Pfadplanungen

Pfadplanungen, also known as path planning or route planning, is a field of computer science and robotics that deals with determining optimal or feasible paths for agents moving through an environment. The goal is to find a sequence of motions that moves a vehicle, robot, or virtual entity from a starting position to a goal while avoiding obstacles, obeying constraints, and optimizing a cost function such as distance, time, or energy consumption. The concept is widely used in autonomous navigation, unmanned aerial vehicles, autonomous cars, and video game AI.

Historically, path planning research grew alongside robotics and automated systems in the 1960s and 1970s. Early work focused on graph search algorithms, including breadth‑first search and Dijkstra’s algorithm. The introduction of the A* algorithm by Hart, Nilsson, and Raphael in 1968 provided a heuristic-driven approach that became a cornerstone for many practical applications. In the 1990s and 2000s, the formal study of configuration spaces (C‑spaces) and sampling‑based planners such as Rapidly-exploring Random Trees (RRT) and Probabilistic Roadmaps (PRM) allowed planners to efficiently tackle high-dimensional, non‑convex problems.

Modern path planning algorithms often combine classical search with geometric reasoning, hybrid optimization, or machine learning. Techniques such as informed RRT*, anytime planners, and motion primitives enable real‑time performance in dynamic and uncertain environments. Parallel computation and graph‑neural networks are increasingly explored to accelerate planning for fleets or swarm systems. Despite advances, challenges remain in guaranteeing completeness, handling non‑holonomic constraints, integrating perception uncertainty, and ensuring safety in shared human‑robot spaces.