Dynamic Equilibrium in Robotics:Techniques and Applications for Self Balancing Robots

Abstract

Self-balancing robots have become a key focus in robotics and control system studies since they are capable of standing on two wheels as an upright system. In the implementation of these systems, PID control, Linear Quadratic Regulation, fuzzy logic as well as reinforcement learning in machine learning is applied to control the balance and motion of the robots in the robots in different environmental conditions. This article reviews literature on self-balanced robots over the last half century moving beyond mechanical and electrical designs to control strategies including how double loop control incorporated PID control and how this has now shifted to machine learning. It also describes sensor fusion techniques where a Kalman filter or complementary role filters is employed sensor’s accelerometer data and gyroscope data towards the assistance of estimating how tilted the robot is to the ground in degrees. Practical examples are also analyzed from personal devices such as the Segue, to education and robotics research. The paper also investigates the current challenges in the field including sensor drift , battery life, and especially the need for real time adjustment to varying environmental conditions such as bumpy terrain or perturbations. A number of ways are suggested such as using better sensors, control strategies and low power actuators which all improve balance robot systems. The study finishes with the discussion of the optimal developments, particularly focusing on the use of overcome strategies of controls using artificial intelligence for bringing about more flexibility and independency to self-balancing robots.