Adaptive Trajectory Optimization for Task-Specific Human-Robot Collaboration

2024-09-19

AI Summary

To address the challenges of adaptability and stability in motion planning arising from dynamic task changes and uncertain human inputs in human–robot collaboration, this paper proposes an adaptive trajectory optimization and control framework grounded in human interaction dynamics modeling. Methodologically, it introduces the inverse differential Riccati equation into task-specific trajectory optimization for the first time, enabling online reference trajectory generation without predefined templates; concurrently, a neural adaptive PID controller is developed, wherein neural networks dynamically tune PID gains in real time to ensure high-precision joint-space tracking. The key contribution lies in the synergistic integration of dynamics-aware trajectory planning and data-driven gain adaptation, eliminating extensive manual parameter tuning. Simulation results demonstrate rapid responsiveness to task transitions and human disturbances, a 32% reduction in tracking error, significantly enhanced robustness, and low computational overhead—fully satisfying real-time collaborative requirements.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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.

Problem

• Dynamic trajectory optimization for human-robot collaboration
• Adaptive motion planning using human interaction dynamics
• Real-time control adjustment with neural network for system uncertainties

Innovation

• Dynamic trajectory optimization using inverse differential Riccati equation
• Neuro-adaptive PID controller with real-time gain adjustment
• Stable joint-space tracking with minimal parameter tuning