Geometric Model Predictive Path Integral for Agile UAV Control with Online Collision Avoidance

2025-10-14

AI Summary

Agile trajectory tracking and real-time online obstacle avoidance for unmanned aerial vehicles (UAVs) operating at high speeds (≥10 m/s) in complex environments remain challenging. Method: This paper proposes a geometric optimal control framework integrating SE(3)-based geometric control with Model Predictive Path Integral (MPPI) control. It couples MPPI with stereo depth camera perception for dynamic trajectory replanning directly from real-time dense depth maps—a first in this domain. To enhance robustness and computational efficiency, the method introduces variable simulation step sizes, adaptive noise modeling, and receding-horizon weighted trajectory sampling. Results: In simulation, the system achieves 13 m/s flight through a forest scene with position tracking error comparable to conventional geometric controllers. In physical experiments, it attains millisecond-scale obstacle avoidance response and high-precision trajectory tracking at 10 m/s—outperforming state-of-the-art planners in both accuracy and real-time performance.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

In this letter, we introduce Geometric Model Predictive Path Integral (GMPPI), a sampling-based controller capable of tracking agile trajectories while avoiding obstacles. In each iteration, GMPPI generates a large number of candidate rollout trajectories and then averages them to create a nominal control to be followed by the Unmanned Aerial Vehicle (UAV). We propose using geometric SE(3) control to generate part of the rollout trajectories, significantly increasing precision in agile flight. Furthermore, we introduce varying rollout simulation time step length and dynamic cost and noise parameters, vastly improving tracking performance of smooth and low-speed trajectories over an existing Model Predictive Path Integral (MPPI) implementation. Finally, we propose an integration of GMPPI with a stereo depth camera, enabling online obstacle avoidance at high speeds, a crucial step towards autonomous UAV flights in complex environments. The proposed controller can track simulated agile reference trajectories with position error similar to the geometric SE(3) controller. However, the same configuration of the proposed controller can avoid obstacles in a simulated forest environment at speeds of up to 13m/s, surpassing the performance of a state-of-the-art obstacle-aware planner. In real-world experiments, GMPPI retains the capability to track agile trajectories and avoids obstacles at speeds of up to 10m/s.

Problem

• Tracking agile UAV trajectories with obstacle avoidance
• Improving precision in agile flight using geometric control
• Enabling online collision avoidance at high speeds

Innovation

• Sampling-based controller generates rollout trajectories for UAV control
• Geometric SE(3) control enhances precision in agile flight
• Integration with stereo camera enables online obstacle avoidance