Abstract
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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