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PE-Planner: A Performance-Enhanced Quadrotor Motion Planner for Autonomous Flight in Complex and Dynamic Environments

Jiaxin Qiu, Qingchen Liu, Jiahu Qin, Dewang Cheng, Yawei Tian, Qichao Ma

TL;DR

This work addresses the challenge of fast, safe autonomous quadrotor flight in complex and dynamic environments with disturbances. It proposes PE-Planner, a two-layer approach combining a global planner (kinodynamic path searching plus $B$-spline optimization) and a local planner (MPCC with CBF constraints and a disturbance observer implemented as GPIO) to generate real-time control inputs. Key contributions include integrating high-order discrete-time CBFs with MPCC constraints and embedding disturbance estimation into the planning loop, achieving robust performance under dynamic obstacles and wind/payload disturbances; the method demonstrates speeds up to $6.8$ m/s in challenging scenarios and validates results in real-world experiments with payload and wind disturbances. The approach enhances practical autonomous flight capabilities for rescue, delivery, and dynamic navigation in uncertain environments, and the authors release accompanying code for reproducibility.

Abstract

The role of a motion planner is pivotal in quadrotor applications, yet existing methods often struggle to adapt to complex environments, limiting their ability to achieve fast, safe, and robust flight. In this letter, we introduce a performance-enhanced quadrotor motion planner designed for autonomous flight in complex environments including dense obstacles, dynamic obstacles, and unknown disturbances. The global planner generates an initial trajectory through kinodynamic path searching and refines it using B-spline trajectory optimization. Subsequently, the local planner takes into account the quadrotor dynamics, estimated disturbance, global reference trajectory, control cost, time cost, and safety constraints to generate real-time control inputs, utilizing the framework of model predictive contouring control. Both simulations and real-world experiments corroborate the heightened robustness, safety, and speed of the proposed motion planner. Additionally, our motion planner achieves flights at more than 6.8 m/s in a challenging and complex racing scenario.

PE-Planner: A Performance-Enhanced Quadrotor Motion Planner for Autonomous Flight in Complex and Dynamic Environments

TL;DR

This work addresses the challenge of fast, safe autonomous quadrotor flight in complex and dynamic environments with disturbances. It proposes PE-Planner, a two-layer approach combining a global planner (kinodynamic path searching plus -spline optimization) and a local planner (MPCC with CBF constraints and a disturbance observer implemented as GPIO) to generate real-time control inputs. Key contributions include integrating high-order discrete-time CBFs with MPCC constraints and embedding disturbance estimation into the planning loop, achieving robust performance under dynamic obstacles and wind/payload disturbances; the method demonstrates speeds up to m/s in challenging scenarios and validates results in real-world experiments with payload and wind disturbances. The approach enhances practical autonomous flight capabilities for rescue, delivery, and dynamic navigation in uncertain environments, and the authors release accompanying code for reproducibility.

Abstract

The role of a motion planner is pivotal in quadrotor applications, yet existing methods often struggle to adapt to complex environments, limiting their ability to achieve fast, safe, and robust flight. In this letter, we introduce a performance-enhanced quadrotor motion planner designed for autonomous flight in complex environments including dense obstacles, dynamic obstacles, and unknown disturbances. The global planner generates an initial trajectory through kinodynamic path searching and refines it using B-spline trajectory optimization. Subsequently, the local planner takes into account the quadrotor dynamics, estimated disturbance, global reference trajectory, control cost, time cost, and safety constraints to generate real-time control inputs, utilizing the framework of model predictive contouring control. Both simulations and real-world experiments corroborate the heightened robustness, safety, and speed of the proposed motion planner. Additionally, our motion planner achieves flights at more than 6.8 m/s in a challenging and complex racing scenario.
Paper Structure (26 sections, 2 theorems, 31 equations, 8 figures, 7 tables)

This paper contains 26 sections, 2 theorems, 31 equations, 8 figures, 7 tables.

Table of Contents

  1. Introduction
  2. RELATED WORK
  3. Trajectory Planning in Complex Environments
  4. Trajectory Tracking Control
  5. GLOBAL PLANNER
  6. Kinodynamic Path Searching
  7. B-Spline Trajectory Optimization
  8. Cubic Uniform B-Spline
  9. Trajectory Optimization Problem Formulation
  10. LOCAL PLANNER
  11. Quadrotor Dynamics
  12. Model Predictive Contouring Control
  13. Static/Dynamic Collision Avoidance with CBF
  14. Disturbance Observer
  15. SIMULATION EXPERIMENTS
  16. ...and 11 more sections

Key Result

Proposition 1

The arc length of the trajectory from the starting time $t_3$ to any $t\in\left[t_3,t_{N_b+1}\right]$ is proportional to $\left(t - t_3\right)$, if the following conditions are met:

Figures (8)

  • Figure 1: Flying with a 370g payload in a complex dynamic environment.
  • Figure 2: The structure diagram of PE-Planner.
  • Figure 3: Collision avoidance illustration.
  • Figure 4: Flying in the simulation environment.
  • Figure 5: Response to 8.49N external force disturbance.
  • ...and 3 more figures

Theorems & Definitions (8)

  • Proposition 1
  • proof
  • Remark 1
  • Remark 2
  • Definition 1: Relative degree7506909
  • Definition 2: High-order discrete-time CBFhigh-order-discrete-cbf
  • Lemma 1
  • proof