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Autonomous thermodynamic cycles via robotic mobility and sensing

Sofia Kuperman, Ezra Ben-Abu, Yaron Veksler, Anna Zigelman, Sefi Givli, Amir D. Gat

arXiv:2610.11667v1cond-mat.softcs.CEcs.ROcs.SYeess.SYphysics.app-ph

TL;DR

Conventional thermodynamic cycles rely on fixed thermal reservoirs, restricting where they can operate. This work pairs multistable capsules with energy-aware navigation, showing that active traversal of thermal fields can support cycles that harvest and store energy.

  • Problem

    Conventional thermodynamic cycles depend on stationary thermal reservoirs, anchoring operation to locations where those reservoirs exist.

    Source: Introduction · p. 1

  • Method

    The authors use elastically multistable gas-filled capsules in an internal network and plan routes across heterogeneous temperature fields to balance locomotion cost with energy harvesting.

    Source: Introduction · p. 2, Energy-aware path optimization in a… · p. 8

  • Results

    Thermodynamic efficiencies of the energy storage processes in Figs. 3–5 were 86.54%, 28.67%, and 66.81%, respectively; cycles emerge through active traversal of environmental thermal fields.

    Source: Discussion · p. 11

  • Takeaways & Limitations

    The framework offers a programmable, scalable, and environmentally safe strategy for robotic energy management using environmental thermal gradients.

    Source: Introduction · p. 2

    Future trajectory planning must address time-varying or partially known temperature fields and the initial energy needed to sense and traverse thermal gradients.

    Source: Discussion · p. 11

Abstract

from arXiv · show

Thermodynamic cycles are the foundation of energy conversion across natural and engineered systems, transforming heat into useful work. However, these cycles traditionally operate between fixed thermal reservoirs, restricting them to specific locations and temperature differences. Here, we introduce autonomous thermodynamic cycles enabled by robotic mobility and sensing, allowing robots to perform thermodynamic cycles by accessing spatially varying temperature fields. We experimentally realize this concept using multistable gas-filled capsules that circulate within the system across a thermal gradient. Our model reveals that rapid transitions in the capsules' energy states allow the system to operate as a mobile heat engine that harvests and stores energy. By linking the capsule-scale internal energy dynamics to the robot's large-scale navigation strategy, we optimize locomotion paths that balance motion cost and energy harvesting. These findings demonstrate that thermodynamic cycles can emerge when autonomous systems navigate their environments, offering an artificial analog of organisms that forage for energy across spatial resources.

Introduction

Conventional thermodynamic cycles depend on stationary reservoirs, while mobile robots could exploit spatial thermal resources through sensing, planning, and movement. This work introduces an internal capsule-based energy system for harvesting environmental heat through mobility.

Introduction

  • Conventional cycles rely on stationary reservoirs, whereas mobile robots could use sensing, planning, and movement to access spatially varying thermal resources.Thermal gradients can occur across nearby materials, phases, heat sources, or diurnal cycles.Source: Introduction · pp. 1–2
  • Existing endurance strategies include electrochemical storage with passive harvesting, photovoltaics, kinetic recovery, and thermoelectric harvesting from residual heat.A complementary strategy couples energy acquisition to robotic locomotion and decision-making.Source: Introduction · p. 2
  • The proposed internal energy network uses reversible, deformable energy-bearing capsules rather than liquid carriers, with encapsulated gases such as air as a safe implementation.The authors position metafluids as a foundation for reconfigurable, distributed architectures inside a robot.Source: Introduction · p. 2
  • The work introduces an internal distributed energy system of elastically multistable, gas-filled capsules that harvests and stores environmental thermal energy through mobility.The framework analyzes coupled dynamics and thermodynamics, and initial experiments validate the concept.Source: Introduction · p. 2

Results

Gas-filled multistable capsules use thermal changes to trigger rapid state transitions that store or harvest energy. A path-planning framework trades motion cost against thermal crossings that yield energy.

Embedded energy via multistable unit cells

  • Gas-filled capsules combine bistable units with magnetic edges, converting rapid snap-through motion into electrical energy while tunable energy barriers set storage capacity.Transitions occur when applied force exceeds the opening or closing threshold.Source: Embedded energy via multistable unit… · pp. 3–4

Equilibrium states of multistable capsules

  • The capsule model locates equilibria where elastic and net actuation forces balance, with snap-through at force thresholds; simulations agree strongly with experiments.The operational window can include multiple stable states at distinct energy levels.Source: Equilibrium states of multistable… · p. 4

Dynamics and thermodynamic cycles for energy storage

  • Heating can snap capsules into a higher-energy state, while insufficient cooling leaves elastic energy stored until a solenoid pulse triggers snap-down and releases work.Snap-through heat exchange occurs much faster than the slower equilibration of internal and external temperatures.Source: Dynamics and thermodynamic cycles for… · pp. 6–7

Energy harvesting from thermal variations

  • Thermal fluctuations drive snap-through bursts whose edge motion is converted by solenoids into electrical energy and stored onboard.The cycles are hysteretic, with greater hysteresis for capsules having a larger difference between snap-force thresholds.Source: Energy harvesting from thermal… · p. 8
  • Circulating capsules crossed thermal thresholds near hot and cold reservoirs, alternated open and closed states, and completed experimentally demonstrated thermodynamic cycles.The capsules sampled a continuous temperature gradient at different phases around the loop.Source: Energy harvesting from thermal… · p. 8, Figure 5 · p. 9

Energy-aware path optimization in a thermal field

  • The planner balances locomotion cost against energy from alternating hot-cold crossings, extending range beyond initial stored energy and enabling comparable range with less onboard storage.Increasing the harvesting incentive favors routes with more harvesting events.Source: Energy-aware path optimization in a… · pp. 8–10

Discussion

The study frames thermodynamic cycles as emerging from active traversal of thermal fields, with multistable unit cells storing energy across discrete states. It presents a coupled analytical framework and identifies energy extraction, system optimization, and energy-aware planning as directions for further work.

  • Thermodynamic cycles can emerge through active traversal of environmental thermal fields, with multistable unit cells storing energy across discrete states.Temperature-driven transitions release energy through rapid instabilities that can harness energy otherwise dissipated.Source: Discussion · p. 11
  • 86.54%, 28.67%, and 66.81% are the thermodynamic efficiencies reported for the energy-storage processes in Figs. 3–5, respectively.The authors say future work will optimize system design and dynamic operation to improve efficiency.Source: Discussion · p. 11
  • Optimizing electrical extraction from rapid snap-through motion remains future work, including electromagnet design, positioning, and transient-response characterization.The authors also identify piezoelectric, triboelectric, magnetostrictive, capacitive, and hybrid transduction as alternatives to explore.Source: Discussion · p. 11
  • The authors place the system among out-of-equilibrium systems that shift gradually under external inputs until stability thresholds trigger rapid dynamical release.They identify phase transitions, stick-slip dynamics, and magnetic domain switching as possible implementation routes, with programmability spanning unit energy profiles to navigation strategies.Source: Discussion · p. 11
  • Future trajectory planning will address time-varying or partially known temperature distributions using local information, while accounting for the initial energy needed to sense and traverse gradients.Source: Discussion · p. 11

Methods

The analytical model, simulations, and experiments are documented in the Supplementary Information.

Analytical model, numerical simulations and experimental details

Additional information

The paper notes that supplementary material is available and gives a contact for materials requests.

Additional information

Supplementary information.

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