Neural network temporal quantized lagrange dynamics with cycloidal trajectory for a toe-foot bipedal robot to climb stairs

The study introduces a stable walking pattern for a biped robot by employing a semi-supervised artificial neural network (ANN) to generate trajectories with a focus on reducing potential damage from small objects that are identified by Yolov5 algorithm. The ANN is utilized as a universal approximator to ensure smooth motion automatically by meeting predefined boundary conditions during its training. This trajectory generation approach is then compared with one another ANN- based method, with nonstop evaluations mainly focusing on position, velocity, and its acceleration profiles to maintain smooth motion. By analysis of trajectory derivatives and its curvature detects and auto corrects any discontinuities. Mathematical model created on from MATLAB 2023 and its simulations validate the trajectory's smoothness and demonstrating its effectiveness in enabling bipedal robots to navigate uneven terrain. The proposed method is very useful and more suitable for online adaptable trajectory generation by addressing collision avoidance and adaptability to various terrains, and overall stability in bipedal robot navigation comprehensively.

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Neural network temporal quantized lagrange dynamics with cycloidal trajectory for a toe-foot bipedal robot to climb stairs

Cited by 3 publications

References 48 publications

Inverse kinematics for the CMOR in the CFETR based on workspace analysis: A novel approach

Inverse kinematics for the CMOR in the CFETR based on workspace analysis: A novel approach

Research on the inverse kinematics of heavy-load manipulator and end-effector for fusion reactor

Stable Biped Robot's Walk using Semi-Supervised ANN based Trajectory Generation within Yolov5 Algorithm based Identified Environment with Ditch

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