Showing 1–7 of 7 results for author: Piro, L

Nonequilibrium energetics of sensing and actuation by a smart active particle

Authors: Luca Cocconi, Benoît Mahault, Lorenzo Piro

Abstract: Smart active agents must allocate finite energetic resources across distinct functions, yet the underlying thermodynamic trade-offs remain poorly understood. Here, we introduce a minimal model of a self-steering particle with an internal polarity-cue sensor coupled to an external environmental field, decomposing its steady-state entropy production rate into locomotion, actuation, and sensing costs… ▽ More Smart active agents must allocate finite energetic resources across distinct functions, yet the underlying thermodynamic trade-offs remain poorly understood. Here, we introduce a minimal model of a self-steering particle with an internal polarity-cue sensor coupled to an external environmental field, decomposing its steady-state entropy production rate into locomotion, actuation, and sensing costs. This separation exposes an energetic bookkeeping structure underlying even the simplest form of embodied navigation. The emergence of Pareto fronts linking energetic expenditure to localisation precision and path-following performance shows that feedback-controlled active motion is constrained by quantitative thermodynamic bounds that persist across distinct task geometries. △ Less

Submitted 16 March, 2026; originally announced March 2026.

Comments: 19 pages, 5 figures

Adaptive shape control for microswimmer navigation in turbulence

Authors: Jingran Qiu, Lorenzo Piro, Luca Biferale, Massimo Cencini, Bernhard Mehlig, Kristian Gustavsson

Abstract: Navigation in turbulent environments is a fundamental challenge for biological and artificial microswimmers. While most existing studies focus on adapting motility or steering, the role of active morphological changes in navigation remains poorly explored. Here, we investigate a shape-changing spheroidal microswimmer tasked with maximising its displacement from an initial position in two-dimension… ▽ More Navigation in turbulent environments is a fundamental challenge for biological and artificial microswimmers. While most existing studies focus on adapting motility or steering, the role of active morphological changes in navigation remains poorly explored. Here, we investigate a shape-changing spheroidal microswimmer tasked with maximising its displacement from an initial position in two-dimensional stochastic and turbulent flows. Using reinforcement learning (RL), the microswimmer learns to adapt its aspect ratio based on its orientation and local velocity-gradient signals. The learned strategies outperform fixed-shape and short-time-optimal baselines across different flow regimes and remain effective when transferred from stochastic flows to fully resolved turbulence. Guided by the learned policies, we propose a minimal analytical model that captures the essential navigation mechanisms and reproduces the performance across flow regimes. These results show that adaptive morphology provides a robust and physically interpretable control paradigm for microswimmer navigation in complex flows. △ Less

Submitted 9 March, 2026; originally announced March 2026.

Dissipation-accuracy tradeoffs in autonomous control of smart active matter

Authors: Luca Cocconi, Benoît Mahault, Lorenzo Piro

Abstract: The study of motility control by smart agents offers a promising platform for systematically exploring the fundamental physical constraints underlying the functioning of bio-inspired micro-machines operating far from equilibrium. Here, we address the question of the energy cost required for a self-steering active agent to localise itself within a specific region of space or follow a pre-defined tr… ▽ More The study of motility control by smart agents offers a promising platform for systematically exploring the fundamental physical constraints underlying the functioning of bio-inspired micro-machines operating far from equilibrium. Here, we address the question of the energy cost required for a self-steering active agent to localise itself within a specific region of space or follow a pre-defined trajectory under the influence of fluctuations and external flows. Building on a stochastic thermodynamic formulation of the problem, we derive a generic relationship between dissipation and localisation accuracy, which reveals a fundamental dissipation-accuracy tradeoff constraining the agent's performance. In addition, we illustrate how our framework enables the derivation of optimal steering policies that achieve localisation at minimum energy expenditure. △ Less

Submitted 19 September, 2024; originally announced September 2024.

Comments: 32 pages (18 in main text), 8 figures

Energetic cost of microswimmer navigation: the role of body shape

Authors: Lorenzo Piro, Andrej Vilfan, Ramin Golestanian, Benoît Mahault

Abstract: We study the energetic efficiency of navigating microswimmers by explicitly taking into account the geometry of their body. We show that, as their shape transitions from prolate to oblate, non-steering microswimmers rotated by flow gradients naturally follow increasingly time-optimal trajectories. At the same time, they also require larger dissipation to swim. The coupling between body geometry an… ▽ More We study the energetic efficiency of navigating microswimmers by explicitly taking into account the geometry of their body. We show that, as their shape transitions from prolate to oblate, non-steering microswimmers rotated by flow gradients naturally follow increasingly time-optimal trajectories. At the same time, they also require larger dissipation to swim. The coupling between body geometry and hydrodynamics thus leads to a generic trade-off between the energetic costs associated with propulsion and navigation, which is accompanied by the selection of a finite optimal aspect ratio. We derive from optimal control theory the steering policy ensuring overall minimum energy dissipation, and characterize how navigation performances vary with the swimmer shape. Our results highlight the important role of the swimmer geometry in realistic navigation problems. △ Less

Submitted 14 July, 2023; originally announced July 2023.

Journal ref: Phys. Rev. Research 6, 013274 (2024)

Optimal navigation of microswimmers in complex and noisy environments

Authors: Lorenzo Piro, Benoît Mahault, Ramin Golestanian

Abstract: We design new navigation strategies for travel time optimization of microscopic self-propelled particles in complex and noisy environments. In contrast to strategies relying on the results of optimal control theory, these protocols allow for semi-autonomous navigation as they do not require control over the microswimmer motion via external feedback loops. Although the strategies we propose rely on… ▽ More We design new navigation strategies for travel time optimization of microscopic self-propelled particles in complex and noisy environments. In contrast to strategies relying on the results of optimal control theory, these protocols allow for semi-autonomous navigation as they do not require control over the microswimmer motion via external feedback loops. Although the strategies we propose rely on simple principles, they show arrival time statistics strikingly similar to those obtained from stochastic optimal control theory, as well as performances that are robust to environmental changes and strong fluctuations. These features, as well as their applicability to more general optimization problems, make these strategies promising candidates for the realization of optimized semi-autonomous navigation. △ Less

Submitted 3 April, 2022; originally announced April 2022.

Optimal navigation strategies for microswimmers on curved manifolds

Authors: Lorenzo Piro, Evelyn Tang, Ramin Golestanian

Abstract: Finding the fastest path to a desired destination is a vitally important task for microorganisms moving in a fluid flow. We study this problem by building an analytical formalism for overdamped microswimmers on curved manifolds and arbitrary flows. We show that the solution corresponds to the geodesics of a Randers metric, which is an asymmetric Finsler metric that reflects the irreversible charac… ▽ More Finding the fastest path to a desired destination is a vitally important task for microorganisms moving in a fluid flow. We study this problem by building an analytical formalism for overdamped microswimmers on curved manifolds and arbitrary flows. We show that the solution corresponds to the geodesics of a Randers metric, which is an asymmetric Finsler metric that reflects the irreversible character of the problem. Using the example of a spherical surface, we demonstrate that the swimmer performance that follows this "Randers policy" always beats a more direct policy. A study of the shape of isochrones reveals features such as self-intersections, cusps, and abrupt nonlinear effects. Our work provides a link between microswimmer physics and geodesics in generalizations of general relativity. △ Less

Submitted 15 October, 2020; originally announced October 2020.

Journal ref: Phys. Rev. Research 3, 023125 (2021)

Collective olfactory search in a turbulent environment

Authors: Mihir Durve, Lorenzo Piro, Massimo Cencini, Luca Biferale, Antonio Celani

Abstract: Finding the distant source of an odor dispersed by a turbulent flow is a vital task for many organisms, either for foraging or for mating purposes. At the level of individual search, animals like moths have developed effective strategies to solve this very difficult navigation problem based on the noisy detection of odor concentration and wind velocity alone. When many individuals concurrently p… ▽ More Finding the distant source of an odor dispersed by a turbulent flow is a vital task for many organisms, either for foraging or for mating purposes. At the level of individual search, animals like moths have developed effective strategies to solve this very difficult navigation problem based on the noisy detection of odor concentration and wind velocity alone. When many individuals concurrently perform the same olfactory search task, without any centralized control, sharing information about the decisions made by the members of the group can potentially increase the performance. But how much of this information is actually valuable and exploitable for the collective task ? Here we show that, in a model of a swarm of agents inspired by moth behavior, there is an optimal way to blend the private information about odor and wind detections with the publicly available information about other agents' heading direction. At optimality, the time required for the first agent to reach the source is essentially the shortest flight time from the departure point to the target. Conversely, agents who discard public information are several fold slower and groups that do not put enough weight on private information perform even worse. Our results then suggest an efficient multi-agent olfactory search algorithm that could prove useful in robotics, for instance in the identification of sources of harmful volatile compounds. △ Less

Submitted 21 November, 2019; originally announced November 2019.

Journal ref: Phys. Rev. E 102, 012402 (2020)