Trajectory planning refers to the set of methods and algorithms used to determine an optimal or feasible path that an agent (robot, autonomous vehicle, drone, etc.) should follow to reach a given goal, while meeting various constraints (obstacles, dynamics, physical laws). This process involves anticipating environmental changes, managing uncertainty, and ensuring the movement is physically possible. It differs from simple navigation by integrating not only path generation but also dynamic adaptation and compatibility with the agent's real-world capabilities. Trajectory planning is central to many autonomous and robotic systems.
Use Cases and Examples
Trajectory planning is used to guide industrial robots through complex manipulation tasks, enable autonomous vehicles to drive safely in urban environments, and coordinate the movement of drones during surveillance missions. It also applies to virtual animation, robot-assisted surgery, and automated logistics.
For instance, an industrial welding robot uses trajectory planning to maneuver its arm around parts without collision, while a self-driving car employs these algorithms to anticipate maneuvers for obstacle avoidance and optimal route selection.
Main Software Tools, Libraries, Frameworks
Several open-source tools and libraries are widely used: MoveIt! (robotics), OMPL (Open Motion Planning Library), ROS Navigation Stack (mobile robotics), TrajOpt (trajectory optimization), Drake, and Tesseract. These frameworks offer a range of algorithms from simple (A*, Dijkstra) to advanced (RRT*, PRM, nonlinear optimization), suitable for different application contexts.
Latest Developments, Trends, and Evolutions
Recent trends include the integration of machine learning to predict dynamic obstacle movements, real-time optimization using hybrid algorithms, and handling uncertain environmental perception. Tool interoperability, automated configuration, and adaptability are major focuses, as is extending solutions to more complex and dynamic environments, especially for autonomous urban mobility and collaborative robotics.