Robotics
Motion Planning
ActivePublished: 9 September 2026Updated: 9 September 2026Published
Key
innovation
Computationally finding a continuous, valid path between a start and a goal configuration while avoiding collisions with obstacles — a foundation of robot motion autonomy.
Category
Robotics
Abstraction level
Pattern
Operation level
Robot control
Use cases
Autonomous mobile navigationIndustrial manipulator motion planningRobotic surgeryCharacter animation and video games
How it works
The problem is posed in configuration space; an algorithm (sampling-based like RRT/PRM, potential field or grid method) searches this space for a continuous path from start to goal that avoids obstacles, and the result is then smoothed and executed by a motion controller.
Problem solved
Finding a collision-free, feasible motion trajectory in a complex, often high-dimensional configuration space, which is a prerequisite for autonomous robot operation.
Key mechanisms
Configuration space
Sampling-based algorithms (RRT, PRM)
Artificial potential fields
Grid-based and geometric methods
Strengths & limitations
Strengths
✓Effective in high-dimensional spaces (sampling methods)
✓Broad applicability (from robots to biology)
✓Collision-free guarantees w.r.t. known obstacles
Limitations
✗Grid methods scale poorly with dimensionality
✗Potential fields prone to local minima
✗Requires an obstacle model; hard in dynamic, unknown environments