The goal of this paper is to develop a continuous optimization-based refinement of the reference trajectory to 'push it out' of the obstacle-occupied space in the global phase for Multi-rotor Aerial Vehicles in unknown environments. Our proposed approach comprises two planners: a global planner and a local planner. The global planner refines the initial reference trajectory when the trajectory goes either through an obstacle or near an obstacle and lets the local planner calculate a near-optimal control policy. The global planner comprises two convex programming approaches: the first one helps to refine the reference trajectory, and the second one helps to recover the reference trajectory if the first approach fails to refine. The global planner mainly focuses on real-time performance and obstacles avoidance, whereas the proposed formulation of the constrained nonlinear model predictive control-based local planner ensures safety, dynamic feasibility, and the reference trajectory tracking accuracy for low-speed maneuvers, provided that local and global planners have mean computation times 0.06s (15Hz) and 0.05s (20Hz), respectively, on an NVIDIA Jetson Xavier NX computer. The results of our experiment confirmed that, in cluttered environments, the proposed approach outperformed three other approaches: sampling-based pathfinding followed by trajectory generation, a local planner, and graph-based pathfinding followed by trajectory generation.
翻译:本文的目标是对在未知环境中的多旋转飞行器的全球阶段中“将其推出”障碍占用空间的参考轨迹进行持续的优化完善。 我们拟议的方法包括两个规划者:一个全球规划员和一个地方规划员。 全球规划员在轨迹通过障碍或接近障碍时改进最初的参考轨迹,让当地规划员计算出接近最佳的控制政策。 全球规划员包括两个 convex编程方法:第一个是帮助改进参考轨迹,第二个是帮助在第一个方法未能完善的情况下恢复参考轨迹。全球规划员主要侧重于实时性能和避免障碍,而拟议的非线性模型基于预测的本地规划员的配置则确保安全、动态可行性和低速动作的参考轨迹跟踪准确性,条件是地方和全球规划员在NVIDIA Jetavier NX 计算机上分别使用0.06s(15Hz)和0.05(20Hz)的平均值,这两类方法分别是NVIDIA Jetson Xavier NX 计算机上所用的参考轨迹迹轨迹。 三种实验结果证实了生成的轨道,以三种方法,以当地模拟方法取取取取取取取取。