A Survey of Trajectory Planning Applied to the Harvesting System

Abstract

This work focuses on trajectory planning for the Palmer Harvesting System. Studying path and trajectory planning is crucial before implementing a consistent harvesting system, as it serves as the input for the robot manipulator. Extensive research has been conducted on mobile robot trajectory planning, exploring various methods to optimize cost, mobility, energy consumption, and efficiency. This study examines several pathplanning techniques, assessing their capabilities and limitations. Path-planning methods can be categorized into mathematical-based, heuristic, or artificial intelligence approaches. Their effectiveness depends on handling complex environments with minimal computation and ensuring obstacle avoidance, whether static or dynamic. Addressing this NP (nondeterministic polynomial) problem is essential, especially in uncertain environments where security is critical. Many application domains, including mobile robots, UAVs, autonomous vehicles, and industrial robots, require real-time, high-efficiency path planning. This study provides a classification of different approaches, highlighting their constraints through testing and implementation using Python.