Miao Zhang, Ziju Li, Shengwei Liu, Lei Jiang, Xiaoyan Chen, and Siyu Zhang
Path planning, bidirectional search, adaptive step size, elliptical sampling domain
In this paper, an improved informed bidirectional rapidly-exploring random trees (improved IRRT-connect) algorithm is proposed to obtain path planning for industrial robot in complex industrial environments. Considering the long planning time, low iteration efficiency and local optima of the RRT and RRT algorithms, the proposed algorithm improves the aforementioned drawbacks. To adapt to the path expansion in complex environments and speed up the search process, two improvements are proposed in this paper. First, an adaptive step strategy is proposed in terms of step expansion to avoid sampling waste. Moreover, in terms of sampling area, the proposed algorithm utilises the major axis of ellipse equations to restrict the elliptical sampling domain to address the low adaptiveness issue. Meanwhile, this enhancement avoids sampling domain iterations, which accelerates the sampling process and improves the accuracy of the path planning. In this paper, the proposed algorithm is compared with RRT-connect, adaptive step size RRT-connect and adaptive ellipse RRT-connect algorithms, and three two-dimensional simulation experiments are carried out in MATLAB. The effectiveness and practicability of the proposed method are verified with the simulation results.
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