mozgástervezés

Mozgástervezés, also known as motion planning, is a fundamental problem in robotics and artificial intelligence concerning how to find a sequence of movements for a robot or agent to move from a starting configuration to a goal configuration while avoiding obstacles. This process involves determining a collision-free path in a state space, which represents all possible positions and orientations of the agent. The complexity arises from the high dimensionality of the state space, the intricate shapes of obstacles, and the dynamic nature of the environment.

Various algorithms have been developed to tackle the motion planning problem. Some common approaches include roadmap-based methods like Probabilistic Roadmaps (PRM) and rapidly-exploring random trees (RRTs). These methods discretize the configuration space by sampling points and connecting them to form a graph or tree, on which pathfinding algorithms like A* can be applied. Other methods are sampling-based and directly search the space, while others use optimization techniques to refine an initial path.

The choice of motion planning algorithm often depends on the specific application, the dimensionality of the problem, and the desired properties of the solution, such as optimality or speed. Applications of motion planning are widespread, including autonomous navigation for vehicles, industrial robot manipulation, character animation in computer graphics, and even the movement of surgical instruments. Effective motion planning is crucial for enabling intelligent agents to operate safely and efficiently in complex environments.