Balancing Robot

Balancing robots, encompassing diverse platforms like two-wheeled, ball-based, and multi-legged systems, aim to achieve and maintain stable upright postures despite inherent instability. Current research emphasizes robust control strategies, including model-based approaches (PID, Lead-Lag) and increasingly, data-driven methods like reinforcement learning (deep deterministic policy gradient, actor-critic algorithms), often combined for enhanced performance and adaptability to complex environments and unexpected disturbances. This field is significant for advancing robotics in areas such as assistive mobility (e.g., self-balancing wheelchairs), aerial manipulation, and the development of more agile and versatile robots for various applications.