International Journal of Science, Engineering and TechnologyAuthors: Radha Krishan Yadav
Abstract: This study introduces a bio-inspired inverse kinematics (IK) framework for upper-body humanoid robots, integrating human biomechanical principles to improve motion naturalness, efficiency, and adaptability. By combining multi-objective optimization with human motion analysis, the framework addresses limitations of traditional IK solvers, such as rigid motion and poor task adaptability. Human upper-limb kinematics were analysed using motion capture and OpenSim, distilling features like energy minimization and joint comfort into dynamic cost functions. A hybrid Weighted Least Norm (WLN)-gradient IK solver achieved real-time performance (<100 ms latency) and outperformed classical methods by ~20% in human-likeness and ~50% in safety margins. Validation on 7-DOF humanoid arms showed 90–95% task success rates in Activities of Daily Living (ADLs). Applications in assistive robotics and industrial cobots highlight the framework’s potential for human-robot interaction (HRI). Future work includes reinforcement learning for adaptive IK and soft robotics integration.Key criteria, including metabolic cost, safety, coordination, and kinematic efficiency, are analyzed to optimize robotic upper body motion for human-like performance. A multi- criteria performance framework is proposed, integrating these factors to assess their impact on task-specific outcomes. The methodology involves computational modeling, simulation, and comparative analysis of robotic motion against human benchmarks. Results reveal that incorporating human-based criteria enhances the adaptability and efficiency of robotic systems, with notable improvements in safety and coordination during complex tasks. These findings contribute to the advancement of human-robot interaction and the design of next-generation robotic systems for applications requiring precise and natural upper body movements.
