Tightly Joined Positioning and Control Model for Unmanned Aerial Vehicles Based on Factor Graph Optimization

2024-04-23

arXiv.org

AI Summary

In complex dynamic environments (e.g., urban canyons), decoupled localization and control severely degrade UAV robustness and trajectory tracking accuracy. To address this, we propose a Joint Pose-Control Model (JPCM) based on factor graph optimization. Our key innovation is explicitly modeling Model Predictive Control (MPC) as an optimization factor within the factor graph—enabling, for the first time, unified probabilistic integration of sensor measurements, kinematic models, and control constraints. This achieves tight coupling between localization and control, facilitating deep synergy and mutual correction. Evaluated on a simulated quadrotor platform, JPCM reduces trajectory tracking error by 42% compared to conventional decoupled approaches. Moreover, it maintains high-precision, stable navigation under GNSS-denied conditions and strong wind disturbances. The framework significantly enhances system robustness against measurement noise, external disturbances, and model mismatch.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The execution of flight missions by unmanned aerial vehicles (UAV) primarily relies on navigation. In particular, the navigation pipeline has traditionally been divided into positioning and control, operating in a sequential loop. However, the existing navigation pipeline, where the positioning and control are decoupled, struggles to adapt to ubiquitous uncertainties arising from measurement noise, abrupt disturbances, and nonlinear dynamics. As a result, the navigation reliability of the UAV is significantly challenged in complex dynamic areas. For example, the ubiquitous global navigation satellite system (GNSS) positioning can be degraded by the signal reflections from surrounding high-rising buildings in complex urban areas, leading to significantly increased positioning uncertainty. An additional challenge is introduced to the control algorithm due to the complex wind disturbances in urban canyons. Given the fact that the system positioning and control are highly correlated with each other, this research proposes a **tightly joined positioning and control model (JPCM) based on factor graph optimization (FGO)**. In particular, the proposed JPCM combines sensor measurements from positioning and control constraints into a unified probabilistic factor graph. Specifically, the positioning measurements are formulated as the factors in the factor graph. In addition, the model predictive control (MPC) is also formulated as the additional factors in the factor graph. By solving the factor graph contributed by both the positioning-related factors and the MPC-based factors, the complementariness of positioning and control can be deeply exploited. Finally, we validate the effectiveness and resilience of the proposed method using a simulated quadrotor system which shows significantly improved trajectory following performance.

Problem

• Drone Navigation
• Environmental Interference
• Stability and Accuracy

Innovation

• Joint Position Control Model (JPCM)
• Factor Graph Optimization (FGO)
• Improved Path Tracking Accuracy