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Alternation emerges as a multi-modal strategy for turbulent odor navigation.

ABSTRACT: Foraging mammals exhibit a familiar yet poorly characterized phenomenon, 'alternation', a pause to sniff in the air preceded by the animal rearing on its hind legs or raising its head. Rodents spontaneously alternate in the presence of airflow, suggesting that alternation serves an important role during plume-tracking. To test this hypothesis, we combine fully resolved simulations of turbulent odor transport and Bellman optimization methods for decision-making under partial observability. We show that an agent trained to minimize search time in a realistic odor plume exhibits extensive alternation together with the characteristic cast-and-surge behavior observed in insects. Alternation is linked with casting and occurs more frequently far downwind of the source, where the likelihood of detecting airborne cues is higher relative to ground cues. Casting and alternation emerge as complementary tools for effective exploration with sparse cues. A model based on marginal value theory captures the interplay between casting, surging, and alternation.

SUBMITTER: Rigolli N

PROVIDER: S-EPMC9489216 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Alternation emerges as a multi-modal strategy for turbulent odor navigation.

Rigolli Nicola N Reddy Gautam G Seminara Agnese A Vergassola Massimo M

eLife 20220823

Foraging mammals exhibit a familiar yet poorly characterized phenomenon, 'alternation', a pause to sniff in the air preceded by the animal rearing on its hind legs or raising its head. Rodents spontaneously alternate in the presence of airflow, suggesting that alternation serves an important role during plume-tracking. To test this hypothesis, we combine fully resolved simulations of turbulent odor transport and Bellman optimization methods for decision-making under partial observability. We sho ...[more]

PMID: 35996954

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Project description:Path planning is a crucial component of unmanned driving systems designed to address the challenge of autonomous navigation. This article introduces a novel approach called the multi-strategy cooperative enhanced dung beetle optimization algorithm (RCDBO) and applies it to dispose of path planning issues. Its primary aim is to mitigate the shortcomings of the fundamental dung beetle optimization algorithm (DBO), namely, its tendency to prematurely converge to local optima and its limited global planning capability. Initially, population initialization leverages Bernoulli-based chaotic mapping to enhance diversity and ensure uniform randomness, thereby improving both the quality of initial solutions and optimization efficiency. Subsequently, a random walk strategy is employed to perturb the rolling behavior of the dung beetle population during the initial stage, thus mitigating potential algorithmic local stagnation. Finally, adopting a vertical and horizontal crossover strategy introduces perturbations to the current best value of the dung beetle population, thereby enhancing the DBO method's global optimization capability during the later stages of evolution. The RCDBO method was evaluated against several well-established swarm intelligence algorithms using twelve benchmark test functions, the CEC2021 test suite, the Wilcoxon rank-sum test, and the Friedman test, to rigorously assess its feasibility. Additionally, the effectiveness of each strategy within the RCDBO framework was validated, and the algorithm's capability to achieve an optimal balance between global exploration and local exploitation was systematically analyzed through the CEC2021 test suite. Furthermore, in path planning simulation experiments targeting maps of varying sizes and complexities, the RCDBO algorithm, compared to the basic DBO algorithm, results in shorter planned path lengths. In simpler map environments, it exhibits fewer turning points and smoother paths, indicating that the RCDBO algorithm also possesses certain advantages in addressing path-planning challenges.

Dataset Information

Alternation emerges as a multi-modal strategy for turbulent odor navigation.

Publications

Alternation emerges as a multi-modal strategy for turbulent odor navigation.

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