Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Event-Triggered Nonlinear Model Predictive Control for Cooperative Cable-Suspended Payload Transportation with Multi-Quadrotors

Tohid Kargar Tasooji, Sakineh Khodadadi, Guangjun Liu

TL;DR

This work tackles the challenge of cooperative transport of a cable-suspended payload by a team of at least $N \ge 3$ quadrotors. It introduces an event-triggered distributed nonlinear model predictive control (NMPC) framework that focuses on payload pose in $SE(3)$, reduces optimization dimensionality, and dynamically adjusts the prediction horizon to balance performance and computational load. The method integrates an event-triggered mechanism, obstacle avoidance, inter-robot separation, and actuator constraints within a hierarchical control scheme, solved in real time via SQP with HPIPM. Simulations in ROS/Gazebo demonstrate improved tracking and energy efficiency under constrained computation and communication, validating scalability to resource-limited, GPS-denied environments such as warehouses.

Abstract

Autonomous Micro Aerial Vehicles (MAVs), particularly quadrotors, have shown significant potential in assisting humans with tasks such as construction and package delivery. These applications benefit greatly from the use of cables for manipulation mechanisms due to their lightweight, low-cost, and simple design. However, designing effective control and planning strategies for cable-suspended systems presents several challenges, including indirect load actuation, nonlinear configuration space, and highly coupled system dynamics. In this paper, we introduce a novel event-triggered distributed Nonlinear Model Predictive Control (NMPC) method specifically designed for cooperative transportation involving multiple quadrotors manipulating a cable-suspended payload. This approach addresses key challenges such as payload manipulation, inter-robot separation, obstacle avoidance, and trajectory tracking, all while optimizing the use of computational and communication resources. By integrating an event-triggered mechanism, our NMPC method reduces unnecessary computations and communication, enhancing energy efficiency and extending the operational range of MAVs. The proposed method employs a lightweight state vector parametrization that focuses on payload states in all six degrees of freedom, enabling efficient planning of trajectories on the SE(3) manifold. This not only reduces planning complexity but also ensures real-time computational feasibility. Our approach is validated through extensive simulation, demonstrating its efficacy in dynamic and resource-constrained environments.

Event-Triggered Nonlinear Model Predictive Control for Cooperative Cable-Suspended Payload Transportation with Multi-Quadrotors

TL;DR

This work tackles the challenge of cooperative transport of a cable-suspended payload by a team of at least quadrotors. It introduces an event-triggered distributed nonlinear model predictive control (NMPC) framework that focuses on payload pose in , reduces optimization dimensionality, and dynamically adjusts the prediction horizon to balance performance and computational load. The method integrates an event-triggered mechanism, obstacle avoidance, inter-robot separation, and actuator constraints within a hierarchical control scheme, solved in real time via SQP with HPIPM. Simulations in ROS/Gazebo demonstrate improved tracking and energy efficiency under constrained computation and communication, validating scalability to resource-limited, GPS-denied environments such as warehouses.

Abstract

Autonomous Micro Aerial Vehicles (MAVs), particularly quadrotors, have shown significant potential in assisting humans with tasks such as construction and package delivery. These applications benefit greatly from the use of cables for manipulation mechanisms due to their lightweight, low-cost, and simple design. However, designing effective control and planning strategies for cable-suspended systems presents several challenges, including indirect load actuation, nonlinear configuration space, and highly coupled system dynamics. In this paper, we introduce a novel event-triggered distributed Nonlinear Model Predictive Control (NMPC) method specifically designed for cooperative transportation involving multiple quadrotors manipulating a cable-suspended payload. This approach addresses key challenges such as payload manipulation, inter-robot separation, obstacle avoidance, and trajectory tracking, all while optimizing the use of computational and communication resources. By integrating an event-triggered mechanism, our NMPC method reduces unnecessary computations and communication, enhancing energy efficiency and extending the operational range of MAVs. The proposed method employs a lightweight state vector parametrization that focuses on payload states in all six degrees of freedom, enabling efficient planning of trajectories on the SE(3) manifold. This not only reduces planning complexity but also ensures real-time computational feasibility. Our approach is validated through extensive simulation, demonstrating its efficacy in dynamic and resource-constrained environments.

Paper Structure

This paper contains 14 sections, 2 theorems, 52 equations, 7 figures.

Table of Contents

  1. INTRODUCTION
  2. PROBLEM FORMULATION
  3. Problem of Interest
  4. System Dynamics
  5. System Constraint Requirements
  6. Methodology
  7. Event-triggered Mechanism
  8. Payload Nonlinear Model Predictive Control
  9. Payload Event-triggered Nonlinear Model Predictive Control
  10. Quadrotor Control
  11. Simulations
  12. Simulation Setup
  13. Trajectory Tracking
  14. CONCLUSION

Key Result

Lemma 3.1

The nonlinear function $f_h(\xi, u)$ is locally Lipschitz continuous in $\xi$ with Lipschitz constant $L_P = \sqrt{2(a^2 + \rho^2 b^2)}$.

Figures (7)

  • Figure 1: The control block diagram of the proposed approach
  • Figure 2: Cable-suspended load transportation by MAVs
  • Figure 3: Visualization of the Cooperative Transportation and Manipulation of Cable Suspended Payloads with Multiple Quadrotors in ROS and Gazebo
  • Figure 4: Event-triggered nonlinear model predictive control for position trajectory of payload in 3D with different triggering conditions
  • Figure 5: Event-triggered nonlinear model predictive control for position trajectory of payload with different triggering conditions
  • ...and 2 more figures

Theorems & Definitions (5)

  • Remark 1
  • Remark 2
  • Lemma 3.1
  • Theorem 3.2
  • Proof 1