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Saavedra R, Gompper G, Ripoll M. Swirling Due to Misaligned Perception-Dependent Motility. PHYSICAL REVIEW LETTERS 2024; 132:268301. [PMID: 38996279 DOI: 10.1103/physrevlett.132.268301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 05/21/2024] [Indexed: 07/14/2024]
Abstract
A system of particles with motility variable in terms of a vision-type of perception is investigated by a combination of Langevin dynamics simulations in two-dimensional systems and an analytical approach based on conservation law principles. Persistent swirling with predetermined direction is here induced by differentiating the self-propulsion direction and the perception cone axis. Clusters can have a fluidlike center with a rotating outer layer or display a solidlike rotation driven by the outer layer activity. Discontinuous motility with misaligned perception might therefore serve as a powerful self-organization strategy in microrobots.
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Rudyak VY, Lopushenko A, Palyulin VV, Chertovich AV. Long-range ordering of velocity-aligned active polymers. J Chem Phys 2024; 160:044905. [PMID: 38275191 DOI: 10.1063/5.0181252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 12/26/2023] [Indexed: 01/27/2024] Open
Abstract
In this work, we study the effect of covalent bonding on the behavior of non-equilibrium systems with the active force acting on particles along their velocity. Self-ordering of single particles does not occur in this model. However, starting from some critical polymerization degree, the ordered state is observed. It is homogeneous and exhibits no phase separation. In the ordered state, the chains prefer a near-two-dimensional configuration and all move in one direction. Importantly, the self-ordering is obtained only at intermediate active force magnitudes. At high magnitudes, the transition from the disordered to ordered state is suppressed by the swelling of the chains during the transition, as we show by the transition kinetics analysis. We demonstrate the bistable behavior of the system in a particular range of polymerization degrees, amplitudes of active force, densities, and thermostat temperatures. Overall, we show that covalent bonding greatly aids the self-ordering in this active particle model, in contrast to active Brownian particles.
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Affiliation(s)
- Vladimir Yu Rudyak
- Semenov Federal Research Center for Chemical Physics, Kosygina, 4, 119991 Moscow, Russia
| | - Alexander Lopushenko
- Semenov Federal Research Center for Chemical Physics, Kosygina, 4, 119991 Moscow, Russia
| | - Vladimir V Palyulin
- Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, 121205 Moscow, Russia
| | - Alexander V Chertovich
- Semenov Federal Research Center for Chemical Physics, Kosygina, 4, 119991 Moscow, Russia
- Faculty of Physics, Lomonosov Moscow State University, Moscow 119991, Russia
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Masmitja I, Martin M, O'Reilly T, Kieft B, Palomeras N, Navarro J, Katija K. Dynamic robotic tracking of underwater targets using reinforcement learning. Sci Robot 2023; 8:eade7811. [PMID: 37494462 DOI: 10.1126/scirobotics.ade7811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 06/26/2023] [Indexed: 07/28/2023]
Abstract
To realize the potential of autonomous underwater robots that scale up our observational capacity in the ocean, new techniques are needed. Fleets of autonomous robots could be used to study complex marine systems and animals with either new imaging configurations or by tracking tagged animals to study their behavior. These activities can then inform and create new policies for community conservation. The role of animal connectivity via active movement of animals represents a major knowledge gap related to the distribution of deep ocean populations. Tracking underwater targets represents a major challenge for observing biological processes in situ, and methods to robustly respond to a changing environment during monitoring missions are needed. Analytical techniques for optimal sensor placement and path planning to locate underwater targets are not straightforward in such cases. The aim of this study was to investigate the use of reinforcement learning as a tool for range-only underwater target-tracking optimization, whose promising capabilities have been demonstrated in terrestrial scenarios. To evaluate its usefulness, a reinforcement learning method was implemented as a path planning system for an autonomous surface vehicle while tracking an underwater mobile target. A complete description of an open-source model, performance metrics in simulated environments, and evaluated algorithms based on more than 15 hours of at-sea field experiments are presented. These efforts demonstrate that deep reinforcement learning is a powerful approach that enhances the abilities of autonomous robots in the ocean and encourages the deployment of algorithms like these for monitoring marine biological systems in the future.
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Affiliation(s)
- I Masmitja
- Institut de Ciències del Mar (ICM), CSIC, Barcelona 95062, Spain
- Research and Development, Bioinspiration Lab, Monterey Bay Aquarium Research Institute MBARI, Moss Landing, CA 95062, USA
| | - M Martin
- Knowledge Engineering and Machine Learning Group, Universitat Politècnica de Catalunya, Barcelona Tech., Barcelona 08034, Spain
- Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - T O'Reilly
- Research and Development, Bioinspiration Lab, Monterey Bay Aquarium Research Institute MBARI, Moss Landing, CA 95062, USA
| | - B Kieft
- Research and Development, Bioinspiration Lab, Monterey Bay Aquarium Research Institute MBARI, Moss Landing, CA 95062, USA
| | - N Palomeras
- Computer vision and Robotics Institute, Universitat de Girona, Girona 17003, Spain
| | - J Navarro
- Institut de Ciències del Mar (ICM), CSIC, Barcelona 95062, Spain
| | - K Katija
- Research and Development, Bioinspiration Lab, Monterey Bay Aquarium Research Institute MBARI, Moss Landing, CA 95062, USA
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Monter S, Heuthe VL, Panizon E, Bechinger C. Dynamics and risk sharing in groups of selfish individuals. J Theor Biol 2023; 562:111433. [PMID: 36738824 PMCID: PMC10020420 DOI: 10.1016/j.jtbi.2023.111433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 01/24/2023] [Accepted: 01/29/2023] [Indexed: 02/05/2023]
Abstract
Understanding why animals organize in collective states is a central question of current research in, e.g., biology, physics, and psychology. More than 50 years ago, W.D. Hamilton postulated that the formation of animal herds may simply result from the individual's selfish motivation to minimize their predation risk. The latter is quantified by the domain of danger (DOD) which is given by the Voronoi area around each individual. In fact, simulations show that individuals aiming to reduce their DODs form compact groups similar to what is observed in many living systems. However, despite the apparent simplicity of this problem, it is not clear what motional strategy is required to find an optimal solution. Here, we use the framework of Multi Agent Reinforcement Learning (MARL) which gives the unbiased and optimal strategy of individuals to solve the selfish herd problem. We demonstrate that the motivation of individuals to reduce their predation risk naturally leads to pronounced collective behaviors including the formation of cohesive swirls. We reveal a previously unexplored rather complex intra-group motion which eventually leads to a evenly shared predation risk amongst selfish individuals.
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Affiliation(s)
- Samuel Monter
- University of Konstanz, Department of Physics, Universtaetsstrasse 10, Konstanz, 78464, Germany
| | - Veit-Lorenz Heuthe
- University of Konstanz, Department of Physics, Universtaetsstrasse 10, Konstanz, 78464, Germany; Centre for the Advanced Study of Collective Behaviour, Universtaetsstrasse 10, Konstanz, 78464, Germany
| | - Emanuele Panizon
- The Abdus Salam International Centre for Theoretical Physics (ICTP), Strada Costiera 11 Trieste, 34151, Italy
| | - Clemens Bechinger
- University of Konstanz, Department of Physics, Universtaetsstrasse 10, Konstanz, 78464, Germany; Centre for the Advanced Study of Collective Behaviour, Universtaetsstrasse 10, Konstanz, 78464, Germany.
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Effects of shady environments on fish collective behavior. Sci Rep 2022; 12:17873. [PMID: 36284154 PMCID: PMC9596401 DOI: 10.1038/s41598-022-22515-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 10/17/2022] [Indexed: 01/20/2023] Open
Abstract
Despite significant efforts devoted to understanding the underlying complexity and emergence of collective movement in animal groups, the role of different external settings on this type of movement remains largely unexplored. Here, by combining time series analysis and complex network tools, we present an extensive investigation of the effects of shady environments on the behavior of a fish species (Silver Carp Hypophthalmichthys molitrix) within earthen ponds. We find that shade encourages fish residence during daylight hours, but the degree of preference for shade varies substantially among trials and ponds. Silver Carp are much slower and exhibit lower persistence in their speeds when under shade than out of it during daytime and nighttime, with fish displaying the highest persistence degree and speeds at night. Furthermore, our research shows that shade affects fish schooling behavior by reducing their polarization, number of interactions among individuals, and the stability among local neighbors; however, fish keep a higher local degree of order when under shade compared to nighttime positions.
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