Hybrid Motion Planning Task Allocation Model for AUV’s Safe Maneuvering in a Realistic Ocean Environment

2018 ◽  
Vol 94 (1) ◽  
pp. 265-282 ◽  
Author(s):  
Somaiyeh MahmoudZadeh ◽  
David M. W. Powers ◽  
Karl Sammut ◽  
Amir Mehdi Yazdani ◽  
Adham Atyabi
Keyword(s):  
Motion Planning ◽  
Task Allocation ◽  
Allocation Model ◽  
Planning Task ◽  
Ocean Environment ◽  
Hybrid Motion

scholarly journals Motion Planning and Task Allocation for a Jumping Rover Team

2020 ◽  
Author(s):  
Kai Chuen Tan ◽  
Myungjin Jung ◽  
Isaac Shyu ◽  
Changhuang Wan ◽  
Ran Dai
Keyword(s):  
Motion Planning ◽  
Task Allocation ◽  
And Task

An Energy Efficiency Task Allocation Method Based on Reasoning Model in Wireless Sensor Network

2011 ◽  
Vol 268-270 ◽  
pp. 440-445
Author(s):  
Wang Lan Tian ◽  
Hong Yan Lei
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Task Allocation ◽  
Energy Efficient ◽  
Negotiation Process ◽  
Energy Threshold ◽  
Wireless Sensor ◽  
Level Energy ◽  
Allocation Model

In this paper, a reasoning model is proposed for energy efficiency task allocation in wireless sensor network. The presented energy efficient contract net protocol is used to implement the negotiation process. Multi-issue scoring function can evaluate the offer with multi-issues in a quantifiable way. An energy threshold is brought to decrease communications which will turn out to decrease nodes’ energy consumption. And the usage of energy threshold also promote the nodes with high level energy have more chance to implement tasks. The simulation results show that the allocation model has outstanding performance maintaining a fair energy balance and is energy efficient in negotiation process.

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.

Research on Task Allocation Model of Materiel Readiness Rate and the Talented Person Growing Law

2013 ◽  
Vol 401-403 ◽  
pp. 2213-2216
Author(s):  
Bing Chen ◽  
Xiao Dong Zhu ◽  
Yi Gang Wang ◽  
Fei Ye
Keyword(s):  
Quantitative Analysis ◽  
Task Allocation ◽  
Armed Forces ◽  
Allocation Problem ◽  
Equipment Maintenance ◽  
Analysis Method ◽  
High Quality ◽  
Allocation Model ◽  
Quantitative Analysis Method ◽  
System Maintenance

The army's existing preparation system, maintenance task allocation problem is missing quantitative analysis method, Research on Materiel readiness rate and the talented person growing law of task allocation model, This method has guiding significance for the Army equipment maintenance tasks assigned, At the same time which has important effect on improving the combat effectiveness of our armed forces and cultivate high-quality constructive. finally, test the feasibility of the method with examples.

scholarly journals Multi-Auv Distributed Task Allocation Based on the Differential Evolution Quantum Bee Colony Optimization Algorithm

2017 ◽  
Vol 24 (s3) ◽  
pp. 65-71
Author(s):  
Jianjun Li ◽  
Ru Bo Zhang
Keyword(s):  
Differential Evolution ◽  
Optimization Algorithm ◽  
Task Allocation ◽  
Artificial Bee Colony ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Allocation Model ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Distributed Task Allocation

Abstract The multi-autonomous underwater vehicle (AUV) distributed task allocation model of a contract net, which introduces an equilibrium coefficient, has been established to solve the multi-AUV distributed task allocation problem. A differential evolution quantum artificial bee colony (DEQABC) optimization algorithm is proposed to solve the multi-AUV optimal task allocation scheme. The algorithm is based on the quantum artificial bee colony algorithm, and it takes advantage of the characteristics of the differential evolution algorithm. This algorithm can remember the individual optimal solution in the population evolution and internal information sharing in groups and obtain the optimal solution through competition and cooperation among individuals in a population. Finally, a simulation experiment was performed to evaluate the distributed task allocation performance of the differential evolution quantum bee colony optimization algorithm. The simulation results demonstrate that the DEQABC algorithm converges faster than the QABC and ABC algorithms in terms of both iterations and running time. The DEQABC algorithm can effectively improve AUV distributed multi-tasking performance.

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