Journal
APPLIED SOFT COMPUTING
Volume 100, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2020.106960
Keywords
Mobile robot; Continuous Bezier curve; Smooth path planning; Adaptive fractional-order velocity; Particle swarm optimization
Categories
Funding
- National Natural Science Foundation of China [61703242, 61703245, 61873148, 61933007]
- China Postdoctoral Science Foundation [2018T110702]
- Postdoctoral Special Innovation Foundation of Shandong province of China [201701015]
- Alexander von Humboldt Foundation of Germany
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This paper presents a new strategy for planning smooth paths for mobile robots using an improved PSO algorithm and continuous high-degree Bezier curves. The improved PSO algorithm effectively addresses local trapping and premature convergence issues, while experiments confirm the advantages of the new strategy.
In this paper, a new strategy is developed to plan the smooth path for mobile robots through an improved PSO algorithm in combination with the continuous high-degree Bezier curve. Rather than connecting several low-degree Bezier curve segments, the use of continuous high-degree Bezier curves facilitates the fulfillment of the requirement of high-order continuity such as the continuous curvature derivative, which is critical for the motion control of the mobile robots. On the other hand, the smooth path planning of mobile robots is mathematically an optimization problem that can be dealt with by evolutionary computation algorithms. In this regard, an improved particle swarm optimization (PSO) algorithm is proposed to tackle the local trapping and premature convergence issues. In the improved PSO algorithm, an adaptive fractional-order velocity is introduced to enforce some disturbances on the particle swarm according to its evolutionary state, thereby enhancing its capability of jumping out of the local minima and exploring the searching space more thoroughly. The superiority of the improved PSO algorithm is verified by comparing with several standard and modified PSO algorithms on some benchmark functions, and the advantages of the new strategy is also confirmed by several comprehensive simulation experiments for the smooth path planning of mobile robots. (c) 2020 Elsevier B.V. All rights reserved.
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