Trajectory Planning Research Based on Rapidly-exploring Random Trees (RRT) Algorithm and Modified Forms

With the rapid advancement of computer technology, robots have become indispensable in various fields. Trajectory planning plays a crucial role in optimizing robot performance by enhancing work efficiency and reducing operational costs. While the widely used Rapidly-exploring Random Trees (RRT) algorithm is effective in path planning, it often generates non-optimal paths with poor smoothness. By incorporating elements from RRT-Connect and reverse optimization, the algorithm can efficiently navigate through intricate and dynamic surroundings. Furthermore, the utilization of cubic polynomial interpolation in optimizing path curves adds a layer of sophistication to the trajectory planning process. This interpolation technique allows for the creation of smoother and more natural path trajectories, reducing jerky movements and enhancing overall motion control precision. The paths produced by the enhanced RRT algorithm demonstrate increased continuity and visual attractiveness, crucial for tasks necessitating accurate and elegant robotic motions. The improved algorithm showcases reduced path length and time consumption, as well as enhanced path smoothness. Particularly, relative to the traditional RRT approach, the time consumption has dropped by 69.15% and the median duration of the paths has fallen by 21.11%. These improvements signify a substantial advancement in trajectory planning for robots, offering more efficient and smoother paths for robotic operations across various applications.