Path Planning

Path planning is one of the main research topics in motion planning, which consists of path planning and trajectory planning. A path is defined as a sequence of points or curves that connect a start position to an end position, and the strategy used to construct this path is referred to as path planning. This technique is widely applied in numerous fields, especially in high-tech sectors including autonomous collision-free navigation for robots, obstacle evasion and penetration flight for unmanned aerial vehicles (UAVs), and guidance systems for cruise missiles.

Path planning plays a crucial role in modern technological applications. As a core component of motion planning, it aims to determine the optimal route between a start point and a destination. These paths, which can be composed of a series of discrete points or continuous curves, are generated using specific path planning algorithms such as A*, Dijkstra, or Rapidly-exploring Random Trees (RRT). The implementation often involves coding techniques for graph traversal, cost calculation, and heuristic optimization to ensure efficiency and robustness. Applications are widespread, particularly in cutting-edge technologies—enabling robots to navigate autonomously without collisions, allowing drones to perform evasive maneuvers around obstacles, and guiding cruise missiles through complex environments.

Through path planning, more efficient and intelligent motion strategies can be achieved, thereby elevating technical capabilities and practical outcomes across various industries. Research and applications in path planning continue to evolve, opening up new possibilities and opportunities. Whether in engineering or scientific research, path planning remains a compelling and challenging area of study, frequently implemented using simulation tools like MATLAB or robotic operating systems (ROS) to test and validate algorithms before real-world deployment.