期刊
IEEE INTERNET OF THINGS JOURNAL
卷 7, 期 8, 页码 7734-7750出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2020.2991198
关键词
Optimization; Mobile robots; Data acquisition; Monitoring; Task analysis; Cognition; Particle swarm optimization; Mobile robot; particle swarm optimization~(PSO); path planning
资金
- National Natural Science Foundation of China [61961014, 61963012]
- Scientific Research Project of Talent Introduced by Guizhou University [Gui Da Ren Ji He Zi (2019) 31]
- Key Research and Development Project of Hainan Province [ZDYF2018015]
- Hainan Province Major Science and Technology Project [ZDKJ2016015]
In mobile data acquisition, mobile robots usually face challenging tasks when collecting information in an undetermined environment with energy limitation and time-sensitive requirements. We formulate the task of data acquisition as a multiobjective optimization problem under energy and time constraints. In our investigation, three objectives for data acquisition are considered, including collecting the largest amount of information, moving along a path with the smallest probability of encountering obstacles, and traveling with shortest possible overall distance. To resolve the formulated problem which yields the best path for a mobile robot, we propose a mixed cognition particle swarm optimization (MCPSO) algorithm, which adopts the min-max normalization to calculate the fitness, and we transform the multiobjective optimization problem into a single-objective optimization problem by summation after normalization. The efficiency of the MCPSO algorithm is evaluated for mobile data acquisition in several well-known benchmarks by simulation. The simulation results demonstrate that the proposed MCPSO algorithm can achieve higher accuracy and faster convergence compared with other particle swarm optimization algorithms.
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