Collaborative Aerial Object Lifting.

Find a lifting configuration together.

Distributed optimization of robot positions for cooperative lifting with multiple aerial robots.

Multi-robot systems
Distributed optimization
Cooperative lifting

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Collaborative Aerial Object Lifting — experimental or simulation results — Published simulation figuresResearch project

01 / OVERVIEW

The idea.

A Distributed Optimization Approach for Collaborative Object Lifting Using Multiple Aerial Robots

Jinxin Liu, Chao Sun, Zhi Feng, Renhe Guan, Jindong Chang, and Guoqiang Hu

Unmanned Systems · 2024

Collaborative lifting couples the choices of every robot. This work formulates those choices as a distributed optimization problem, accounting for individual objectives, feasible positions, and the force balance of the team.

Local robot objectives
Coupled lifting constraints
Numerical simulation study

RESEARCH VISUALS

Simulation study.

Original figures from the published paper: system schematic and numerical simulation trajectories. This study reports simulation results, not flight experiments.

02 / IMPLEMENTATION

How it works.

Method at a glance

Model
Describe each robot’s objective.
Constrain
Account for feasible positions and force balance.
Coordinate
Exchange information with neighboring robots.
Optimize
Converge toward a cooperative configuration.

Couple individual decisions

Each robot contributes a local objective to the shared optimization.

Respect lifting constraints

The formulation accounts for constraints imposed by the payload.

Study convergence in simulation

Numerical results evaluate the proposed distributed algorithms.

03 / RESOURCES

Read further.

The paper, original demonstrations, and available project materials.

Original paperA Distributed Optimization Approach for Collaborative Object Lifting Using Multiple Aerial Robots

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