Search-based Planning of Dynamic MAV Trajectories Using Local Multiresolution State Lattices

TL;DR

This paper introduces a local multiresolution approach to search-based planning for MAV trajectories, significantly reducing computation time by adaptively varying resolution in high-dimensional state lattices, enabling efficient replanning in dynamic environments.

Contribution

The paper extends local multiresolution techniques to high-dimensional state lattices with velocities and accelerations for MAV trajectory planning, improving efficiency.

Findings

Planning times are significantly reduced.

Approach enables faster replanning in dynamic environments.

Maintains trajectory feasibility and optimality.

Abstract

Search-based methods that use motion primitives can incorporate the system's dynamics into the planning and thus generate dynamically feasible MAV trajectories that are globally optimal. However, searching high-dimensional state lattices is computationally expensive. Local multiresolution is a commonly used method to accelerate spatial path planning. While paths within the vicinity of the robot are represented at high resolution, the representation gets coarser for more distant parts. In this work, we apply the concept of local multiresolution to high-dimensional state lattices that include velocities and accelerations. Experiments show that our proposed approach significantly reduces planning times. Thus, it increases the applicability to large dynamic environments, where frequent replanning is necessary.