Abstract
The path planning of mobile robot is to find an optimal collision-free path in time distance or space from the starting point to the target point in a given environment. With the popularization and application of mobile robots, if the efficiency of mobile robots path is not high,
Highlights
Due to the characteristics of autonomous operation and flexible movement, mobile robots have a broad prospect of development and utilization in important fields such as national defense science and technology, life service, production and construction [1,2]
In order to verify the performance of the proposed algorithm in the process of local dynamic obstacle avoidance, simulation comparisons are made under different environments
The convergence speed of traditional ant colony algorithm is slow and it is easy to fall into the local optimal solution, so it is difficult to find the global optimal solution when applied to robot path planning
Summary
Due to the characteristics of autonomous operation and flexible movement, mobile robots have a broad prospect of development and utilization in important fields such as national defense science and technology, life service, production and construction [1,2]. In reference [14], the improved A* algorithm was successfully used to realize path planning in static environment. Tan [15] adopted the improved Dijkstra algorithm to realize global path planning, but its local path search was slow. Wang [16] adopted improved ant colony algorithm to realize global path search, but the data storage capacity in the search process was large. Literature [20] proposed a multi-level hybrid algorithm combining A* algorithm and artificial potential field method to realize the path planning in dynamic environment. This paper presents a new A* algorithm for robot path planning based on the combination of improved ant colony(ACO) and improved pigeon-inspired optimization (PIO).
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