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Artificial Potential Field

An artificial potential field is a reactive motion generation method, introduced by Khatib in 1986, that treats the goal as an attractive potential and obstacles as repulsive potentials, moving the robot along the negative gradient of their sum. It is computationally trivial and naturally real-time, but suffers from local minima where attractive and repulsive forces cancel, and from oscillation in narrow passages, so it is typically used as a local layer beneath a global planner.

Why it matters for physical AI

Potential-field ideas persist inside modern systems as reactive collision avoidance layers and safety filters that wrap learned policies, providing an interpretable last line of defense near obstacles.