How do you know when a path-finding algorithm is really needed? There are contexts, where you just want to improve AI navigation to avoid an object, like a space -ship that won't crash on a planet or a car that already knows where to steer, but needs small corrections to avoid a road bump. As I've seen on similar posts, the obvious solution is to implement some path-finding algorithm, most likely like A*, and let your AI-controlled object to navigate through the path. Now, I have the necessary skills to implement a path-finding algorithm, and I'm not being lazy here, but I'm still a bit skeptical on if this is really needed. I have the impression that path-finding is appropriate to navigate through a maze, or picking a path when there are many alternatives. But in obstacle avoidance, when you do know the path, but need to make slight corrections, is path finding really necessary? Even when the obstacles are too sparse or small ? I mean, in real life, when you're driving and notice a bump on the road, you will just have to pick between steering a bit on the left (and have the bump on your right side) or the other way around. You will not consider stopping, or going backwards. A path finding would be appropriate when you need to pick a route through the city, right ? So, are there any other methods to help AI navigation, except path-finding? And if there are, how do you know when path-fining is the appropriate algorithm ? Thanks for any thoughts