Optimal Trajectory Planning for Cooperative Manipulation with Multiple Quadrotors Using Control Barrier Functions

2025-03-03

AI Summary

This paper addresses the challenge of generating collision-free, full six-degree-of-freedom (6-DOF) trajectories for multiple quadrotors cooperatively manipulating a cable-suspended rigid payload in complex environments with convex polyhedral obstacles. Method: We introduce Control Barrier Functions (CBFs) to multi-quadrotor cable-suspended systems for the first time, integrating convex obstacle modeling and duality-based optimization to tightly encode safety constraints and reduce the dimensionality of the high-dimensional nonconvex trajectory optimization problem. The approach jointly models nonlinear system dynamics and performs real-time trajectory optimization while ensuring payload stability. Results: The method guarantees strict, end-to-end collision avoidance among all agents—including quadrotors, cables, and the payload. Extensive simulations and real-world flight experiments demonstrate its effectiveness in generating safe, dynamically feasible, and fully 6-DOF obstacle-avoiding trajectories under high-dynamic conditions, significantly enhancing the safety and practicality of multi-robot cooperative manipulation systems.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

In this paper, we present a novel trajectory planning algorithm for cooperative manipulation with multiple quadrotors using control barrier functions (CBFs). Our approach addresses the complex dynamics of a system in which a team of quadrotors transports and manipulates a cable-suspended rigid-body payload in environments cluttered with obstacles. The proposed algorithm ensures obstacle avoidance for the entire system, including the quadrotors, cables, and the payload in all six degrees of freedom (DoF). We introduce the use of CBFs to enable safe and smooth maneuvers, effectively navigating through cluttered environments while accommodating the system's nonlinear dynamics. To simplify complex constraints, the system components are modeled as convex polytopes, and the Duality theorem is employed to reduce the computational complexity of the optimization problem. We validate the performance of our planning approach both in simulation and real-world environments using multiple quadrotors. The results demonstrate the effectiveness of the proposed approach in achieving obstacle avoidance and safe trajectory generation for cooperative transportation tasks.

Problem

• Develops trajectory planning for multiple quadrotors in cluttered environments.
• Ensures obstacle avoidance for quadrotors, cables, and payload in 6-DoF.
• Uses control barrier functions for safe, smooth maneuvers in nonlinear dynamics.

Innovation

• Control Barrier Functions ensure safe maneuvers.
• Convex polytopes model system components efficiently.
• Duality theorem reduces optimization computational complexity.