ArticlePloS one2023
Collective evolution learning model for vision-based collective motion with collision avoidance.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Allocentric flocking.Nature communications · 2025Article
- Background optic flow modulates responses of multiple descending interneurons to object motion in locusts.PloS one · 2025Article
- Exploration-exploitation model of moth-inspired olfactory navigation.Journal of the Royal Society, Interface · 2024Article
- Anisotropic interaction and motion states of locusts in a hopper band.Proceedings. Biological sciences · 2024Article
- Mathematical modeling of BCG-based bladder cancer treatment using socio-demographics.Scientific reports · 2023Article
- A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge.Scientific reports · 2023Article
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Collective motion (CM) takes many forms in nature; schools of fish, flocks of birds, and swarms of locusts to name a few. Commonly, during CM the individuals of the group avoid collisions. These CM and collision avoidance (CA) behaviors are based on input from the environment such as smell, air pressure, and vision, all of which are processed by the individual and defined action. In this work, a novel vision-based CM with CA model (i.e., VCMCA) simulating the collective evolution learning process is proposed. In this setting, a learning agent obtains a visual signal about its environment, and throughout trial-and-error over multiple attempts, the individual learns to perform a local CM with CA which emerges into a global CM with CA dynamics. The proposed algorithm was evaluated in the case of locusts' swarms, showing the evolution of these behaviors in a swarm from the learning process of the individual in the swarm. Thus, this work proposes a biologically-inspired learning process to obtain multi-agent multi-objective dynamics.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.