MACHINE LEARNING FOR ROBOT NAVIGATION CLASSIFICATION USING ULTRASOUND SENSOR DATA

Abstract

Robot navigation is a crucial aspect of robotics, enabling autonomous robots to move safely and efficiently through their surroundings. Conventionally, engineers and programmers have relied on fixed rules and heuristics to guide robot movements. However, these rules are often specific to certain environments and struggle to adapt to new or changing conditions. For instance, simple obstacle avoidance techniques or path planning algorithms are commonly used. While effective in controlled settings, they lack the flexibility needed to handle diverse and unpredictable surroundings. In recent years, machine learning (ML) has emerged as a promising alternative. ML allows robots to learn from data and adjust their navigation strategies based on real-time sensory inputs. As a result, this project focuses on implementing ML for robot navigation classification