To solve the autonomous navigation problem in complex environments, an efficient motion planning approach is newly presented in this paper. Considering the challenges from large-scale, partially unknown complex environments, a three-layer motion planning framework is elaborately designed, including global path planning, local path optimization, and time-optimal velocity planning. Compared with existing approaches, the novelty of this work is twofold: 1) a novel heuristic-guided pruning strategy of motion primitives is proposed and fully integrated into the state lattice-based global path planner to further improve the computational efficiency of graph search, and 2) a new soft-constrained local path optimization approach is proposed, wherein the sparse-banded system structure of the underlying optimization problem is fully exploited to efficiently solve the problem. We validate the safety, smoothness, flexibility, and efficiency of our approach in various complex simulation scenarios and challenging real-world tasks. It is shown that the computational efficiency is improved by 66.21% in the global planning stage and the motion efficiency of the robot is improved by 22.87% compared with the recent quintic B\'{e}zier curve-based state space sampling approach. We name the proposed motion planning framework E$ \mathrm{^3} $MoP, where the number 3 not only means our approach is a three-layer framework but also means the proposed approach is efficient in three stages.
翻译:为解决复杂环境中的自主导航问题,本文件新提出了高效的机动规划方法。考虑到大规模、部分不为人知的复杂环境带来的挑战,我们精心设计了一个三层运动规划框架,包括全球路径规划、本地路径优化和时间最佳速度规划。与现有方法相比,这项工作的新颖之处是双重的:1) 提出了一个新的超光速引导运动原始动力操纵运行战略,并将其充分纳入基于州基平板全球路径规划仪,以进一步提高图表搜索的计算效率;2) 提出了一个新的软软约束本地路径优化方法,其中充分利用了基本优化问题的分散带式系统结构,以有效解决问题。我们验证了我们在不同复杂的模拟情景和挑战现实世界任务中所采用的方法的安全、顺畅、灵活性和效率。我们指出,全球规划阶段的计算效率提高了66.21%,而机器人的动作效率则提高了22.87%,与最近的开端 B\ {ezier curizer 曲线定位法相比提高了22.87 %,而在三个基于州级框架中,我们提议的3级空间取样法只是3个阶段。我们提出的运动的方法是3级方法。