Self-organized multi-target trapping of swarm robots with density-based interaction

Lei, Xiaokang; Zhang, Shuai; Xiang, Yalun; Duan, Mengyuan

doi:10.1007/s40747-023-01014-6

Cited by 8 publications

(8 citation statements)

References 46 publications

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“…There is still an insufficient investigation and utilization of density information to reflect the group-level configurations. In our previous work [12,13,34], we use a novel density-based framework to design the control policy of swarm robots; these studies showed that density-based interactions can endow the swarm robots with various unique capabilities, such as spontaneously forming multiple-/single-ring configurations and symmetric shrinking and expanding [34]. These backgrounds motivate us to address the TSC task by using the density-based control framework for swarm robots.…”

Section: Motivation and Contributionsmentioning

confidence: 99%

“…To estimate the spatial density field of objects (i.e., the robots and targets) by our swarm robots, we adopt the kernel-function-based definition of spatial density from our previous works [13,34]. For an object i with position X , a locally spatial density field is attached to its neighborhood, and the intensity (i.e., density) of this spatial-relevant field decays monotonically with increasing distance from the object.…”

Section: Spatial Density Estimationmentioning

confidence: 99%

“…In our earlier studies [13,34], based on the concept of the spatial density field, we proposed a novel density-based control framework for swarm robotics. In [13], we used the density-based control framework to achieve swarm robots' trapping of multiple targets, and the properties of the density-based control framework are analyzed in detail in [14]. Here, we take the searching robots as an example to briefly review the framework.…”

Section: Density-driven Control Framework For Swarm Robotsmentioning

confidence: 99%

“…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%

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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%

Section: Spatial Density Estimationmentioning

confidence: 99%

Section: Density-driven Control Framework For Swarm Robotsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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“…Due to their behavior being regulated based on the distributed control framework using the local sensing information, the collective behavior of swarm robots is self-organizing and emerging; it features strong robustness, scalability, flexibility, and adaptability [ 1 ]. These excellent characteristics enable them to serve a wide range of challenging applications, such as target search [ 2 ], collective transport [ 3 ], multi-target trapping [ 4 ], object collection [ 5 ], etc. Recently, the field of swarm robotics has attracted a lot of attention, from theoretical approaches to various applications.…”

Section: Introductionmentioning

confidence: 99%

A Minimalist Self-Localization Approach for Swarm Robots Based on Active Beacon in Indoor Environments

Duan

Lei

Duan

et al. 2023

Sensors

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When performing indoor tasks, miniature swarm robots are suffered from their small size, poor on-board computing power, and electromagnetic shielding of buildings, which means that some traditional localization methods, such as global positioning system (GPS), simultaneous localization and mapping (SLAM), and ultra-wideband (UWB), cannot be employed. In this paper, a minimalist indoor self-localization approach for swarm robots is proposed based on active optical beacons. A robotic navigator is introduced into a swarm of robots to provide locally localization services by actively projecting a customized optical beacon on the indoor ceiling, which contains the origin and the reference direction of localization coordinates. The swarm robots observe the optical beacon on the ceiling via a bottom-up-view monocular camera, and extract the beacon information on-board to localize their positions and headings. The uniqueness of this strategy is that it uses the flat, smooth, and well-reflective ceiling in the indoor environment as a ubiquitous plane for displaying the optical beacon; meanwhile, the bottom-up view of swarm robots is not easily blocked. Real robotic experiments are conducted to validate and analyze the localization performance of the proposed minimalist self-localization approach. The results show that our approach is feasible and effective, and can meet the needs of swarm robots to coordinate their motion. Specifically, for the stationary robots, the average position error and heading error are 2.41 cm and 1.44°; when the robots are moving, the average position error and heading error are less than 2.40 cm and 2.66°.

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A flocking control algorithm of multi-agent systems based on cohesion of the potential function

Li,

Yang,

Jiang

et al. 2023

Complex Intell. Syst.

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Flocking cohesion is critical for maintaining a group’s aggregation and integrity. Designing a potential function to maintain flocking cohesion unaffected by social distance is challenging due to the uncertainty of real-world conditions and environments that cause changes in agents’ social distance. Previous flocking research based on potential functions has primarily focused on agents’ same social distance and the attraction–repulsion of the potential function, ignoring another property affecting flocking cohesion: well depth, as well as the effect of changes in agents’ social distance on well depth. This paper investigates the effect of potential function well depths and agent’s social distances on the multi-agent flocking cohesion. Through the analysis, proofs, and classification of these potential functions, we have found that the potential function well depth is proportional to the flocking cohesion. Moreover, we observe that the potential function well depth varies with the agents’ social distance changes. Therefore, we design a segmentation potential function and combine it with the flocking control algorithm in this paper. It enhances flocking cohesion significantly and has good robustness to ensure the flocking cohesion is unaffected by variations in the agents’ social distance. Meanwhile, it reduces the time required for flocking formation. Subsequently, the Lyapunov theorem and the LaSalle invariance principle prove the stability and convergence of the proposed control algorithm. Finally, this paper adopts two subgroups with different potential function well depths and social distances to encounter for simulation verification. The corresponding simulation results demonstrate and verify the effectiveness of the flocking control algorithm.

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Self-organized multi-target trapping of swarm robots with density-based interaction

Cited by 8 publications

References 46 publications

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

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

A Minimalist Self-Localization Approach for Swarm Robots Based on Active Beacon in Indoor Environments

A flocking control algorithm of multi-agent systems based on cohesion of the potential function

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