Trajectory generation is a critical component in robotics and autonomous systems, providing the computational techniques to plan paths that objects or agents must follow to achieve specified goals while adhering to constraints like avoiding obstacles, minimizing energy usage, or optimizing travel time. This process involves mathematical models and algorithms that can predict and simulate the motion of objects through space and time. Applications of trajectory generation span robotics (e.g., for manipulators in assembly lines), autonomous vehicles (to determine routes that avoid collisions), aerospace (flight path planning), and animation (for realistic movement of characters).
The concept of trajectory generation became prominent in the 1980s with the rise of robotics and automated systems needing precise motion control. It has roots in control theory and classical mechanics, where the fundamentals of motion planning were first formalized.
Significant advancements in trajectory generation have been made by researchers in robotics and control systems. Figures such as Rodney Brooks with his subsumption architecture and Ruzena Bajcsy in active perception systems have indirectly contributed by developing frameworks that integrate trajectory planning.