Abstract
To solve the problem of automatic recharging path planning for cleaning robots in complex industrial environments, this paper proposes two environmental path planning types based on designated charging location and multiple charging locations. First, we use the improved Maklink graph to plan the complex environment; then, we use the Dijkstra algorithm to plan the global path to reduce the complex two-dimensional path planning to one dimension; finally, we use the improved fruit fly optimization algorithm (IFOA) to adjust the path nodes for shorting the path length. Simulation experiments show that the effectiveness of using this path planning method in a complex industrial environment enables the cleaning robot to select a designated location or the nearest charging location to recharge when the power is limited. The proposed improved algorithm has the characteristics of a small amount of calculation, high precision, and fast convergence speed.
Highlights
Since the first autonomous mobile robot came out in the 1960s, mobile robot technology has developed rapidly
When the battery is low, it will move to the charging stand for charging and return to the original working position to continue cleaning. e second stage is to integrate positioning and navigation technology with SLAM to establish a map in real-time and design path planning to achieve
The Ecovacs series of cleaning robots combined with laser sensors can enable the robot to restore the room layout during the cleaning process, establish a real-time and accurate positioning system, and make the cleaning process more accurate and efficient. e third stage is to combine artificial intelligence with cleaning robots to realize the functions of human-computer interaction and recognition of complex obstacles and further realize the intelligence and efficiency of cleaning robots
Summary
Since the first autonomous mobile robot came out in the 1960s, mobile robot technology has developed rapidly. Is method increases exponentially with the complexity of the environment, resulting in very long computing time and inability to obtain a better solution, while the artificial intelligence algorithm applies the instinct of natural creatures to the robot path planning and uses the behavior of the group to find the global optimal solution in a complex space. The coordinate system of the cleaning robot is established, and the complex environment is modeled; secondly, to simplify the model and reduce the amount of calculation, the improved Maklink graph planning model is adopted; the above-mentioned algorithm is used on this basis to realize the automatic recharging path planning of the cleaning robot. (1) e improved Maklink graph further simplifies the complexity of industrial environment on the basis of Maklink graph It prevents the cleaning robot from colliding with obstacles and speeds up the computation for later charging path planning. Establishing the robot coordinate system is the primary condition for studying its specific position in the environmental area, judging the relative position of obstacles and the ending point, and laying the foundation for the step of environmental modeling and path planning
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