Next generation legged robot locomotion: A review on control techniques

The next generation of autonomous-legged robots will herald a new era in the fields of manufacturing, healthcare, terrain exploration, and surveillance. We can expect significant progress in a number of industries, including inspection, search and rescue, elderly care, workplace safety, and nuclear decommissioning. Advanced legged robots are built with a state-of-the-art architecture that makes use of stereo vision and inertial measurement data to navigate unfamiliar and challenging terrains. However, designing controllers for these robots is a difficult task due to a number of factors, including dynamic terrains, tracking delays, inaccurate 3D maps, unforeseen events, and sensor calibration issues. To address these challenges, this paper discusses the current methods for controlling autonomous-legged robots. Our primary contribution is comparative research on robot control strategies such as virtual model control (VMC), model predictive control (MPC), and model-free reinforcement learning (RL). This paper provides information on different strategies for controlling autonomous legged robots and discusses the potential advancements and applications of this technology in the future. The aim of this study is to assist future researchers in making informed decisions on the selection of optimal control strategies and innovative concepts when developing and working with legged robots.