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

arXiv: https://arxiv.org/abs/2503.01096 · HTML: https://mlsanity.com/paper/2503.01096/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2025.

## Built on by (most cited first, 1 of 1)

- [Safe and Scalable Multi-Drone Payload Transport via CBF-based Reinforcement Learning with Zero-Shot Sim-to-Real Transfer](https://mlsanity.com/paper/2607.20665/) (2026)

## Abstract (arXiv)

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.