scholarly journals Dynamic Coalition Task Allocation of Heterogeneous Multiple Agents

2020 ◽  
Vol 38 (5) ◽  
pp. 1094-1104
Author(s):  
Xiangmin Li ◽  
Jiayu Tang ◽  
Jinjin Dai ◽  
Ning Bo
Keyword(s):  
Task Allocation ◽  
Time Windows ◽  
Dynamic Planning ◽  
Aerial Vehicles ◽  
Allocation Process ◽  
Bundle Algorithm ◽  
Complex Constraints ◽  
Planning Strategies ◽  
Unmanned Combat Aerial Vehicles ◽  
Present Algorithm

The dynamic coalition task allocation of heterogeneous multiple UAV agents is researched, which is divided into two parts. Firstly, the consensus based coalition algorithm(CBCA) is presented via consensus based bundle algorithm(CBBA), considering complex constraints of specific equipment requirements and coupling the relationships between the subtasks and the time windows. Secondly, three dynamic planning strategies are proposed in cope with appearance of new tasks during the allocation process. Finally, the feasibility and applicability of the present algorithm and dynamic planning strategies are validated in the scenario of a search and attack mission executed by multiple unmanned search aerial vehicles(USAVs) and unmanned combat aerial vehicles (UCAVs).

scholarly journals The best task allocation process is to decide on one’s own: effects of the allocation agent in human–robot interaction on perceived work characteristics and satisfaction

2020 ◽  
Author(s):  
Alina Tausch ◽  
Annette Kluge
Keyword(s):  
Task Allocation ◽  
New Technologies ◽  
Human Robot Interaction ◽  
Work Characteristics ◽  
Allocation Decision ◽  
Robot Interaction ◽  
Work Systems ◽  
Organizational Unit ◽  
Allocation Process ◽  
Task Identity

AbstractNew technologies are ever evolving and have the power to change human work for the better or the worse depending on the implementation. For human–robot interaction (HRI), it is decisive how humans and robots will share tasks and who will be in charge for decisions on task allocation. The aim of this online experiment was to examine the influence of different decision agents on the perception of a task allocation process in HRI. We assume that inclusion of the worker in the allocation will create more perceived work resources and will lead to more satisfaction with the allocation and the work results than a decision made by another agent. To test these hypotheses, we used a fictional production scenario where tasks were allocated to the participant and a robot. The allocation decision was either made by the robot, by an organizational unit, or by the participants themselves. We then looked for differences between those conditions. Our sample consisted of 151 people. In multiple ANOVAs, we could show that satisfaction with the allocation process, the solution, and with the result of the work process was higher in the condition where participants themselves were given agency in the allocation process compared to the other two. Those participants also experienced more task identity and autonomy. This has implications for the design of allocation processes: The inclusion of workers in task allocation can play a crucial role in leveraging the acceptance of HRI and in designing humane work systems in Industry 4.0.

Distributed task allocation for multiple heterogeneous UAVs based on consensus algorithm and online cooperative strategy

2018 ◽  
Vol 90 (9) ◽  
pp. 1464-1473 ◽  
Author(s):  
Weinan Wu ◽  
Naigang Cui ◽  
Wenzhao Shan ◽  
Xiaogang Wang
Keyword(s):  
Task Allocation ◽  
Consensus Algorithm ◽  
Communication Link ◽  
Communication Mode ◽  
Cooperative Strategy ◽  
Bayesian Theorem ◽  
Content Type ◽  
Distributed Framework ◽  
Allocation Process ◽  
Distributed Task Allocation

Purpose The purpose of this paper is to develop a distributed task allocation method for cooperative mission planning of multiple heterogeneous unmanned aerial vehicles (UAVs) based on the consensus algorithm and the online cooperative strategy. Design/methodology/approach In this paper, the allocation process is conducted in a distributed framework. The cooperative task allocation problem is proposed with constraints and uncertainties in a real mission. The algorithm based on the consensus algorithm and the online cooperative strategy is proposed for this problem. The local chain communication mode is adopted to restrict the bandwidth of the communication link among the UAVs, and two simulation tests are given to test the optimality and rapidity of the proposed algorithm. Findings This method can handle both continuous and discrete uncertainties in the mission space, and the proposed algorithm can obtain a feasible solution in allowable time. Research limitations/implications This study is only applied to the case that the total number of the UAVs is less than 15. Practical implications This study is expected to be practical for a real mission with uncertain targets. Originality/value The proposed algorithm can go beyond previous works that only deal with continuous uncertainties, and the Bayesian theorem is adopted for estimation of the target.

Research on dynamic task allocation for multiple unmanned aerial vehicles

2017 ◽  
Vol 39 (4) ◽  
pp. 466-474 ◽  
Author(s):  
Yongfei Miao ◽  
Luo Zhong ◽  
Yufu Yin ◽  
Chengming Zou ◽  
Zhenjun Luo
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Task Allocation ◽  
Clonal Selection ◽  
Immune Memory ◽  
Allocation Model ◽  
Dynamic Task ◽  
Aerial Vehicles ◽  
Dynamic Task Allocation ◽  
Multi Agent ◽  
Multiple Unmanned Aerial Vehicles

To solve the distributed task allocation problems of search and rescue missions for multiple unmanned aerial vehicles (UAVs), this paper establishes a dynamic task allocation model under three conditions: 1) when new targets are detected, 2) when UAVs break down and 3) when unexpected threats suddenly occur. A distributed immune multi-agent algorithm (DIMAA) based on an immune multi-agent network framework is then proposed. The technologies employed by the proposed algorithm include a multi-agent system (MAS) with immune memory, neighbourhood clonal selection, neighbourhood suppression, neighbourhood crossover and self-learning operators. The DIMAA algorithm simplifies the decision-making process among agents. The simulation results show that this algorithm not only obtains the global optimum solution, but also reduces the communication load between agents.

scholarly journals Hypovigilance Detection for UCAV Operators Based on a Hidden Markov Model

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yerim Choi ◽  
Namyeon Kwon ◽  
Sungjun Lee ◽  
Yongwook Shin ◽  
Chuh Yeop Ryo ◽  
...  
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Military Technology ◽  
Real World Data ◽  
Accident Rate ◽  
Detection Model ◽  
Aerial Vehicles ◽  
Unmanned Combat Aerial Vehicles

With the advance of military technology, the number of unmanned combat aerial vehicles (UCAVs) has rapidly increased. However, it has been reported that the accident rate of UCAVs is much higher than that of manned combat aerial vehicles. One of the main reasons for the high accident rate of UCAVs is the hypovigilance problem which refers to the decrease in vigilance levels of UCAV operators while maneuvering. In this paper, we propose hypovigilance detection models for UCAV operators based on EEG signal to minimize the number of occurrences of hypovigilance. To enable detection, we have applied hidden Markov models (HMMs), two of which are used to indicate the operators’ dual states, normal vigilance and hypovigilance, and, for each operator, the HMMs are trained as a detection model. To evaluate the efficacy and effectiveness of the proposed models, we conducted two experiments on the real-world data obtained by using EEG-signal acquisition devices, and they yielded satisfactory results. By utilizing the proposed detection models, the problem of hypovigilance of UCAV operators and the problem of high accident rate of UCAVs can be addressed.

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