Nest characteristics and food supply affect reproductive output of white storks Ciconia ciconia in semi-arid areas

Benharzallah, Naouel; Bachir, Abdelkrim Si; Barbraud, Christophe

doi:10.1007/s11756-022-01011-z

Cited by 4 publications

(1 citation statement)

References 57 publications

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“…On the other hand, animals can draw on spatial density or concentration signals as the mediator to drive behavioral adjustments or make decisions. For example, ants change their colony structure based on pheromone concentration [31] and optimize nest site selection according to the density of food sources [32]; ants, as a team, deposit soil wastes in constructing nests also according to the density of wastes already present [33]. As the primary driving factor of these behaviors, the density information characterizes the spatial distribution of objects, and it plays a crucial role in animals' decision making.…”

Section: Motivation and Contributionsmentioning

confidence: 99%

Self-Organized Patchy Target Searching and Collecting with Heterogeneous Swarm Robots Based on Density Interactions

et al. 2023

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The issue of searching and collecting targets with patchy distribution in an unknown environment is a challenging task for multiple or swarm robots because the targets are unevenly dispersed in space, which makes the traditional solutions based on the idea of path planning and full spatial coverage very inefficient and time consuming. In this paper, by employing a novel framework of spatial-density-field-based interactions, a collective searching and collecting algorithm for heterogeneous swarm robots is proposed to solve the challenging issue in a self-organized manner. In our robotic system, two types of swarm robots, i.e., the searching robots and the collecting robots, are included. To start with, the searching robots conduct an environment exploration by means of formation movement with Levy flights; when the targets are detected by the searching robots, they spontaneously form a ring-shaped envelope to estimate the spatial distribution of targets. Then, a single robot is selected from the group to enter the patch and locates at the patch’s center to act as a guiding beacon. Subsequently, the collecting robots are recruited by the guiding beacon to gather the patch targets; they first form a ring-shaped envelope around the target patch and then push the scattered targets inward by using a spiral shrinking strategy; in this way, all targets eventually are stacked near the center of the target patch. With the cooperation of the searching robots and the collecting robots, our heterogeneous robotic system can operate autonomously as a coordinated group to complete the task of collecting targets in an unknown environment. Numerical simulations and real swarm robot experiments (up to 20 robots are used) show that the proposed algorithm is feasible and effective, and it can be extended to search and collect different types of targets with patchy distribution.

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Section: Motivation and Contributionsmentioning

confidence: 99%