Robot control
Motion planning
Definition
Motion planning finds a robot movement from an initial state to a goal while satisfying constraints such as collision avoidance. A planner may produce a geometric path, a timed trajectory, or a sequence of controls.
Also known as: Robot motion planning
Updated
Plan the robot's movement, not just the hand's destination
A hand target does not specify how the elbow, torso, and other links should move around obstacles. Modern Robotics formulates motion planning in configuration or state space, with collision and motion constraints.
For a reaching task, a planner might search for joint configurations that take a gripper around a shelf edge. A humanoid planning a step may need additional constraints on contact and body dynamics.
A path and a trajectory answer different questions
A geometric path describes where to move. A timed trajectory also specifies when to occupy each state. A path that avoids obstacles may still require timing adjustments to respect velocity, acceleration, or actuator limits.
Planner guarantees have conditions
The cited overview distinguishes complete, resolution-complete, and probabilistically complete planners. Probabilistic completeness concerns success probability as planning time grows under the algorithm's assumptions; it does not guarantee success before a particular deadline. The quality of a plan also depends on the robot and environment models. An obstacle omitted from the model cannot be avoided merely because the modeled path is collision-free.
Sources
Related terms
Configuration space
Configuration space is the set of all possible configurations of a robot or mechanical system. Each point specifies the entire modeled arrangement, and the space has as many local dimensions as the system has degrees of freedom.
Rapidly-exploring random tree
A rapidly-exploring random tree is a sampling-based structure that grows through a configuration or state space toward sampled targets. Motion planners use it to search for feasible routes through spaces with obstacles and movement constraints.
Probabilistic roadmap
A probabilistic roadmap is a motion-planning graph built by sampling collision-free configurations and connecting nearby samples with feasible local paths. The graph can then answer start-to-goal queries within the modeled environment.
Trajectory optimization
Trajectory optimization finds a time-varying motion, and often control inputs, that minimizes an objective while satisfying specified constraints. Robot applications can include geometric, kinematic, and dynamic constraints.