Collecting a Flock With Multiple Sub-Groups by Using Multi-Robot System

Zhang, Shuai; Pan, Jia

doi:10.1109/lra.2022.3178152

Cited by 14 publications

(8 citation statements)

References 24 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%

“…Note that although this information is collected globally, the control of each robot only uses the local information within its sensing range. A more detailed description about the SwarmBang robot system can be found in our previous work [12,13,34]. The control parameters of the searching robots are v s 0 = 20 mm/s, R s = 2 m, δ s = 0.5; the collecting robots are set as v c 0 = 10 mm/s, R c = 1 m, δ c = 0.1.…”

Section: Experiments Set-upmentioning

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: Experiments Set-upmentioning

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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“…Unlike input-affine nonlinear systems, where many standard control techniques are well-known (e.g., feedback linearization), there is no standard control technique for input-nonaffine systems. As a consequence, herding solutions are usually particular to the specific dynamics they consider [20] or assume linear dynamics to facilitate the control [14]. Implicit Control [15] is a novel general control technique for input-nonaffine systems, which allows to address the herding for general applications.…”

Section: Introductionmentioning

confidence: 99%

Multi-robot Implicit Control of Herds

Sebastián

Montijano

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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This paper solves the problem of herding countless evaders by means of a few robots. The objective is to steer all the evaders towards a desired tracking reference while avoiding escapes. The problem is very challenging due to the highly complex repulsive evaders' dynamics and the underdetermined states to control. We propose a solution that is based on Implicit Control and a novel dynamic assignment strategy to select the evaders to be directly controlled. The former is a general technique that explicitly computes control inputs even in highly complex input-nonaffine dynamics. The latter is built upon a convex-hull dynamic clustering inspired by the Voronoi tessellation problem. The combination of both allows to choose the best evaders to directly control, while the others are indirectly controlled by exploiting the repulsive interactions among them. Simulations show that massive herds can be herd throughout complex patterns by means of a few herders.

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Collecting a Flock With Multiple Sub-Groups by Using Multi-Robot System

Cited by 14 publications

References 24 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

Multi-robot Implicit Control of Herds

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