Search-based Motion Planning for Aggressive Flight in SE(3)

TL;DR

This paper presents a search-based trajectory planning method for quadrotors that exploits their maneuverability and an ellipsoid model to generate safe, optimal, and dynamically feasible trajectories in cluttered environments, including real-world tests.

Contribution

It introduces a novel search-based planning approach using an ellipsoid model for quadrotor collision checking, enabling aggressive and safe flight through narrow gaps without prior gap information.

Findings

Successfully generated safe trajectories in cluttered environments.

Validated approach through simulations and real-world experiments.

Achieved near-optimal flight times with the proposed method.

Abstract

Quadrotors with large thrust-to-weight ratios are able to track aggressive trajectories with sharp turns and high accelerations. In this work, we develop a search-based trajectory planning approach that exploits the quadrotor maneuverability to generate sequences of motion primitives in cluttered environments. We model the quadrotor body as an ellipsoid and compute its flight attitude along trajectories in order to check for collisions against obstacles. The ellipsoid model allows the quadrotor to pass through gaps that are smaller than its diameter with non-zero pitch or roll angles. Without any prior information about the location of gaps and associated attitude constraints, our algorithm is able to find a safe and optimal trajectory that guides the robot to its goal as fast as possible. To accelerate planning, we first perform a lower dimensional search and use it as a heuristic to guide the generation of a final dynamically feasible trajectory. We analyze critical discretization parameters of motion primitive planning and demonstrate the feasibility of the generated trajectories in various simulations and real-world experiments.