Abstract
In this study, a new method of smooth path planning is proposed based on Bezier curves and is applied to solve the problem of redundant nodes and peak inflection points in the path planning process of traditional algorithms. First, genetic operations are used to obtain the control points of the Bezier curve. Second, a shorter path is selected by an optimization criterion that the length of the Bezier curve is determined by the control points. Finally, a safe distance and adaptive penalty factor are introduced into the fitness function to ensure the safety of the walking process of the robot. Numerous experiments are implemented in two different environments and compared with the existing methods. It is proved that the proposed method is more effective to generate a shorter, smoother, and safer path compared with traditional approaches.
Highlights
Path planning is an important research direction in the field of mobile robots, and it is one of the main difficulties in research on such robots [1]. e path planning problem aims to find the safest and shortest path autonomously without collisions from the start point to the target point under a given environment with barriers [2, 3]
A robot path planning method was proposed based on the improved genetic algorithm [15], in which the adaptability of a mobile robot path planning algorithm was improved by introducing chromosomes with variable lengths
The contributions of this paper are as follows: (1) compared with the methods introduced in [29, 30], our genetic operations are used to obtain the control points of the Bezier curve, which can guarantee the continuity of path curvature; (2) a shorter smooth path is selected by using an optimization criterion, which can ensure that the generated path is optimal without requiring further smoothing; and (3) in contrast with the methods in [6, 22], the safety in robots’ walking progress is further improved by introducing an adaptive adjustment in the fitness function
Summary
Path planning is an important research direction in the field of mobile robots, and it is one of the main difficulties in research on such robots [1]. e path planning problem aims to find the safest and shortest path autonomously without collisions from the start point to the target point under a given environment with barriers [2, 3]. A genetic algorithm was proposed to find the control points of the Computational Intelligence and Neuroscience segmented Bezier curves and solve the problem of the mobile robot path planning [16]. The contributions of this paper are as follows: (1) compared with the methods introduced in [29, 30], our genetic operations are used to obtain the control points of the Bezier curve, which can guarantee the continuity of path curvature; (2) a shorter smooth path is selected by using an optimization criterion, which can ensure that the generated path is optimal without requiring further smoothing; and (3) in contrast with the methods in [6, 22], the safety in robots’ walking progress is further improved by introducing an adaptive adjustment in the fitness function.
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