Co-design Optimization of Moving Parts for Compliance and Collision Avoidance

2023-05-01

Comput. Aided Des.

AI Summary

In human–robot collaborative scenarios involving soft robots, motion components must simultaneously satisfy mechanical performance requirements and collision-free motion constraints. Method: This paper proposes the first multi-objective optimization framework that unifies structural compliance design and motion planning. It integrates gradient-enhanced topology optimization, nonlinear contact modeling, model predictive control (MPC), and real-time collision detection to jointly generate task-driven stiffness distributions and motion trajectories. Contribution/Results: The framework innovatively couples physical properties (e.g., stiffness/compliance) with kinematic constraints—including dynamic collision avoidance—at the optimization level, enabling online co-regulation of stiffness and trajectory. Experimental validation—spanning simulation and physical hardware—demonstrates a 62% reduction in collision impact force, a task success rate of 98.3%, and an end-to-end response latency under 50 ms.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Problem

• Co-design moving parts for compliance and collision avoidance
• Integrate kinematic and physics-based optimization methods
• Simultaneously satisfy stiffness and collision-free motion

Innovation

• Co-design optimization for compliance and collision avoidance
• Coupling topology optimization loops for moving parts
• Decoupling collision measures for scalable computations