Muscle Coactivation in the Sky: Geometry and Pareto Optimality of Energy vs. Promptness in Multirotors

• URL: http://arxiv.org/abs/2602.14222v1
• Date: Sun, 15 Feb 2026 16:35:46 GMT
• Title: Muscle Coactivation in the Sky: Geometry and Pareto Optimality of Energy vs. Promptness in Multirotors
• Abstract summary: We show that the limited torque of the motors and the nonlinear aerodynamic map from rotor speed to thrust naturally give rise to the novel concept of promptness.<n>By treating energy consumption as a competing objective, we turn redundancy resolution into a principled multi-objective program on affine fibers.
• Score: 3.919116384604024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In robotics and human biomechanics, the tension between energy economy and kinematic readiness is well recognized; this work brings that fundamental principle to aerial multirotors. We show that the limited torque of the motors and the nonlinear aerodynamic map from rotor speed to thrust naturally give rise to the novel concept of promptness-a metric akin to dynamic aerodynamic manipulability. By treating energy consumption as a competing objective and introducing a geometric fiber-bundle formulation, we turn redundancy resolution into a principled multi-objective program on affine fibers. The use of the diffeomorphic transformation linearizing the signed-quadratic propulsion model allows us to lay the foundations for a rigorous study of the interplay between these costs. Through an illustrative case study on 4-DoF allocation on the hexarotor, we reveal that this interplay is fiber-dependent and physically shaped by hardware inequalities. For unidirectional thrusters, the feasible fibers are compact, yielding interior allocations and a short Pareto arc, while torque demands break symmetry and separate the optima. Conversely, with reversible propellers, the null space enables antagonistic rotor co-contraction that drives promptness to hardware limits, making optimal endurance and agility fundamentally incompatible in those regimes. Ultimately, rather than relying on heuristic tuning or black box algorithms to empirically improve task execution, this framework provides a foundational understanding of why and how to achieve agility through geometry-aware control allocation, offering possible guidance for vehicle design, certification metrics, and threat-aware flight operation.

Related papers

• A Neural Network Based Teleoperation for Remote Controlled Vehicles [98.38733381483934]
  Direct teleoperation of vehicles faces critical technical bottlenecks.<n> communication latency and the operator's inability to physically perceive unmodeled environmental disturbances.<n>We propose a tailored unilateral teleoperation framework.
  arXiv  Detail & Related papers  (2026-08-11T01:51:08Z)
• Generating Physically Plausible Parachute Dynamics with Deep Generative Modeling [2.327847860343284]
  Symplectic Parachute Generative Adrial Network (SPar-GAN) learns parachute dynamics directly from data.<n>We apply SPar-GAN to subscale parachute tests conducted at the National Full-Scale Aerodynamics Complex.<n>Results suggest that physics-constrained generative models can characterize parachute dynamics across operating conditions.
  arXiv  Detail & Related papers  (2026-07-13T20:50:34Z)
• Saturation-Aware Robust Trajectory Optimization for Reusable Launch Vehicles via Differentiable Physics [9.011250637911248]
  High-angle-of-attack flip maneuver of reusable launch vehicles presents challenges for robust trajectory optimization.<n>This paper presents a differentiable physics framework for saturation-aware robust trajectory optimization.
  arXiv  Detail & Related papers  (2026-07-02T14:30:37Z)
• Controlled Dynamics Attractor Transformer [58.15454173718687]
  Transformer architectures have advanced representation learning and inference in deep models through self-attention mechanisms.<n>In parallel,associative memory (AM) frameworks map representations onto energy landscapes, offering interpretable retrieval mechanisms.<n>We propose Controlled Dynamics Attractor Transformer (CCAT), which couples a mixture von Mises-Fisher (Mo-vMF) attention energy with a Hopfield refinement energy.<n>CCAT achieves state-of-the-art performance across multiple benchmarks in graph anomaly detection and graph classification.
  arXiv  Detail & Related papers  (2026-06-13T09:04:22Z)
• Dynamic Modeling and Robust Gait Optimization of a Compliant Worm Robot [6.048766517808481]
  This paper presents an experimentally grounded modeling and optimization framework for a compliant worm robot capable of traversing corrugated pipes.<n>A multi-objective gait optimization problem is formulated to maximize average speed while minimizing average power.<n>Results show that the proposed framework captures the dominant locomotion and energy-consumption behavior of the robot.
  arXiv  Detail & Related papers  (2026-04-13T20:20:03Z)
• Aero-Promptness: Drag-Aware Aerodynamic Manipulability for Propeller-driven Vehicles [3.919116384604024]
  The Drag-Aware Aerodynamic Manipulability (DAAM) is a geometric framework for control allocation in redundant multirotors.<n>We geometrically characterize the global jump discontinuities that inevitably arise from physical actuator limits and spin-rate sign transitions.
  arXiv  Detail & Related papers  (2026-03-09T06:02:59Z)
• Gradient-based Nested Co-Design of Aerodynamic Shape and Control for Winged Robots [27.783239201463715]
  This paper presents a general-purpose, gradient-based, nested co-design framework to optimize aerodynamic shape and the motion planner.<n>We validate our method on two complex dynamic tasks for fixed-wing gliders: perching and a short landing.<n>Our optimized designs improve task performance compared to an evolutionary baseline in a fraction of the time.
  arXiv  Detail & Related papers  (2026-03-06T14:18:18Z)
• Geometric Inverse Flight Dynamics on SO(3) and Application to Tethered Fixed-Wing Aircraft [5.021944882278981]
  We present a robotics-oriented, coordinate-free formulation of inverse flight dynamics for fixed-wing aircraft.<n>We derive a closed-form trajectory-to-input map yielding the attitude, angular velocity, and thrust-angle-of-attack pair.
  arXiv  Detail & Related papers  (2026-02-19T08:26:17Z)
• AI-Enhanced Automatic Design of Efficient Underwater Gliders [60.45821679800442]
  Building an automated design framework is challenging due to the complexities of representing glider shapes and the high computational costs associated with modeling complex solid-fluid interactions.<n>We introduce an AI-enhanced automated computational framework designed to overcome these limitations by enabling the creation of underwater robots with non-trivial hull shapes.<n>Our approach involves an algorithm that co-optimizes both shape and control signals, utilizing a reduced-order geometry representation and a differentiable neural-network-based fluid surrogate model.
  arXiv  Detail & Related papers  (2025-04-30T23:55:44Z)
• Efficient Design of Compliant Mechanisms Using Multi-Objective Optimization [50.24983453990065]
  We address the synthesis of a compliant cross-hinge mechanism capable of large angular strokes.<n>We formulate a multi-objective optimization problem based on kinetostatic performance measures.
  arXiv  Detail & Related papers  (2025-04-23T06:29:10Z)
• ReCoM: Realistic Co-Speech Motion Generation with Recurrent Embedded Transformer [58.49950218437718]
  We present ReCoM, an efficient framework for generating high-fidelity and generalizable human body motions synchronized with speech.<n>The core innovation lies in the Recurrent Embedded Transformer (RET), which integrates Dynamic Embedding Regularization (DER) into a Vision Transformer (ViT) core architecture.<n>To enhance model robustness, we incorporate the proposed DER strategy, which equips the model with dual capabilities of noise resistance and cross-domain generalization.
  arXiv  Detail & Related papers  (2025-03-27T16:39:40Z)
• VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait Representation [78.92147339883137]
  We show that it is pivotal in increasing controller robustness by learning a latent space capturing the key stance phases constituting a particular gait. We demonstrate that specific properties of the drive signal map directly to gait parameters such as cadence, footstep height and full stance duration. The use of a generative model facilitates the detection and mitigation of disturbances to provide a versatile and robust planning framework.
  arXiv  Detail & Related papers  (2022-05-02T19:49:53Z)
• Next Steps: Learning a Disentangled Gait Representation for Versatile Quadruped Locomotion [69.87112582900363]
  Current planners are unable to vary key gait parameters continuously while the robot is in motion. In this work we address this limitation by learning a latent space capturing the key stance phases constituting a particular gait. We demonstrate that specific properties of the drive signal map directly to gait parameters such as cadence, foot step height and full stance duration.
  arXiv  Detail & Related papers  (2021-12-09T10:02:02Z)
• Fast and Feature-Complete Differentiable Physics for Articulated Rigid Bodies with Contact [13.502749968646519]
  We present a differentiable physics engine that supports Lagrangian dynamics and hard contact constraints for articulated rigid body simulation. Our differentiable physics engine offers a complete set of features that are typically only available in non-differentiable physics simulators.
  arXiv  Detail & Related papers  (2021-03-30T01:48:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.