A particle swarm algorithm-based landscape planting path planning method in agriculture sector

Abstract

Currently, path planning in agricultural landscapes involves heuristic-based methods to determine the optimal placement of plantings. In this study, we explore the application of particle swarm optimization (PSO) to plan paths in a dynamical system, specifically by employing PSO on the linearized dynamics at each time step. Two distinct scenarios are considered: one where PSO is executed on demand and another where it must await necessity. To evaluate the effectiveness of our proposed algorithm, we compare it against three existing approaches: the h-algorithm, LQR-based (Linear Quadratic Regulation) strategies, and the Gauss–Newton method. By analyzing the running time of the algorithm on various hardware configurations, we gauge its efficiency and scalability. The conference paper presents a comprehensive account of the complete algorithm, including its implementation. Through our research, we aim to enhance the efficiency of agricultural path planning and optimize planting arrangements, contributing to sustainable and effective agricultural practices.