Adaptive Trajectory Optimization for Task-Specific Human-Robot Collaboration

TL;DR

This paper introduces an adaptive trajectory optimization framework for human-robot collaboration that dynamically adjusts motion planning based on human interaction, ensuring stability and accuracy through neural network-based control.

Contribution

It presents a novel task-specific, adaptive trajectory optimization method combined with a neuro-adaptive PID controller for improved human-robot collaboration.

Findings

Successful numerical simulation validation

Enhanced adaptability to task variations

Stable joint-space tracking achieved

Abstract

This paper proposes a task-specific trajectory optimization framework for human-robot collaboration, enabling adaptive motion planning based on human interaction dynamics. Unlike conventional approaches that rely on predefined desired trajectories, the proposed framework optimizes the collaborative motion dynamically using the inverse differential Riccati equation, ensuring adaptability to task variations and human input. The generated trajectory serves as the reference for a neuro-adaptive PID controller, which leverages a neural network to adjust control gains in real time, addressing system uncertainties while maintaining low computational complexity. The combination of trajectory planning and the adaptive control law ensures stability and accurate joint-space tracking without requiring extensive parameter tuning. Numerical simulations validate the proposed approach.