Wake Up to the Past: Using Memory to Model Fluid Wake Effects on Robots

2026-03-23

AI Summary

This work addresses the challenge of modeling fluid wake-induced disturbances among neighboring robots in agile motion scenarios, where traditional memoryless models exhibit limited predictive accuracy. The study presents the first systematic analysis of key properties essential for effective wake-effect predictors and introduces a memory-augmented, data-driven model that integrates historical state information with transmission delay prediction. Leveraging seven neural network architectures incorporating memory mechanisms, the approach models spatiotemporally evolving wake interactions across four distinct fluid media. Real-world validation is conducted using a planar linear gantry platform. Experimental results demonstrate that the proposed method significantly improves prediction accuracy of wake disturbances across diverse fluid environments, offering a novel paradigm for multi-robot cooperative control in fluidic settings.

Technology Category

• Intelligent Robots: State Estimation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Autonomous aerial and aquatic robots that attain mobility by perturbing their medium, such as multicopters and torpedoes, produce wake effects that act as disturbances for adjacent robots. Wake effects are hard to model and predict due to the chaotic spatio-temporal dynamics of the fluid, entangled with the physical geometry of the robots and their complex motion patterns. Data-driven approaches using neural networks typically learn a memory-less function that maps the current states of the two robots to a force observed by the "sufferer" robot. Such models often perform poorly in agile scenarios: since the wake effect has a finite propagation time, the disturbance observed by a sufferer robot is some function of relative states in the past. In this work, we present an empirical study of the properties a wake-effect predictor must satisfy to accurately model the interactions between two robots mediated by a fluid. We explore seven data-driven models designed to capture the spatio-temporal evolution of fluid wake effects in four different media. This allows us to introspect the models and analyze the reasons why certain features enable improved accuracy in prediction across predictors and fluids. As experimental validation, we develop a planar rectilinear gantry for two spinning monocopters to test in real-world data with feedback control. The conclusion is that support of history of previous states as input and transport delay prediction substantially helps to learn an accurate wake-effect predictor.

Problem

• wake effects
• fluid dynamics
• robot interaction
• spatio-temporal modeling
• disturbance prediction

Innovation

• wake effect modeling
• memory-based prediction
• spatio-temporal dynamics
• fluid-robot interaction
• transport delay