Development of a task-oriented, auction-based task allocation framework for a heterogeneous multirobot system

Eijyne, Tan; Rishwaraj, G.; Ponnambalam, S. G.

doi:10.1007/s12046-020-01330-4

Cited by 7 publications

(4 citation statements)

References 39 publications

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“…Therefore, the algorithm allows each UAV to make decisions timely during the mission, with the effectiveness demonstrated by simulation experiments. Literature [12] presents a taskoriented task allocation framework based on auction algo-rithm, with good robustness in different tasks and environment demand scenarios. In literature [13], distributed task allocation and scheduling algorithms are put forward, aiming at tight coupling the tasks with time priorities.…”

Section: Related Workmentioning

confidence: 99%

A Decision-Making Method for Distributed Unmanned Aerial Vehicle Swarm considering Attack Constraints in the Cooperative Strike Phase

Gao

et al. 2023

International Journal of Aerospace Engineering

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In view of the growing military forces of various countries, unmanned aerial vehicle (UAV) swarms, as a new type of weapon, are gradually attracting the attention of more and more countries. Decision-making, as the core link in its application, has also become the focus of research in these countries. In this work, the distributed UAV swarm cooperative strike decision-making problems are separated from the distributed UAV swarm cooperative search strike decision-making problems, and the distributed UAV swarm cooperative strike multiobjective decision-making model is established, with the relevant constraints clarified. Besides, according to the analysis of the motion characteristics of UAV and the speed requirements of the distributed UAV swarm cooperative strike decision, an arc tangent trajectory planning method, conforming to the motion characteristics of UAV, is proposed. Moreover, a distributed cooperative strike decision-making method, based on the idea of “campaign endorsement and invitation cooperation,” is put forward, with the effectiveness and superiority validated by simulation experiments.

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Section: Related Workmentioning

confidence: 99%

A Decision-Making Method for Distributed Unmanned Aerial Vehicle Swarm considering Attack Constraints in the Cooperative Strike Phase

Gao

et al. 2023

International Journal of Aerospace Engineering

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“…Eijyne, T. et.al. [25] proposed a task-oriented, auction-based task allocation framework, which is implemented in a multi-robot system, allowing tasks to be dynamically allocated to robots when a given task is completed. These literatures adopt different methods for the research goals to obtain the task allocation table and then update the execution according to the system framework.…”

Section: Related Workmentioning

confidence: 99%

Swarm Robots Cooperative and Persistent Distribution Modeling and Optimization Based on the Smart Community Logistics Service Framework

Zhang

Yang

2022

Algorithms

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The high efficiency, flexibility, and low cost of robots provide huge opportunities for the application and development of intelligent logistics. Especially during the COVID-19 pandemic, the non-contact nature of robots effectively helped with preventing the spread of the epidemic. Task allocation and path planning according to actual problems is one of the most important problems faced by robots in intelligent logistics. In the distribution, the robots have the fundamental characteristics of battery capacity limitation, limited load capacity, and load affecting transportation capacity. So, a smart community logistics service framework is proposed based on control system, automatic replenishment platform, network communication method, and coordinated distribution optimization technology, and a Mixed Integer Linear Programming (MILP) model is developed for the collaborative and persistent delivery of a multiple-depot vehicle routing problem with time window (MDVRPTW) of swarm robots. In order to solve this problem, a hybrid algorithm of genetically improved set-based particle swarm optimization (S-GAIPSO) is designed and tested with numerical cases. Experimental results show that, Compared to CPLEX, S-GAIPSO has achieved gaps of 0.157%, 1.097%, and 2.077% on average, respectively, when there are 5, 10, and 20 tasks. S-GAIPSO can obtain the optimal or near-optimal solution in less than 0.35 s, and the required CPU time slowly increases as the scale increases. Thus, it provides utility for real-time use by handling a large-scale problem in a short time.

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“…Machine learning is extremely promising for solving dynamic task allocation [7]. A series of human-robot function allocation algorithms have been proposed, such as the market-based auction algorithm [8,9], particle swarm optimization algorithm [10,11], greedy algorithm [12], heuristic algorithm [13], queen bee-assisted genetic algorithm [14], and simulation-based learning techniques [15,16]. Liu et al introduced a task allocation approach that takes into account both capacity and time, with the objective of enhancing the efficiency of task allocation and accommodating current tasks as they arise [17].…”

Section: Introductionmentioning

confidence: 99%

Generating Function Reallocation to Handle Contingencies in Human–Robot Teaming Missions: The Cases in Lunar Surface Transportation

Wen

Wang³

et al. 2023

Applied Sciences

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On lunar missions, efficient and safe transportation of human–robot systems is essential for the success of human exploration and scientific endeavors. Given the fact that transportation constructs bottlenecks for numerous typical lunar missions, it is appealing to investigate what function allocation strategies can generate optimal task implementation paths for robots with low-human workloads when the situation changes. Thus, this paper presents a novel approach to dynamic human–robot function allocation explicitly designed for team transportation in lunar missions. The proposed dynamic allocation framework aims to optimize human–robot collaboration by responding to existing and potential contingencies. First, a fitness concept model is designed to quantify the factors that motivate the functional adaptation of each agent in dynamic lunar mission scenarios. A hierarchical reinforcement learning (HRL) algorithm with two layers is then employed for decision-making and optimization of human–robot function allocation. Finally, the validity of the framework and algorithm proposed is validated by a series of human–robot function allocation experiments on a simulated environment that mimics lunar transportation scenarios, and is compared with the performance of other algorithms. In the future, path-planning algorithms can be incorporated into the proposed framework to improve the adaptability and efficiency of the human–robot function allocation in lunar missions.

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Development of a task-oriented, auction-based task allocation framework for a heterogeneous multirobot system

Cited by 7 publications

References 39 publications

A Decision-Making Method for Distributed Unmanned Aerial Vehicle Swarm considering Attack Constraints in the Cooperative Strike Phase

A Decision-Making Method for Distributed Unmanned Aerial Vehicle Swarm considering Attack Constraints in the Cooperative Strike Phase

Swarm Robots Cooperative and Persistent Distribution Modeling and Optimization Based on the Smart Community Logistics Service Framework

Generating Function Reallocation to Handle Contingencies in Human–Robot Teaming Missions: The Cases in Lunar Surface Transportation

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