Improved Shuffled Frog Leading Algorithm-Based Optimal Trajectory Path for Robot Control System

The robots are the emerging technology in the recent years that are capable of interpreting the human actions and non-human actions without automatically based on their predefined destinations. The existing models tried to advance the learning ability of the robot to increase their energy efficiency but did not reach the expected goal due to overfitting problem of the robot mechanism. The recognition of best path for reaching destinations without any obstacles increases efficiency of the robot performance. To overcome the existing problem an Improved Shuffled Frog Leading Algorithm (ISFLA) is proposed in the system to get optimized trajectory path for the robot control system. The colony predation strategy is adapted in the ISFLA algorithm for advancing its space searching ability that enhanced the robot control system performance. The ISFLA algorithm finds the best trajectory path for the robot to reach the destinated goal that minimizes the energy consumption of the robot for the task completion and improves the efficiency of the robot control system. The proposed ISFLA algorithm has attained improved results having R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 0.987, RMSE 3.452, MAE 0.641, and training time of 4.013s compared to the existing Light Gradient Boosting Machine (LightGBM).