A Flexible Framework for Diverse Multi-Robot Task Allocation Scenarios Including Multi-Tasking

Arif, Muhammad Usman; Haider, Sajjad

doi:10.1145/3502200

Cited by 6 publications

(4 citation statements)

References 41 publications

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“…Achieving coordinated teamwork among robots with real-time task distribution necessitates a decentralized framework [31] that covers resilient robot awareness, low-level motion control, and high-level task scheduling [32], [33]. Effective location management in multi-robot networks is critical for collaboration, where decision-making and communication play pivotal roles in mission distribution, presenting substantial challenges in data exchange among robots and the operating station [34].…”

Section: Distributed Mrta Use Case and Problem Statementmentioning

confidence: 99%

Distributed and autonomous multi-robot for task allocation and collaboration using a greedy algorithm and robot operating system platform

Tamali,

Amardjia,

Tamali

2024

IJRA

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Research investigations in the realm of micro-robotics often center around strategies addressing the multi-robot task allocation (MRTA) problem. Our contribution delves into the collaborative dynamics of micro-robots deployed in targeted hostile environments. Employing advanced algorithms, these robots play a crucial role in enhancing and streamlining operations within sensitive areas. We adopt a tailored GREEDY approach, strategically adjusting weight parameters in a multi-objective function that serves as a cost metric. The objective function, designed for optimization purposes, aggregates the cost functions of all agents involved. Our evaluation meticulously examines the MRTA efficiency for each micro-robot, considering dependencies on factors such as radio connectivity, available energy, and the absolute and relative availability of agents. The central focus is on validating the positive trend associated with an increasing number of agents constituting the cluster. Our methodology introduces a trio of micro-robots, unveiling a flexible strategy aimed at detecting individuals at risk in demanding environments. Each micro-robot within the cluster is equipped with logic that ensures compatibility and cooperation, enabling them to effectively execute assigned missions. The implementation of MRTA-based collaboration algorithms serves as an adaptive strategy, optimizing agents' mobility based on specific criteria related to the characteristics of the target site.<p class="JAMRISAbstract"> </p>

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Section: Distributed Mrta Use Case and Problem Statementmentioning

confidence: 99%

Distributed and autonomous multi-robot for task allocation and collaboration using a greedy algorithm and robot operating system platform

Tamali,

Amardjia,

Tamali

2024

IJRA

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“…According to matrix (6) and matrix (9), we calculate the value of A* i,j and express it with (10). Next, we express A* i,j , the advantage value of assigning the jth task to the ith sensor node.…”

Section: G Bit Packet Transmission Circuitmentioning

confidence: 99%

“…Generating an optimum task allocation for IUWSNs with limited power sources and limited perception constraints is very useful, but the problem is NP-hard. For IUWSNs with many nodes, an exhaustive search cannot be employed to obtain the optimal task allocation results in real-time [5][6][7][8][9]. To obtain higher network revenue, many stochastic methods for task allocation have been proposed in the literature.…”

Section: Introductionmentioning

confidence: 99%

A Novel Chaotic Elite Adaptive Genetic Algorithm for Task Allocation of Intelligent Unmanned Wireless Sensor Networks

Fei,

Zhang,

Liu

et al. 2023

Applied Sciences

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In recent times, the progress of Intelligent Unmanned Wireless Sensor Networks (IUWSNs) has inspired scientists to develop inventive task allocation algorithms. These efficient techniques serve as robust stochastic optimization methods, aimed at maximizing revenue for the network’s objectives. However, with the increase in sensor numbers, the computation time for addressing the challenge grows exponentially. To tackle the task allocation issue in IUWSNs, this paper introduces a novel approach: the Chaotic Elite Adaptive Genetic Algorithm (CEAGA). The optimization problem is formulated as an NP-complete integer programming challenge. Innovative elite and chaotic operators have been devised to expedite convergence and unveil the overall optimal solution. By merging the strengths of genetic algorithms with these new elite and chaotic operators, the CEAGA optimizes task allocation in IUWSNs. Through simulation experiments, we compare the CEAGA with other methods—Hybrid Genetic Algorithm (HGA), Multi-objective Binary Particle Swarm Optimization (MBPSO), and Improved Simulated Annealing (ISA)—in terms of task allocation performance. The results compellingly demonstrate that the CEAGA outperforms the other approaches in network revenue terms.

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“…In the decentralized control approach, numerous studies have used an activationthreshold model to implement TA in a swarm of foraging robots [12], whereby robots decide to go foraging when an external stimulus exceeds a threshold value [13]. Among them, the external stimulus could be related to the number of food tokens stored in the nest or the amount of time spent in each state by each robot.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Response Threshold Model for Self-Organized Task Allocation in a Swarm of Foraging Robots

et al. 2023

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In swarm-robotics foraging, the purpose of task allocation is to adjust the number of active foraging robots dynamically based on the task demands and changing environment. It is a difficult challenge to generate self-organized foraging behavior in which each robot can adapt to environmental changes. To complete the foraging task efficiently, this paper presents a novel self-organized task allocation strategy known as the dynamic response threshold model (DRTM). To adjust the behavior of the active foraging robots, the proposed DRTM newly introduces the traffic flow density, which can be used to evaluate the robot density. Firstly, the traffic flow density and the amount of obstacle avoidance are used to adjust the threshold which determines the tendency of a robot to respond to a stimulus in the environment. Then, each individual robot uses the threshold and external stimulus to calculate the foraging probability that determines whether or not to go foraging. This paper completes the simulation and physical experiments, respectively, and the performance of the proposed method is evaluated using three commonly used performance indexes: the average deviation of food, the energy efficiency, and the number of obstacle avoidance events. The experimental results show that the DRTM is superior to and more efficient than the adaptive response threshold model (ARTM) in all three indexes.

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A Flexible Framework for Diverse Multi-Robot Task Allocation Scenarios Including Multi-Tasking

Cited by 6 publications

References 41 publications

Distributed and autonomous multi-robot for task allocation and collaboration using a greedy algorithm and robot operating system platform

Distributed and autonomous multi-robot for task allocation and collaboration using a greedy algorithm and robot operating system platform

A Novel Chaotic Elite Adaptive Genetic Algorithm for Task Allocation of Intelligent Unmanned Wireless Sensor Networks

Dynamic Response Threshold Model for Self-Organized Task Allocation in a Swarm of Foraging Robots

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