HPA-MPC: Hybrid Perception-Aware Nonlinear Model Predictive Control for Quadrotors with Suspended Loads

TL;DR

This work presents a novel Hybrid Perception-Aware Nonlinear Model Predictive Control (HPA-MPC) control approach for quadrotors with suspended loads that considers the complete hybrid system dynamics and includes a perception-aware cost to ensure the payload remains visible in the robot's camera during navigation.

Abstract

Quadrotors equipped with cable-suspended loads represent a versatile, low-cost, and energy efficient solution for aerial transportation, construction, and manipulation tasks. However, their real-world deployment is hindered by several challenges. The system is difficult to control because it is nonlinear, underactuated, involves hybrid dynamics due to slack-taut cable modes, and evolves on complex configuration spaces. Additionally, it is crucial to estimate the full state and the cable's mode transitions in real-time using on-board sensors and computation. To address these challenges, we present a novel Hybrid Perception-Aware Nonlinear Model Predictive Control (HPA-MPC) control approach for quadrotors with suspended loads. Our method considers the complete hybrid system dynamics and includes a perception-aware cost to ensure the payload remains visible in the robot's camera during navigation. Furthermore, the full state and hybrid dynamics' transitions are estimated using onboard sensors. Experimental results demonstrate that our approach enables stable load tracking control, even during slack-taut transitions, and operates entirely onboard. The experiments also show that the perception-aware term effectively keeps the payload in the robot's camera field of view when a human operator interacts with the load.

Figures (6)

• Figure 1: System frame convention.
• Figure 2: The system architecture describing the flow of information through the various components of our pipeline.
• Figure 3: Payload trajectory tracking plots for following a lissajous trajectory. The last row shows the $x-y$ visualization of payload path with a colorbar indicating the magnitude of the payload linear velocity.
• Figure 4: Non-Hybrid (left) vs. Hybrid (right) controller during a slack-taut transition in a hover scenario. The hybrid controller maintains the robot's position and is unaffected by the payload movement. The non-hybrid controller incorrectly commands the robot to move towards the payload during the slack state due to $z$ error in the desired payload position.
• Figure 5: Non-Hybrid (left) vs Hybrid (right) controller during a slack-taut transition during a straight line trajectory. The non-hybrid controller moves towards the payload during the slack phase. Due to this downward motion, the robot experiences a higher negative $z$ velocity and higher angular rates upon impact when compared to our hybrid controller. The hybrid controller commands the robot to track the nominal quadrotor trajectory during the slack phase and stably continues flight once the cable is taut.