Primitive-Planner: An Ultra Lightweight Quadrotor Planner with Time-optimal Primitives

TL;DR

This work presents an ultra-lightweight quadrotor planner that precomputes time-optimal primitives offline using TOPP-RA, enabling fast online planning with minimal computational load. A deterministic collision-checking scheme based on voxel grids prunes unsafe primitives efficiently, while a receding-horizon planner selects the minimum-cost safe trajectory and aligns it with the current velocity. The approach achieves short flight times and distances with low online overhead, outperforming Mapless and EGO-Planner-v2 in dense environments and across real-world SWaP hardware. The combination of offline time-optimal primitives, deterministic collision checking, and receding-horizon execution offers a practical, robust solution for swift, resource-constrained quadrotor navigation.

Abstract

It is a significant requirement for a quadrotor trajectory planner to simultaneously guarantee trajectory quality and system lightweight. Many researchers focus on this problem, but there's still a gap between their performance and our common wish. In this paper, we propose an ultra lightweight quadrotor planner with time-optimal primitives. Firstly, a novel motion primitive library is proposed to generate time-optimal and dynamical feasible trajectories offline. Secondly, we propose a fast collision checking method with a deterministic time consumption, independent of the sampling resolution of the primitives. Finally, we select the minimum cost trajectory to execute among the safe primitives based on user-defined requirements. The propsed transformation relation between the local trajectories ensures the smoothness of the global trajectory. The planner reduces unnecessary online computing power consumption as much as possible, while ensuring a high-quality trajectory. Benchmark comparisons show that our method can generate the shortest flight time and distance of trajectory with the lowest computation overload. Challenging real-world experiments validate the robustness of our method.

Figures (9)

• Figure 1: Real-world experiment. The SWaP(Size, Weight, and Power) constrained quadrotor rapidly avoids obstacles one by one with a maximum expected speed of $2m/s$ using time-optimal primitives.
• Figure 2: System overview.
• Figure 3: Path library generation; Blue splines are paths. The origin of all paths coincides with the origin of the coordinate system {V}. All paths are tangent to the x-axis. The orange dots are the end point of each path.
• Figure 4: Fast collision checking; Voxel grid $N_{th}$ associates paths $0$ and $1$. The blue paths are free. The red paths are occupied.
• Figure 5: Trajectory selection; The blue curves represent all trajectories in the motion primitive library. The green curve represents the selected trajectory.