Search and rescue with sparsely connected swarms

Dah-Achinanon, Ulrich; Bajestani, Seyed Ehsan Marjani; Lajoie, Pierre-Yves; Beltrame, Giovanni

doi:10.1007/s10514-022-10080-7

Cited by 4 publications

(3 citation statements)

References 64 publications

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“…This reduces the on-board processing load on the individual robots, allowing them to focus on localised mapping and navigation. In addition, the centralised server provides a uni ed coordinate framework for map fusion and robot pose estimation, which helps to mitigate inconsistency and drift problems in distributed SLAM approaches [14].…”

Section: Server-based Centralized Swarm Robot Systemmentioning

confidence: 99%

SLAM in Weak Environment Information Applications using Swarm Robots

Guo,

Li,

Qiu

2024

Preprint

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Simultaneous Localisation and Mapping (SLAM) in weak environment information scenarios where there are only sparse and ambiguous references available presents significant challenges. Feature sparsity and repetitive elements of landmarks impose difficulties in establishing stable tracking and lead perception overlap. The point-line matching based approach presented in this paper provides additional geometric context that makes it easier to distinguish between similar features. This method identifies landmarks as points, extrapolates lines from consecutive point observations, and generates point-line pairs using geometric and appearance-based criteria. The pairs serve as unique identifiers for locations and, hence, effectively solves the identical perceptions problem. To optimize this solution further, we introduce the generation of trajectory graphs, allowing the swarm to refine its map and location estimates iteratively. Experimental results validate that our approach outperforms traditional methods in the challenging weak environment information applications.

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Section: Server-based Centralized Swarm Robot Systemmentioning

confidence: 99%

SLAM in Weak Environment Information Applications using Swarm Robots

Guo,

Li,

Qiu

2024

Preprint

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“…Robotic swarms consist of a distributed cognitive network promising to enhance tasks in terrestrial, aerial, and aquatic environments [11], [12]. Robotic swarms are currently being used in a variety of application domains such as agriculture [13], search and rescue [14], monitoring and patrol [15], warehouse operation [16], last-mile delivery [17], military scenarios [18], environmental monitoring [19], and space exploration [20]. The move from multi-UAVs to those of greater numbers is challenging, as the ability of a human operator to manage each drone individually becomes less feasible as the swarm size increases [21], [5].…”

Section: Related Workmentioning

confidence: 99%

The Effect of Data Visualisation Quality and Task Density on Human-Swarm Interaction

Abioye,

Naiseh,

Hunt

et al. 2023

2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators.In this paper, we study the human factors aspect of the humanswarm interaction and investigate how having access to highquality data can affect the performance of the human-swarm system-the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.

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“…In recent years, with the increase in mission complexity and the enhancement of UAV autonomy, unmanned aircraft swarm systems (UAS) have gradually received attention from researchers. Drone swarms have been used for target search and rescue [1,2], cluster operation [3,4], and target tracking [5,6]. Within the military domain, coordinated reconnaissance search attacks by UAV swarms in unknown environments is an important application.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Collaborative Search and Attack Mission Decision-Making in Unknown Environments

Liang,

Li,

2023

Sensors

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To address the challenge of coordinated combat involving multiple UAVs in reconnaissance and search attacks, we propose the Multi-UAV Distributed Self-Organizing Cooperative Intelligence Surveillance and Combat (CISCS) strategy. This strategy employs distributed control to overcome issues associated with centralized control and communication difficulties. Additionally, it introduces a time-constrained formation controller to address the problem of unstable multi-UAV formations and lengthy formation times. Furthermore, a multi-task allocation algorithm is designed to tackle the issue of allocating multiple tasks to individual UAVs, enabling autonomous decision-making at the local level. The distributed self-organized multi-UAV cooperative reconnaissance and combat strategy consists of three main components. Firstly, a multi-UAV finite time formation controller allows for the rapid formation of a mission-specific formation in a finite period. Secondly, a multi-task goal assignment module generates a task sequence for each UAV, utilizing an improved distributed Ant Colony Optimization (ACO) algorithm based on Q-Learning. This module also incorporates a colony disorientation strategy to expand the search range and a search transition strategy to prevent premature convergence of the algorithm. Lastly, a UAV obstacle avoidance module considers internal collisions and provides real-time obstacle avoidance paths for multiple UAVs. In the first part, we propose a formation algorithm in finite time to enable the quick formation of multiple UAVs in a three-dimensional space. In the second part, an improved distributed ACO algorithm based on Q-Learning is introduced for task allocation and generation of task sequences. This module includes a colony disorientation strategy to expand the search range and a search transition strategy to avoid premature convergence. In the third part, a multi-task target assignment module is presented to generate task sequences for each UAV, considering internal collisions. This module provides real-time obstacle avoidance paths for multiple UAVs, preventing premature convergence of the algorithm. Finally, we verify the practicality and reliability of the strategy through simulations.

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Search and rescue with sparsely connected swarms

Cited by 4 publications

References 64 publications

SLAM in Weak Environment Information Applications using Swarm Robots

SLAM in Weak Environment Information Applications using Swarm Robots

The Effect of Data Visualisation Quality and Task Density on Human-Swarm Interaction

Multi-UAV Collaborative Search and Attack Mission Decision-Making in Unknown Environments

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