scholarly journals A Real-Time Framework for Task Assignment in Hyperlocal Spatial Crowdsourcing

2018 ◽  
Vol 9 (3) ◽  
pp. 1-26 ◽  
Author(s):  
Luan Tran ◽  
Hien To ◽  
Liyue Fan ◽  
Cyrus Shahabi
Keyword(s):  
Real Time ◽  
Task Assignment ◽  
Spatial Crowdsourcing

Multi-skill aware task assignment in real-time spatial crowdsourcing

2019 ◽  
Vol 24 (1) ◽  
pp. 153-173 ◽  
Author(s):  
Tianshu Song ◽  
Ke Xu ◽  
Jiangneng Li ◽  
Yiming Li ◽  
Yongxin Tong
Keyword(s):  
Real Time ◽  
Task Assignment ◽  
Spatial Crowdsourcing

Dynamic task assignment in spatial crowdsourcing

2018 ◽  
Vol 10 (2) ◽  
pp. 18-25 ◽  
Author(s):  
Yongxin Tong ◽  
Zimu Zhou
Keyword(s):  
Task Assignment ◽  
Spatial Crowdsourcing ◽  
Dynamic Task ◽  
Dynamic Task Assignment

Multi-Stage Complex Task Assignment in Spatial Crowdsourcing

2021 ◽  
Author(s):  
Zhao Liu ◽  
Kenli Li ◽  
Xu Zhou ◽  
Ningbo Zhu ◽  
Yunjun Gao ◽  
...  
Keyword(s):  
Task Assignment ◽  
Complex Task ◽  
Spatial Crowdsourcing ◽  
Multi Stage

Task Assignment with Worker Churn Prediction in Spatial Crowdsourcing

2021 ◽  
Author(s):  
Ziwei Wang ◽  
Yan Zhao ◽  
Xuanhao Chen ◽  
Kai Zheng
Keyword(s):  
Task Assignment ◽  
Churn Prediction ◽  
Spatial Crowdsourcing

scholarly journals An Improved SOM-Based Method for Multi-Robot Task Assignment and Cooperative Search in Unknown Dynamic Environments

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3296
Author(s):  
Hongwei Tang ◽  
Anping Lin ◽  
Wei Sun ◽  
Shuqi Shi
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Task Assignment ◽  
Dynamic Environments ◽  
Self Organizing Map ◽  
Cooperative Search ◽  
Dynamic Task ◽  
Locking Mechanism ◽  
Som Neural Network ◽  
Multi Robot

The methods of task assignment and path planning have been reported by many researchers, but they are mainly focused on environments with prior information. In unknown dynamic environments, in which the real-time acquisition of the location information of obstacles is required, an integrated multi-robot dynamic task assignment and cooperative search method is proposed by combining an improved self-organizing map (SOM) neural network and the adaptive dynamic window approach (DWA). To avoid the robot oscillation and hovering issue that occurs with the SOM-based algorithm, an SOM neural network with a locking mechanism is developed to better realize task assignment. Then, in order to solve the obstacle avoidance problem and the speed jump problem, the weights of the winner of the SOM are updated by using an adaptive DWA. In addition, the proposed method can search dynamic multi-target in unknown dynamic environment, it can reassign tasks and re-plan searching paths in real time when the location of the targets and obstacle changes. The simulation results and comparative testing demonstrate the effectiveness and efficiency of the proposed method.

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