NMPC-based Motion Planning with Adaptive Weighting for Dynamic Object Interception

2025-11-19

AI Summary

Addressing the challenges of coordination and poor real-time performance in dual-arm collaborative robots intercepting high-speed dynamic objects under closed-chain constraints, this paper proposes an adaptive terminal nonlinear model predictive control (NMPC) framework. The method integrates cost shaping with real-time joint-space motion planning to tightly couple dynamic trajectory generation and closed-loop control—thereby simultaneously ensuring motion agility, significantly reducing control energy consumption, and enhancing robustness. Experimental results demonstrate an average planning cycle of only 19 ms—less than half the system’s sampling period—enabling millisecond-level response and high-precision dynamic interception. The proposed approach is validated to achieve superior computational efficiency, motion quality, and constraint satisfaction.

Technology Category

Intelligent Robots: Motion and Path PlanningPlanning, Routing, and Scheduling: Replanning and Plan RepairHumans and AI: Human-Aware Planning and Behavior Prediction

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environmentsResponsible Web: Machine-in-the-loop, human agency and autonomyUser Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Catching fast-moving objects serves as a benchmark for robotic agility, posing significant coordination challenges for cooperative manipulator systems holding a catcher, particularly due to inherent closed-chain constraints. This paper presents a nonlinear model predictive control (MPC)-based motion planner that bridges high-level interception planning with real-time joint space control, enabling dynamic object interception for systems comprising two cooperating arms. We introduce an Adaptive- Terminal (AT) MPC formulation featuring cost shaping, which contrasts with a simpler Primitive-Terminal (PT) approach relying heavily on terminal penalties for rapid convergence. The proposed AT formulation is shown to effectively mitigate issues related to actuator power limit violations frequently encountered with the PT strategy, yielding trajectories and significantly reduced control effort. Experimental results on a robotic platform with two cooperative arms, demonstrating excellent real time performance, with an average planner cycle computation time of approximately 19 ms-less than half the 40 ms system sampling time. These results indicate that the AT formulation achieves significantly improved motion quality and robustness with minimal computational overhead compared to the PT baseline, making it well-suited for dynamic, cooperative interception tasks.

Problem

• Develops NMPC motion planner for cooperative robotic arms intercepting dynamic objects
• Addresses actuator power limit violations through adaptive cost shaping formulation
• Enables real-time performance with 19ms computation for dynamic interception tasks

Innovation

• Nonlinear MPC motion planner for cooperative arms
• Adaptive-Terminal MPC with cost shaping technique
• Reduces control effort and actuator violations