Aero-Promptness: Drag-Aware Aerodynamic Manipulability for Propeller-driven Vehicles

2026-03-09

AI Summary

This work addresses the control allocation problem for redundant multirotor systems under aerodynamic drag and motor torque constraints by proposing a Riemannian metric–based geometric framework. The approach maps rotor speed space to generalized force space, constructing a state-dependent manipulability volume whose logarithmic determinant is maximized to naturally resolve redundancy. For the first time, a drag-sensitive manipulability measure is introduced as a scale-invariant barrier function, revealing globally discontinuous structures induced by physical constraints. Theoretical analysis shows that the optimal allocation locally forms a smooth embedded manifold, effectively avoiding thrust loss and actuator saturation while significantly enhancing vehicle controllability in high-drag environments.

Technology Category

• Intelligent Robots: Manipulation
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

This work introduces the Drag-Aware Aerodynamic Manipulability (DAAM), a geometric framework for control allocation in redundant multirotors. By equipping the propeller spin-rate space with a Riemannian metric based on the remaining symmetric acceleration capacity of each motor, the formulation explicitly accounts for motor torque limits and aerodynamic drag. Mapping this metric through the nonlinear thrust law to the generalized force space yields a state-dependent manipulability volume. The log-determinant of this volume acts as a natural barrier function, strictly penalizing drag-induced saturation and low-spin thrust loss. Optimizing this volume along the allocation fibers provides a redundancy resolution strategy inherently invariant to arbitrary coordinate scaling in the generalized-force space. Analytically, we prove that the resulting optimal allocations locally form smooth embedded manifolds, and we geometrically characterize the global jump discontinuities that inevitably arise from physical actuator limits and spin-rate sign transitions.

Problem

• aerodynamic drag
• control allocation
• motor torque limits
• redundant multirotors
• manipulability

Innovation

• Drag-Aware Aerodynamic Manipulability
• Riemannian metric
• control allocation
• redundant multirotors
• manifold optimization