Particle Swarm Optimization for Cooperative Multi-Robot Task Allocation: A Multi-Objective Approach

Wei, Changyun; Ji, Ze; Cai, Boliang

doi:10.1109/lra.2020.2972894

Cited by 96 publications

(35 citation statements)

References 33 publications

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“…Suppose there are M individuals in the population, S nodes in the sensor network, and N tasks to be assigned. In formula (17), d m,n represents the situation where the n th task in the m th individual is allocated to the s th sensor:…”

Section: Iacga For Task Allocation Optimization In Itwsnsmentioning

confidence: 99%

“…Paper [16] finds the key subtasks based on the estimated completion time of the subtasks and the weight coefficients and preferentially selects node assignments with strong capabilities and high processing efficiency. Paper [17] mixes the adaptability of particle swarm optimization with the flexible ability of dynamic alliance and obtains the fitness value through the weighting method to obtain the global optimal allocation method. Paper [18] allocates tasks to different clusters to achieve the goal of high benefit and then allocates tasks from the clusters to appropriate sensor nodes to balance the energy loss of the network.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Adaptive Clone Genetic Algorithm for Task Allocation Optimization in ITWSNs

Zha

Xiao

et al. 2021

Journal of Sensors

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Research on intelligent transportation wireless sensor networks (ITWSNs) plays a very important role in an intelligent transportation system. ITWSNs deploy high-yield and low-energy-consumption traffic remote sensing sensor nodes with complex traffic parameter coordination on both sides of the road and use the self-organizing capabilities of each node to automatically establish the entire network. In the large-scale self-organization process, the importance of tasks undertaken by each node is different. It is not difficult to prove that the task allocation of traffic remote sensing sensors is an NP-hard problem, and an efficient task allocation strategy is necessary for the ITWSNs. This paper proposes an improved adaptive clone genetic algorithm (IACGA) to solve the problem of task allocation in ITWSNs. The algorithm uses a clonal expansion operator to speed up the convergence rate and uses an adaptive operator to improve the global search capability. To verify the performance of the IACGA for task allocation optimization in ITWSNs, the algorithm is compared with the elite genetic algorithm (EGA), the simulated annealing (SA), and the shuffled frog leaping algorithm (SFLA). The simulation results show that the execution performance of the IACGA is higher than EGA, SA, and SFLA. Moreover, the convergence speed of the IACGA is faster. In addition, the revenue of ITWSNs using IACGA is higher than those of EGA, SA, and SFLA. Therefore, the proposed algorithm can effectively improve the revenue of the entire ITWSN system.

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Section: Iacga For Task Allocation Optimization In Itwsnsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Adaptive Clone Genetic Algorithm for Task Allocation Optimization in ITWSNs

Zha

Xiao

et al. 2021

Journal of Sensors

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show abstract

“…The optimization of operations inside a warehouse is currently characterized by several research areas. In particular, the implementation of a robotic system able to assist human workers in their tasks requires the capability to effectively plan and optimize the sequence of actions [2][3][4], to correctly localize persons and objects [5] and to interact with the environment and other systems in the network [6]. Since the advent of Industry 4.0, the requirements of optimizing the transport and distribution of products has encouraged the use of autonomous guided vehicles within modern automated warehouses [7].…”

Section: Related Workmentioning

confidence: 99%

Toward Future Automatic Warehouses: An Autonomous Depalletizing System Based on Mobile Manipulation and 3D Perception

et al. 2021

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This paper presents a mobile manipulation platform designed for autonomous depalletizing tasks. The proposed solution integrates machine vision, control and mechanical components to increase flexibility and ease of deployment in industrial environments such as warehouses. A collaborative robot mounted on a mobile base is proposed, equipped with a simple manipulation tool and a 3D in-hand vision system that detects parcel boxes on a pallet, and that pulls them one by one on the mobile base for transportation. The robot setup allows to avoid the cumbersome implementation of pick-and-place operations, since it does not require lifting the boxes. The 3D vision system is used to provide an initial estimation of the pose of the boxes on the top layer of the pallet, and to accurately detect the separation between the boxes for manipulation. Force measurement provided by the robot together with admittance control are exploited to verify the correct execution of the manipulation task. The proposed system was implemented and tested in a simplified laboratory scenario and the results of experimental trials are reported.

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“…In terms of application fields (Figure 5d), the most papers proposed for ground vehicles considered the general context, without being limited to a particular application area, except some papers such as [57,71,60,38,51], which are proposed for the MRTA problem. However, UAVs solutions present a diversity of applications areas (Table 3).…”

Section: Analysis Of Reviewed Mtsp's Solutionsmentioning

confidence: 99%

A Comprehensive Survey on the Multiple Travelling Salesman Problem: Applications, Approaches and Taxonomy

Cheikhrouhou¹

2021

Preprint

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The Multiple Travelling Salesman Problem (MTSP) is among the most interesting combinatorial optimization problems because it is widely adopted in real-life applications, including robotics, transportation, networking, etc.Although the importance of this optimization problem, there is no survey dedicated to reviewing recent MTSP contributions. In this paper, we aim to fill this gap by providing a comprehensive review of existing studies on MTSP. In this survey, we focus on MTSP’s recent contributions to both classical vehicles/robots and unmanned aerial vehicles. We highlight the approaches applied to solve the MTSP as well as its application domains. We analyze the MTSP variants and propose a taxonomy and a classification of recent studies.

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Particle Swarm Optimization for Cooperative Multi-Robot Task Allocation: A Multi-Objective Approach

Abstract: 2020. Particle swarm optimization for cooperative multirobot task allocation: a multi-objective approach.

Cited by 96 publications

References 33 publications

An Improved Adaptive Clone Genetic Algorithm for Task Allocation Optimization in ITWSNs

An Improved Adaptive Clone Genetic Algorithm for Task Allocation Optimization in ITWSNs

Toward Future Automatic Warehouses: An Autonomous Depalletizing System Based on Mobile Manipulation and 3D Perception

A Comprehensive Survey on the Multiple Travelling Salesman Problem: Applications, Approaches and Taxonomy

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