Cooperative Control of Multi-Quadrotors for Transporting Cable-Suspended Payloads: Obstacle-Aware Planning and Event-Based Nonlinear Model Predictive Control

2025-03-24

AI Summary

To address the challenges of obstacle avoidance, poor trajectory adaptability, and insufficient control real-time performance for multi-quadrotor cooperative cable-suspended payload transportation in dynamic environments, this paper proposes a closed-loop framework integrating A* global path planning with event-triggered nonlinear model predictive control (NMPC). We innovatively design a dual-mode event-driven perception and map update mechanism, synergizing event cameras with RGB-SLAM to achieve millisecond-level dynamic obstacle detection and adaptive NMPC triggering. Furthermore, we formulate an NMPC optimization framework incorporating rope-quadrotor coupled dynamics and safety constraints. Simulation results demonstrate a 27% reduction in energy consumption, a 40% decrease in control update frequency, sub-15-ms response latency, and an obstacle avoidance success rate exceeding 99.2% in complex dynamic scenarios—significantly enhancing system safety, stability, and computational efficiency.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

This paper introduces a novel methodology for the cooperative control of multiple quadrotors transporting cablesuspended payloads, emphasizing obstacle-aware planning and event-based Nonlinear Model Predictive Control (NMPC). Our approach integrates trajectory planning with real-time control through a combination of the A* algorithm for global path planning and NMPC for local control, enhancing trajectory adaptability and obstacle avoidance. We propose an advanced event-triggered control system that updates based on events identified through dynamically generated environmental maps. These maps are constructed using a dual-camera setup, which includes multi-camera systems for static obstacle detection and event cameras for high-resolution, low-latency detection of dynamic obstacles. This design is crucial for addressing fast-moving and transient obstacles that conventional cameras may overlook, particularly in environments with rapid motion and variable lighting conditions. When new obstacles are detected, the A* algorithm recalculates waypoints based on the updated map, ensuring safe and efficient navigation. This real-time obstacle detection and map updating integration allows the system to adaptively respond to environmental changes, markedly improving safety and navigation efficiency. The system employs SLAM and object detection techniques utilizing data from multi-cameras, event cameras, and IMUs for accurate localization and comprehensive environmental mapping. The NMPC framework adeptly manages the complex dynamics of multiple quadrotors and suspended payloads, incorporating safety constraints to maintain dynamic feasibility and stability. Extensive simulations validate the proposed approach, demonstrating significant enhancements in energy efficiency, computational resource management, and responsiveness.

Problem

• Cooperative control of multi-quadrotors for cable-suspended payloads
• Obstacle-aware planning with A* and event-based NMPC
• Real-time dynamic obstacle detection using dual-camera systems

Innovation

• A* algorithm for global path planning
• Event-based NMPC for local control
• Dual-camera setup for obstacle detection