Path planning for indoor potted plant maintenance robots based on IACO and IAPF algorithms
Year: 2025
Type: article
Abstract: In the complex indoor potted plant environment, the intelligent path planning algorithm directly affects the guarantee of the operational efficiency and stability of the robot. The disordered layout of the indoor potted plant environment contains many unknown obstacles, which impose high requirements on accuracy of positioning. An intelligent path navigation algorithm needs to plan reliable and fast navigation routes in a dynamic and chaotic environment. This paper proposes a collaborative path navigation algorithm that integrates the improved ant colony algorithm and the improved artificial potential field algorithm. Firstly, in the local guidance function for path selection in the ant colony algorithm, factors such as obstacle density, gravitational influence, dynamic monitoring of pheromone evaporation coefficient, and secondary optimization of the path are added to generate the global optimal path. Secondly, the obstacle potential field functions in the potential field guidance algorithm and the method of setting virtual target location are improved to solve the problems of failing to achieve the goal and escaping from local traps, thereby improving the safety distance and operational stability. Ultimately, the efficiency of the algorithm was confirmed through simulation test and actual indoor potting experiments. The simulation test show that the ant colony algorithm based on improved strategies reduces the turning points by 65 % and the path length by 11.69 %. The results of the actual indoor potting environment experiments indicate that under two different paths in scenarios, the collaborative path navigation algorithm reduces the average navigation positioning error by 7.60 % and the alignment deviation by 30.72 %, and the safety distance is increased by 20 %.
Source: Smart Agricultural Technology
Authors: Siyu Chen, Xiaoyang Zhang, Xi Wang, Chunshan Liu
Institutions: Jiamusi University, Bengbu University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,770
Award: G3821941886
OpenAlex ID: W4417147462