An Optimized-LSTM and RGB-D Sensor-Based Human Gait Trajectory Generator for Bipedal Robot Walking

Bipedal robots are gaining significant attention due to their wide range of applications in various fields such as manufacturing, transportation, rehabilitation, and many others. However, mimicking human-like walking in bipedal robots is a challenging task as human walking represents a complex learning process that requires the coordination of the human brain motor cell with its leg muscles. Besides, maintaining walking stability in bipedal robots is another significant task that has not been fully explored. To address the challenges mentioned above and improve the performance of bipedal robot walk, this work proposes a long short-term memory (LSTM)-based human gait trajectory generator. A low-cost and markerless motion capture sensor system (Microsoft Kinect V2) is used to capture human gait data during a treadmill walk. The captured data is then analyzed and utilized to extract the significant features using the proposed model. Furthermore, the selection of significant hyperparameter(s) has also been focused on achieving the optimal performance for the proposed model using the Rao-3 metaheuristic optimization algorithm (optimized-LSTM). The stability performance of the generated gait trajectories is verified through standard evaluation measures like cyclograms and phase plots. The gait trajectories obtained through the proposed model are also validated on the HOAP-2 robot simulator.