Group chase and escape with prey's anti-attack behavior

Zhang, Shuai; Liu, Mingyong; Lei, Xiaokang; Yang, Panpan; Huang, Yunke; Clark, Ruaridh

doi:10.1016/j.physleta.2019.125871

Cited by 9 publications

(3 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We consider this grouping as defensive aggregation, a widely observed antiattack behavior in prey species ( 27 , 28 ). To date, this pattern has been considered as a form of self-organization that derives from each individual’s preference to avoid harmful encounters, which enable effective defense against predators ( 29 ), but the brain activity of individuals within these groups has not been explored in this context. We compared this defensive aggregation with huddling during baseline ( Fig.…”

Section: Resultsmentioning

confidence: 99%

A bird’s-eye view of brain activity in socially interacting mice through mobile edge computing (MEC)

Kim

Han

et al. 2020

Sci. Adv.

View full text Add to dashboard Cite

Social cognition requires neural processing, yet a unifying method linking particular brain activities and social behaviors is lacking. Here, we embedded mobile edge computing (MEC) and light emitting diodes (LEDs) on a neurotelemetry headstage, such that a particular neural event of interest is processed by the MEC and subsequently an LED is illuminated, allowing simultaneous temporospatial visualization of that neural event in multiple, socially interacting mice. As a proof of concept, we configured our system to illuminate an LED in response to gamma oscillations in the basolateral amygdala (BLA gamma) in freely moving mice. We identified (i) BLA gamma responses to a spider robot, (ii) affect-related BLA gamma during conflict, and (iii) formation of defensive aggregation under a threat by the robot, and reduction of BLA gamma responses in the inner-located mice. Our system can provide an intuitive framework for examining brain-behavior connections in various ecological situations and population structures.

show abstract

Section: Resultsmentioning

confidence: 99%

A bird’s-eye view of brain activity in socially interacting mice through mobile edge computing (MEC)

Kim

Han

et al. 2020

Sci. Adv.

View full text Add to dashboard Cite

show abstract

“…Hunting in groups involves two main actions: chasing and escaping (Shuai . The process of group chasing and escaping depends crucially on the differences in the number of predators to prey (Packer and Ruttan 1988;Shuai Zhang et al 2019). In a more realistic and dynamically perplexing case is when a group of targets escapes from many chasers (Kamimura and Ohira 2019), the chasers cooperate among themselves and create packs to trap the flock of targets.…”

Section: Chase and Escape Processesmentioning

confidence: 99%

Chase and Escape Optimization Algorithm

Eesa

2022

Preprint

View full text Add to dashboard Cite

This paper proposes a new optimization algorithm called Chase and Escape (CHE). The proposed CHE is based on a hunting strategy for a group of predators attacking a group of prey. The mathematical model of CHE algorithm is built on three phases. First, the predators begin by approaching and encircling the prey from all sides. The second phase is the attacking process, where the predator decides to attack the prey. Third, is the escape process, where the prey assesses the level of threat and decides whether to escape or remain in its alert zone. To assess the performance of the proposed algorithm, it is tested and evaluated on CEC-2017 benchmark functions, and its performance is compared to different cutting-edge optimization algorithms. Experimental results and Friedman mean rank test reveal that the proposed CHE outperforms the majority of the other algorithms. Furthermore, the proposed algorithm CHE is applied to four real-world engineering problems, and the results are compared with the state-of-the-art literature. The comparative results revealed that the proposed CHE algorithm is quite promising, producing better results than most of the similar recent work in the literature.

show abstract

“…Correspondingly, the swarm robots enable a group of robots to coordinate their actions and cooperate with each other without relying on a central controller or a global communication network. This feature makes swarm robots suitable for various applications that require scalability, robustness, and flexibility, such as target search [6][7][8], target handling [9][10][11], multiple subgroups collecting [12], multi-target trapping [13], group chase [14,15], invasion and defense [16,17], etc. The great advantages of swarm robots provide us with a new way to solve the TSC task.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Group chase and escape with prey's anti-attack behavior

Cited by 9 publications

References 37 publications

A bird’s-eye view of brain activity in socially interacting mice through mobile edge computing (MEC)

A bird’s-eye view of brain activity in socially interacting mice through mobile edge computing (MEC)

Chase and Escape Optimization Algorithm

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

Contact Info

Product

Resources

About