Used solvers: Some works use different graph-based, single-objective solvers to solve their path planning problems, respectively. Jalel et al [16] utilize a version of the Bellman-Ford algorithm [21]. Babel [13] uses the Dijkstra algorithm [7].…”

Path Planning Based on Parametric Curves

“…More recently, in [9][10][11][12] a predefined NURBS curve is used to improve its properties adjusting the weights.…”