loopbaansplanning

Loopbaansplanning is the process of determining a feasible trajectory or path that moves a robot or autonomous system from a starting state to a goal state while avoiding obstacles and complying with kinematic and dynamic constraints. It is a core component of autonomous navigation and motion control, bridging perception and control.

In planning, the robot's environment is often modeled as a configuration space (C-space), where each point represents a state of the robot. Obstacles map to forbidden regions. A valid plan is a path in C-space (or a trajectory over time) that connects start and goal without intersecting obstacles and satisfies constraints such as nonholonomic motion and actuator limits. Plans can be static (precomputed) or dynamic (replanned in changing environments).

Common approaches are categorized as graph-based, sampling-based, and optimization-based. Graph-based methods (e.g., Dijkstra's, A*) search discrete representations like grids or graphs. Sampling-based methods (e.g., RRT, RRT*, PRM) handle high-dimensional spaces and complex constraints. Optimization-based methods formulate planning as a constrained optimization problem, producing smooth trajectories subject to dynamics, often via quadratic programming or nonlinear programming. Hybrid methods combine strengths, and real-time systems use model predictive control or reactive planners.

Applications include mobile robots, autonomous vehicles, UAVs, and robotic arms in manufacturing and logistics. Challenges include dynamic or uncertain environments, nonholonomic constraints, high dimensionality, and real-time requirements.

See also: motion planning, SLAM, model predictive control, Open Motion Planning Library (OMPL).