artificial potential field method
The artificial potential field method is a way of steering a robot by pretending the world is a landscape of hills and valleys, and then letting the robot roll downhill. The goal you want to reach is treated like the lowest point of a valley, so it gently pulls the robot toward it, the way a ball rolls to the bottom of a bowl. Every obstacle, meanwhile, is treated like a hilltop that pushes the robot away, the way two magnets with the same pole repel each other. At any spot, the robot simply adds up the pull from the goal and the pushes from nearby obstacles, gets one combined arrow telling it which way is 'downhill', and takes a small step that way.
What makes this method appealing is that it is fast and reactive: the robot does not need to plan a whole route in advance, it just feels the forces around it right now and moves, which lets it dodge things that suddenly appear. The big weakness is that it can get stuck in a 'local minimum' — a little dip in the landscape that is not the goal, like a small hollow partway down a hill where the ball settles and stops. A classic trap is a U-shaped wall: the goal pulls the robot straight in, the walls push back evenly from both sides, and the robot freezes in the pocket, never reaching the goal on the other side. Because of this, the basic method is often combined with smarter planners or extra tricks to escape these traps.
A delivery robot crossing a lobby feels a steady pull toward the exit door and a push away from each pillar and person; the sum of these nudges curves its path smoothly between them without it ever computing a full route.
Goal pulls, obstacles push — the robot rides the combined force.
The pull toward the goal is called the attractive field and the push from obstacles is called the repulsive field; the robot follows the combined force, which is the slope (gradient) of the total field.