Trajectory Planning and Optimization Methods for Intelligent Robots
Abstract
Trajectory planning and trajectory optimization are the key technologies to realize the autonomous operation of intelligent robots. In order to solve the limitations of traditional manual teaching and offline planning in adapting to complex dynamic environments, insufficient movement smoothness and limited adaptability, this paper systematically summarizes the trajectory planning and optimization methods of intelligent robots. On the basis of introducing the intelligent robot trajectory planning framework, this paper compares and analyzes the characteristics and applicable scenarios of two typical smooth trajectory optimization algorithms - Minimum Jerk and Minimum Snap. In addition, this paper summarizes the advantages and disadvantages of the jerk-snap hybrid optimization strategy, and reviews the data-driven trajectory optimization method based on reinforcement learning. Finally, the technical characteristics, existing challenges and future development directions of various methods are summarized and analyzed, which provides reference for the research and engineering application of intelligent robot trajectory planning and optimization technology.
