Abstract
Path planning of mobile robots in complex environments is the most challenging research. A hybrid approach combining the enhanced ant colony system with the local optimization algorithm based on path geometric features, called EACSPGO, has been presented in this study for mobile robot path planning. Firstly, the simplified model of pheromone diffusion, the pheromone initialization strategy of unequal allocation, and the adaptive pheromone update mechanism have been simultaneously introduced to enhance the classical ant colony algorithm, thus providing a significant improvement in the computation efficiency and the quality of the solutions. A local optimization method based on path geometric features has been designed to further optimize the initial path and achieve a good convergence rate. Finally, the performance and advantages of the proposed approach have been verified by a series of tests in the mobile robot path planning. The simulation results demonstrate that the presented EACSPGO approach provides better solutions, adaptability, stability, and faster convergence rate compared to the other tested optimization algorithms.
Highlights
With the improvements in automation technology and artificial intelligence, robotics is becoming a necessary part of various areas, such as unmanned aerial vehicles,[1] industrial automation,[2] surveillance operations,[3] agricultural automation,[4,5] logistics,[6] and other applications
We proposed a hybrid approach by combining the enhanced ant colony system (EACS) with local optimization algorithm based on path geometric features (LOAGF) for addressing mobile robot path planning
We examine the performance of the EACSPGO algorithm in different scenarios
Summary
With the improvements in automation technology and artificial intelligence, robotics is becoming a necessary part of various areas, such as unmanned aerial vehicles,[1] industrial automation,[2] surveillance operations,[3] agricultural automation,[4,5] logistics,[6] and other applications. A considerable number of research issues in mobile robotics, such as trajectory tracking, path planning, and simultaneous localization and mapping, have been studied to date.[7] The most essential research hotspots are the path planning problem among them. The main purpose of path planning is to figure out a feasible and optimal path so that the robot can reach the target point from the starting position and avoid the scattered obstacles in the environment.
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