Path Planning

Path planning is the problem of computing a collision-free route for a robot, vehicle, or autonomous agent from a start configuration to a goal configuration in an environment with obstacles. It encompasses classical graph-search algorithms (A*, Dijkstra), sampling-based methods (RRT, PRM), and optimization-based approaches. Modern path planning increasingly integrates machine learning and deep reinforcement learning to handle complex, dynamic, and high-dimensional environments.

Autonomous vehicles, warehouse robots, surgical robots, and drones all require reliable path planning as a core capability, making it one of the most actively hired-for skills in robotics and autonomous systems engineering in 2026. AI companies building embodied AI, self-driving stacks, and robotic manipulation pipelines depend on engineers who can implement and extend planners that operate safely in real time. The rise of large language models guiding high-level task planning has further expanded demand for practitioners who understand the full planning stack, from low-level trajectory generation to high-level task sequencing.