Introduction

This chapter focuses on fast, local decision-making for safe robot motion. You will start by organizing reactive if/else logic into Finite State Machines, then add multi-sensor arrays that give a robot the spatial awareness needed to steer around obstacles rather than just stopping in front of them. From there you will study Artificial Potential Fields, a physics-inspired alternative that treats the goal as an attractor and obstacles as repellers, and put both approaches into practice with a hands-on wall-avoidance coding exercise. The chapter closes by examining the central weakness shared by every purely reactive method, local minima traps, and shows how hybrid architectures combine global planning from Chapter 3 with local reactive control to escape them. These methods are especially useful in dynamic environments where conditions change quickly.