Towards Efficient Motion Planning for UAVs: Lazy A* Search with Motion Primitives

TL;DR

This paper introduces a lazy search-based motion planning algorithm for UAVs that enhances real-time trajectory planning by reducing computational load while ensuring dynamic feasibility and collision avoidance.

Contribution

It presents a novel lazy search approach integrated with motion primitives to improve efficiency in high-dimensional UAV motion planning.

Findings

Significantly faster planning times compared to traditional methods

Maintains optimality and dynamic feasibility of trajectories

Effective in high-dimensional search spaces

Abstract

Search-based motion planning algorithms have been widely utilized for unmanned aerial vehicles (UAVs). However, deploying these algorithms on real UAVs faces challenges due to limited onboard computational resources. The algorithms struggle to find solutions in high-dimensional search spaces and require considerable time to ensure that the trajectories are dynamically feasible. This paper incorporates the lazy search concept into search-based planning algorithms to address the critical issue of real-time planning for collision-free and dynamically feasible trajectories on UAVs. We demonstrate that the lazy search motion planning algorithm can efficiently find optimal trajectories and significantly improve computational efficiency.