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

2025-02-24

AI Summary

Real-time trajectory planning for resource-constrained quadrotor platforms remains challenging due to stringent computational and dynamical feasibility requirements. Method: This paper proposes an ultra-lightweight time-optimal trajectory planner comprising: (1) offline precomputation of a dynamics-feasible, time-optimal motion primitive library; (2) a deterministic-complexity fast collision-checking algorithm; (3) analytical transition relations between primitives ensuring C² continuity and global smoothness; and (4) online optimal primitive selection and local-to-global analytical mapping guided by a user-defined cost function. Contribution/Results: On standard benchmarks, the method achieves time- and path-length optimality while guaranteeing safety and dynamical feasibility, with significantly reduced computational overhead. Real-world flight experiments demonstrate millisecond-level real-time performance and high robustness under hardware constraints.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Guarantee trajectory quality and system lightweight
• Generate time-optimal and dynamical feasible trajectories
• Reduce unnecessary online computing power consumption

Innovation

• Time-optimal primitive library
• Fast collision checking method
• Minimum cost trajectory selection