基于偶阶超椭圆轨道极限环的分层路径规划器

Shuai Zhou, Xiaosu Xu, Cheng Chi, Shede Liu
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摘要

提出了一种基于偶阶超椭圆轨道极限环的分层路径规划器(HPP)。传统的椭圆轨道极限环(EOLC)得到了广泛的研究。然而,在结构化矩形障碍物环境中,椭圆包围矩形时孔隙率较高,环境适应性较低。因此,引入了EHOLC,并在给定孔隙度的基础上确定了障碍物的超椭圆方程阶。然后,建立了EHOLC的微分方程,求解了轨迹。最后,提出了一种分层路径规划方法,根据环境映射选择避障控制器和目标到达控制器求解整个路径。仿真实验表明,与传统的EOLC相比,基于eholc的HPP具有更高的地图空间利用率,更短的路径规划长度,并保持了灵活性、平滑性和安全性。
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A Hierarchical Path Planner Based on Even-order Hyperelliptic Orbital Limit-Cycles
This paper proposes a Hierarchical Path Planner (HPP) based on Even-order Hyperelliptic Orbital Limit-Cycles (EHOLC). Traditional Elliptic Orbital Limit-Cycles (EOLC) have been widely studied. However, in structured rectangular obstacle environments, ellipses have a higher porosity when enveloping rectangles, resulting in lower environmental adaptability. Therefore, EHOLC is introduced, and the hyperelliptic equation order of obstacles is determined based on a set porosity. Then, the differential equation of the EHOLC is established, and the trajectory is solved. Finally, a hierarchical path planner is proposed, which selects obstacle avoidance controllers and target reaching controllers according to the environment map to solve the entire path. Simulation experiments show that compared with traditional EOLC, the EHOLC-based HPP has higher map space utilization, shorter path planning length, and maintains flexibility, smoothness, and safety.
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