Navigation & SLAM

Potential Field

A potential field is a motion generation method that treats the robot as a particle in an artificial field where the goal exerts attraction and obstacles exert repulsion, with the robot descending the combined gradient. Introduced for real-time obstacle avoidance by Khatib in 1986, it is fast and naturally reactive but suffers from local minima, motivating variants such as navigation functions and harmonic potentials.

Why it matters for physical AI

Potential fields remain a building block for reactive collision avoidance layers that wrap learned or planned motions, and their local-minima failure mode is a standing lesson in why purely reactive schemes need global guidance.