Path Planning Problem

Path planning, the problem of finding optimal routes for robots or other agents in complex environments, aims to generate collision-free trajectories that satisfy various constraints, such as minimizing path length or travel time. Current research emphasizes efficient algorithms, including those based on rapidly-exploring random trees (RRTs), probabilistic roadmaps (PRMs), particle swarm optimization (PSO), and graph neural networks (GNNs), often tailored to specific challenges like dynamic obstacles, multi-agent coordination, or limited sensor information. These advancements are crucial for improving the autonomy and efficiency of robotic systems in diverse applications, from warehouse logistics and autonomous driving to aerial surveillance and surgical robotics. The development of faster, more robust, and adaptable path planning methods remains a significant area of ongoing investigation.