Advancements in Coverage Path Planning and Motion Stabilization for Control Object Motion along Designed Paths

Coverage path planning (CPP) and path motion stabilization (PMS) play crucial roles in a variety of robotics applications, with a particular focus on their significance in precision agriculture. CPP determines optimal paths for covering specified target space, while PMS ensures precise movement along these paths. The paper proposes the advancement of CPP and PMS methodologies through the application of modern metaheuristic algorithms and symbolic regression methods. This study addresses a novel problem where the utilization of these methodologies is pursued to develop a universal stabilization system for moving an object along a trajectory, ensuring complete coverage of target space. Metaheuristic algorithms, such as the Grey Wolf Optimizer used in this study, can effectively solve CPP problems by decomposing the target space into subregions and searching for the optimal trajectory to efficiently explore these subregions. PMS utilizes symbolic regression methods and machine learning control techniques to develop stabilization systems. The control function of the stabilization system is considered as a function of the deviation from the equilibrium state. Consequently, it becomes feasible to explicitly derive a universal stabilization system for subsequent integration into the control object. The proposed CPP and PMS methodologies are evaluated through a computational experiment centered on crop monitoring using a quadcopter, thereby validating their effectiveness in practical applications.